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WASTE-TO-RESOURCE SYSTEM DESIGN FOR LOW-CARBON CIRCULAR ECONOMY
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WASTE-TO-RESOURCE SYSTEM DESIGN FOR LOW-CARBON CIRCULAR ECONOMY SIMING YOU James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright Ó 2022 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-822681-0 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Susan Dennis Editorial Project Manager: Hilary Carr Production Project Manager: R.Vijay Bharath Cover Designer: Christian J. Bilbow Typeset by TNQ Technologies
Contents Chapter 1 The waste challenge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1. 2. 3. 4. 5. 6. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Agricultural waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Municipal solid waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Waste-to-resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Rural waste management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Chapter 3 Waste-to-energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1. 2. 3. 4. 5. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Incineration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Pyrolysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Anaerobic digestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Chapter 4 Waste-to-biohydrogen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2. Biohydrogen production technologies. . . . . . . . . . . . . . . . . . . . . . 48 3. Downstream processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
vi Contents Chapter 5 Waste-to-biomethane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 2. Biogas production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3. Biogas cleanup and upgrading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Chapter 6 Waste-to-bioethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 1. 2. 3. 4. 5. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saccharification and fermentation . . . . . . . . . . . . . . . . . . . . . . . . Pretreatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yeasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Further development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 103 108 111 114 115 Chapter 7 Waste-to-biodiesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 1. 2. 3. 4. 5. 6. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biodiesel properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biodiesel classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biodiesel impacts on soil and water . . . . . . . . . . . . . . . . . . . . . . Biodiesel production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Whole process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 120 122 124 125 131 133 Chapter 8 Waste-to-biochar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Waste-to-biochar technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Biochar system design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 141 156 157 Chapter 9 System design: costebenefit analysis. . . . . . . . . . . . . . . . . . . . . . . 161 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 2. Mathematical principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Contents vii 3. Economic feasibility of waste-to-resource development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 4. Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Chapter 10 System design: life cycle assessment. . . . . . . . . . . . . . . . . . . . . . 189 1. 2. 3. 4. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LCA procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LCA of waste-to-resource developments . . . . . . . . . . . . . . . . Uncertainty analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 191 200 207 208 Chapter 11 System optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 2. Multiobjective optimization methods . . . . . . . . . . . . . . . . . . . . 214 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 Chapter 12 Perspectives of future development. . . . . . . . . . . . . . . . . . . . . . . . 225 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
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The waste challenge 1 Abstract This chapter gives an overview of the overall waste management challenge and highlights the importance of sustainable waste management. It explains the existing waste management hierarchy strategy and the roles of waste-to-resource development in managing the waste that cannot be handled by the “reduce, reuse, and recycle” (3R) methods. It also introduces the potential factors that need to be considered upon the design of waste-to-resource development with a special focus on public engagement, economics, and environmental impacts. Finally, it presents a summary of the scope and content arrangement of the book. Keywords: Climate change; Sustainable development goals; Sustainable waste management; Waste management hierarchy; Waste-to-resource technologies; Whole system and supply chain design. 1. Introduction Sustainable waste management (SWM) is a worldwide challenge and is calling for effective actions under the socioeconomic and environmental pressures of enormous waste production. The rates of municipal solid waste (MSW) generation in developed and developing countries were reported to be 521.95e759.2 kg per person per year (kpc) and 109.5e525.6 kpc, respectively (Karak et al., 2012). About 2.01 billion tonnes of MSW are generated annually, and it is estimated that at least 33% of the generation are not managed in an environmentally safe manner (Kaza et al., 2018). In view of the continuous economic growth and population expansion, the waste generation will keep increasing and it is expected that 2.2 billion tonnes of MSW will be generated per annum by 2025 worldwide (Hoornweg & Bhada-Tata, 2012). The increasing pile-up of waste pose a realistic threat to the environment, ecosystems, and human welfare if proper waste management practices and facilities are not in place. The climate change crisis is closely associated with waste generation management in various aspects, i.e., methane emissions of organic waste landfill, emission abatement via waste reuse, recycling, and reduction, renewable and low carbon resource Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00005-0 Copyright © 2022 Elsevier Inc. All rights reserved. 1
2 Chapter 1 The waste challenge recovery from waste, emissions rated to the transportation of waste, etc. (Ackerman, 2000). The carbon saving potential has become one of the most significant factors that has been considered upon the design of SWM approaches. On the other hand, climate change can also influence the practicing and consequences of SWM with changes in global temperature, annual precipitation, and sea levels rendering conventional waste management practices less effective. For example, the rise in temperature may increase the fire risk from combustible waste (e.g., composting) at open sites, more frequent extreme weather conditions may increase the health and safety risks of waste operators who implement waste management, and the rise in the sea level poses a risk of seawater intrusion to coastal landfills and washing away floating waste, leading to marine waste (e.g., plastics) pollution (Bebb & Kersey, 2003). SWM is essential to achieving the United Nations’ sustainable development goals (SDGs) and is closely related to such SDGs as Decent Work and Economic Growth (SDG8), Sustainable Cities and Communities (SDG11), and Sustainable Consumption and Production (SDG12) (Robert et al., 2005). This is reflected by its significant socioeconomic and environmental consequences. Waste mismanagement can cause serious environmental issues such as heavy metal pollution in ecosystems (e.g., water, plants, and soil) and marine plastic pollution via field dumping, and pollutant (e.g., CO, CO2, SO, NO, particulate matters, etc.) emissions via open field burning (Ferronato & Torretta, 2019). As local and global populations continue to expand, so as will the requirements and strain on waste infrastructure, meaning the costs of waste mismanagement will increase. It was predicted that the costs for SWM globally would increase from US$205.4 billion per year to around US$375.5 billion in 2025 (Hoornweg & BhadaTata, 2012). A hierarchical strategy has been proposed and implemented for promoting SWM (See Fig. 1.1). On the top of the hierarchy, the “reduce, reuse, and recycle” (3R) methods are regarded as a long-term strategy to reduce waste pollution toward the transition from a traditional linear economy to a circular one (Geng et al., 2019). Specifically, the 3R strategy serves to protect the environment, promote sustainable development, and improve resource utilization efficiency, and aims to achieve a closed resource loop within the circular economy model by lessening the pressure on the stock of resources (Ioannidis et al., 2021). However, considering the varied composition and value of waste as well as the
Chapter 1 The waste challenge Figure 1.1 Illustration of the hierarchical strategy for SWM. economic profitability requirement of waste management, the 3R strategy alone is insufficient to curb the rapid waste accumulation and its increasing threat to the environment, ecosystems, and societies, especially given limited waste management infrastructure and lack of plans actually in place. Complementary measures are necessary to handle the waste that is not covered by the 3R strategy and achieve resource (energy and chemicals) recovery from waste and end-of-life disposal. These measures are less favored as compared to 3R in the waste management hierarchy but are essential components of the whole SWM chain (Lombardi et al., 2015). Conventional practices for handling waste that is not reduceable, reusable, or recyclable rely on landfill and incineration which are still playing a major role in some parts of the world. Globally, around 66.6% of MSW was disposed of in open dumpsites or landfills (Fischedick et al., 2014). According to the UK government statistics, landfills are the second most used waste treatment in the United Kingdom, with 24.4% of waste being disposed of by landfills in 2016 (DEFRA, 2021). Landfill is losing its appeal due to adverse environmental impacts. For example, in Europe and the United States, landfills account for 20% of anthropogenic CH4 emissions, and are the second and third largest CH4 emission sources, respectively (Mønster et al., 2019). This number is 8%, also nonnegligible, from a global perspective (Blanco et al., 2014). The landfill leachate containing pollutants like heavy metals, organic, xenobiotics, and inorganic poses a contamination risk to the soil and groundwater in nonsanitary landfills and uncontrolled dumpsites (Negi et al., 2020). Air 3
4 Chapter 1 The waste challenge surrounding landfill sites can affect local communities as the smell is unpleasant and the soil in the area may be saturated with chemicals or hazardous substances. The European Commission proposed to phase out landfilling by 2025 for recyclable waste (e.g., plastics, paper, metals, glass, and biowaste) in nonhazardous waste landfills and reduce the landfilled municipal waste to 10% or less of the total amount of waste generated by 2035 (EC, 2018). Waste-to-energy technologies play a critical role in diverting waste from direct landfill. According to the International Energy Agency, waste-to-energy systems are one of the promising solutions toward a low carbon future via the decarbonization of energy production which is the dominant contributor to greenhouse gas emissions (IEA, 2013). Waste incineration is being widely employed in both developed and developing countries. There are about 1179 MSW incineration plants around the world with a total capacity over 700,000 tonnes per day and most of the plants are in the European Union, the United States, and East Asia (Lu et al., 2017). Incinerators using energy recovery techniques have been used in SWM development to help recover electricity and/or heat from waste while simultaneously reducing the mass and volume of waste sent to landfills. Some typical advantages of the incineration technologies include the effective reduction of waste volume (by 90%) and mass (by 75%), elimination of pathogens, flexibility in feedstock selection, and energy production (Lino & Ismail, 2018). Their disadvantages include high capital and operational costs, significant pollutant emissions, and requiring mandatory treatment of flue gas (Gabbar et al., 2018). Additionally, there exists widespread negative public perception about its emissions of pollutants such as dioxin carcinogen, which needs to be abated to enhance the public acceptance of the technology (Makarichi et al., 2018). Alternative waste-to-energy technologies have been developed to achieve lower pollutant emissions or to improve the energy recovery from some specific types of waste. For example, gasification is a thermochemical process where carbonaceous waste materials are converted into synthesis gas or syngas (a mixture of H2, CO, and CH4 mainly) under an oxygen-deficient condition. The syngas can be further combusted to generate heat or electricity or upgraded to produce value-added chemicals (e.g., pure hydrogen). Anaerobic digestion is a biochemical process where organic waste is decomposed to produce CH4, CO2, and digestate under the effect of anaerobic microorganisms. As compared to gasification, anaerobic digestion is less energy intensive but suffers from the weakness of low productivity.
Chapter 1 The waste challenge Recent development has been focused on converting waste into value-added chemicals for applications in the industrial or transportation sectors, such as biohydrogen, biomethane, bioethanol, biodiesel, biochar, etc. (bio- is used to indicate the chemicals are produced from waste biomass). A significant amount of these chemicals have been produced out of conventional fossil fuele based chemical processes. Displacing the chemicals with the ones derived from waste biomass will lead to carbon abatement and facilitate the development of the circular economy concept. In general, the efficiencies of the waste-to-resource (resource denotes energy and chemicals) technologies depend on the types of waste feedstock, process conditions, and selection of technological routes. The variety of technologies that recover valuable resources from waste are expected to play an increasingly important role in alleviating the challenges of SWM and climate change. The design of waste-to-resource systems needs to consider a variety of factors beyond the technology, and also importantly its relationship with the 3R strategy. Specifically, the waste-toresource approach needs to work in tandem with the 3R strategy, which needs to be further supported by educational initiatives to enhance public awareness for tackling the challenges. Meanwhile, reduced, reused, recycled, and recovered resources that precisely match the socioeconomic, energy, and environmental demands of end-users will accelerate the uptake of such initiatives and lead to higher public engagement. Successful addition of the waste-to-resource technologies as a tier in the 3R hierarchy is dependent on understanding of local context. This will underpin the development of a comprehensive and systematic hierarchical waste management roadmap that clearly defines the relative roles and effects of the measures and includes the steps or milestones needed to achieve waste pollution reduction. The success of such a hierarchical strategy is contingent upon the participation and cooperation of all the stakeholders (i.e., policymakers, investors, and consumers) along the SWM chain as well as effective policy support. This means that the design of waste-toresource systems needs to be gauged in relation to socioeconomic and environmental impacts that are some of the most significant indices for evaluating the feasibility of the systems. The implementation of a waste-to-resource system is subject to its social acceptability and benefits, which is directly reflected by its ability to create jobs and affect income, and indirectly by its effects on equality and welfare development of local communities. The environmental impacts are linked to the system’s ability to tackle the crises of fossil fuel depletion and global climate change, as well as its 5
6 Chapter 1 The waste challenge complication with the development of associated ecosystems. The economic feasibility of waste-to-resource development critically determines its sustainability and depends on (also affects) the formulation of governmental subsidies. Although the different stakeholders have different preferences on the impacts, it is important to consider all the three impacts during the decisionmaking process for optimal planning. The design of the supply chain and logistics of waste management also critically determines the feasibility and impacts of waste-to-resource systems due to the geographical distribution of waste and consumer zones, weather variability, and the potential seasonality of waste feedstocks (Chaplin-Kramer et al., 2017; Field et al., 2018). It has been shown that the waste collection and transportation process accounts for the significant economic factor for waste-to-energy development (Ascher et al., 2020). Moreover, the varied compositions and physicochemical properties of waste imply the complexity of system design. On the one hand, for the same type of waste, there are different technologies available for processing and subsequent product upgrading, depending on the types of targeted end-products (e.g., electricity, heat, liquid transport fuel, biochar, etc.). On the other hand, for the same type of end-product, multiple technologies and waste feedstocks are available upon the design of the system. Hence, there are vast possibilities of waste-to-resource system configurations in terms of the choices of waste feedstock types, processing technologies, and end-product types. This adds a complication of spatial and temporal dimensions to the assessment of the potential of bioresources (defined as the resources recovered from waste biomass in this book), and transportation network and modes, distance, and intermodal-transportation becomes important parameters upon the supply chain and logistics design. To understand the potential contribution of waste-to-resource to our environment, society, and ecosystems, it is essential to develop a systematic database about the economic and environmental impacts of waste-to-resource development under a feasible range of waste-to-resource system and supply chain configurations. Moreover, optimal configurations need to be identified and combined with decision support tools, to allow the policymakers to make informed decisions about waste-to-resource action plans. Considering the various possibilities of technology and process alternatives, superstructure optimization based on, e.g., mixed-integer programming techniques serves as an appropriate approach for optimal technology and process selection by allowing systematic generation and automatic evaluation of
Chapter 1 The waste challenge design candidates based on process economics and environmental sustainability (Gong & You, 2015). A multiobjective optimization framework can be formed by integrating cost-benefit analysis (CBA) and life cycle assessment (LCA) into the superstructure optimization. This book will introduce the fundamentals, development, and applications of various types of waste-to-resource technologies that are expected to play a major role in developing SWM practices in the future. This book will focus on two major analysis and design methods of waste-to-resource development, i.e. CBA and environmental LCA and assemble some basic data sets for carrying out baseline analysis. Examples of LCA and CBA studies and results will be summarized to illustrate the impacts of different configurations of waste-to-resource developments. We will also introduce the multiobjective optimization method in terms of its application in the designing and planning of SWM systems in the end. This book will serve as a starting point for you to conduct waste-to-resource design with the availability of theories and baseline data sets. References Ackerman, F. (2000). Waste management and climate change. Local Environment, 5(2), 223e229. Ascher, S., Li, W., & You, S. (2020). Life cycle assessment and net present worth analysis of a community-based food waste treatment system. Bioresource Technology, 305, 123076. Bebb, J., & Kersey, J. (2003). Potential impacts of climate change on waste management. UK: Environment Agency Bristol. Blanco, G., Gerlagh, R., Suh, S., Barrett, J., de Coninck, H. C., Morejon, C. F. D., Mathur, R., Nakicenovic, N., Ahenkorah, A. O., & Pan, J. (2014). Drivers, trends and mitigation. Chaplin-Kramer, R., Sim, S., Hamel, P., Bryant, B., Noe, R., Mueller, C., Rigarlsford, G., Kulak, M., Kowal, V., & Sharp, R. (2017). Life cycle assessment needs predictive spatial modelling for biodiversity and ecosystem services. Nature Communications, 8(1), 1e8. DEFRA. (2021). UK statistics on waste. https://assets.publishing.service.gov.uk/ government/uploads/system/uploads/attachment_data/file/874265/UK_ Statistics_on_Waste_statistical_notice_March_2020_accessible_FINAL_rev_v0. 5.pdf. EC. (2018). Circular Economy: New rules will make EU the global front-runner in waste management and recycling. https://ec.europa.eu/commission/ presscorner/detail/en/IP_18_3846. Ferronato, N., & Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. International Journal of Environmental Research and Public Health, 16(6), 1060. Field, J. L., Evans, S. G., Marx, E., Easter, M., Adler, P. R., Dinh, T., Willson, B., & Paustian, K. (2018). High-resolution technoeecological modelling of a 7
8 Chapter 1 The waste challenge bioenergy landscape to identify climate mitigation opportunities in cellulosic ethanol production. Nature Energy, 3(3), 211e219. Fischedick, M., Roy, J., Acquaye, A., Allwood, J., Ceron, J.-P., Geng, Y., Kheshgi, H., Lanza, A., Perczyk, D., & Price, L. (2014). Industry in: Climate change 2014: Mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Technical Report. Gabbar, H. A., Aboughaly, M., & Ayoub, N. (2018). Comparative study of MSW heat treatment processes and electricity generation. Journal of the Energy Institute, 91(4), 481e488. Geng, Y., Sarkis, J., & Bleischwitz, R. (2019). How to globalize the circular economy. Nature Publishing Group. Gong, J., & You, F. (2015). Sustainable design and synthesis of energy systems. Current Opinion in Chemical Engineering, 10, 77e86. Hoornweg, D., & Bhada-Tata, P. (2012). What a waste: A global review of solid waste management. IEA. (2013). Waste to energy summary and conclusions from the IEA bioenergy ExCo71 workshop. https://www.ieabioenergy.com/wp-content/uploads/ 2014/03/ExCo71-Waste-to-Energy-Summary-and-Conclusions-28.03.14.pdf. Ioannidis, A., Chalvatzis, K. J., Leonidou, L. C., & Feng, Z. (2021). Applying the reduce, reuse, and recycle principle in the hospitality sector: Its antecedents and performance implications. Business Strategy and the Environment. Karak, T., Bhagat, R. M., & Bhattacharyya, P. (2012). Municipal solid waste generation, composition, and management: The world scenario. Critical Reviews in Environmental Science and Technology, 42(15), 1509e1630. Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Publications. Lino, F. A. M., & Ismail, K. A. R. (2018). Evaluation of the treatment of municipal solid waste as renewable energy resource in Campinas, Brazil. Sustainable Energy Technologies and Assessments, 29, 19e25. Lombardi, L., Carnevale, E., & Corti, A. (2015). A review of technologies and performances of thermal treatment systems for energy recovery from waste. Waste Management, 37, 26e44. Lu, J.-W., Zhang, S., Hai, J., & Lei, M. (2017). Status and perspectives of municipal solid waste incineration in China: A comparison with developed regions. Waste Management, 69, 170e186. Makarichi, L., Jutidamrongphan, W., & Techato, K. (2018). The evolution of waste-to-energy incineration: A review. Renewable and Sustainable Energy Reviews, 91, 812e821. Mønster, J., Kjeldsen, P., & Scheutz, C. (2019). Methodologies for measuring fugitive methane emissions from landfillseA review. Waste Management, 87, 835e859. Negi, P., Mor, S., & Ravindra, K. (2020). Impact of landfill leachate on the groundwater quality in three cities of North India and health risk assessment. Environment, Development and Sustainability, 22(2), 1455e1474. Robert, K. W., Parris, T. M., & Leiserowitz, A. A. (2005). What is sustainable development? Goals, indicators, values, and practice. Environment: Science and Policy for Sustainable Development, 47(3), 8e21.
Waste 2 Abstract This chapter introduces the classification of waste (i.e., industrial waste, nuclear waste, agricultural waste and municipal solid waste) and their generation characteristics and statistics. It focuses on agricultural waste and municipal solid waste as they are more relevant to waste-toresource development. It explains the definitions of the compositions (i.e., ultimate and proximate) and heating values of waste and the properties of typical agricultural and municipal solid waste are summarized. This chapter concludes with highlighting the importance of waste-to-resource development and emphasizing that rural waste management needs to be paid special attention. Keywords: Agricultural waste; Composition; Heating value; Municipal solid waste; Rural waste management; Waste-to-resource. 1. Introduction According to the European Commission Waste Framework Directive, waste is defined as any substance or object which the holder discards or intends or is required to discard (EC, 2008). There exist different waste classifications based on various criteria such as sources, state, biodegradability, etc. This chapter considers the source-based classification that categorizes waste into four types, i.e., industrial, agricultural, municipal, and nuclear. Typical industrial waste includes MSW incineration ash, iron and steelmaking slags, cement dust, petroleum spent catalyst, etc. MSW incineration ash (fly ash and bottom ash) is the solid residue of the combustion processing of MSW that serves to reduce the mass and volume of MSW while recovering energy. Fly ash (w3e5 wt.% of raw MSW) refers to the pulverized fine particles captured by filtration devices post an incineration reactor, while bottom ash (w20e25 wt.% of solid residue) normally consists of slag recovered from the base of incineration furnace. In China, around 15 million tonnes of bottom ash are produced in MSW incineration plants each year (Hu et al., 2021). 17.6 million tonnes of bottom ash are produced each year in the European Union, Norway, and Switzerland Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00004-9 Copyright © 2022 Elsevier Inc. All rights reserved. 9
10 Chapter 2 Waste (Blasenbauer et al., 2020). Some of the industrial waste contains considerable heavy metals and pose a risk of environmental pollution if not disposed of properly. For example, waste batteries contain Ni, Cd, and Ag, while electronic waste contains Sn, Au, Ag, Ni, and Zn, their improper disposal will lead to the pollution of soil and surface and/or groundwater (Pant et al., 2012). Nuclear or radioactive waste is referred to as any material that is either radioactive or contaminated by radioactivity above the thresholds defined in associated legislation (IAEA, 2009). Typical sources of nuclear waste include nuclear power stations, hospitals, science laboratories, etc., in a variety of physical and chemical forms (e.g., aqueous waste, solid waste, liquid organic waste, cwet solid waste, and biological and medical waste) (Sljivi Ivanovic & Smiciklas, 2020). The rules and methods of industrial and nuclear waste management are typically different from that of agricultural and MSW managements with the necessity of ensuring high socioenvironmental security. Agricultural and municipal solid waste, on the other hand, refer to the ones from which bioenergy or value-added chemicals could be derived, will be the focus of this book, due to their great potential of facilitating renewable energy and resource generation. The property and generation of waste vary considerably across different types of waste and are important factors affecting the design and implementation of waste management practices. Different types of waste could differ significantly in their compositions, making some technologies a preferred option upon a preliminary design. For example, biochemical technologies such as anaerobic digestion (i.e., the biological decomposition of organic material into mainly “biogas” whose main constituents are methane (50e70 wt.%) and carbon dioxide (30e50 wt.%)) is a desirable option for treating organic waste such as food waste featured by a high moisture content (Ascher et al., 2020). The locality and availability of waste generation is also linked to demand side management, making certain technologies a preferred option because their production matches well with the demand of local customers. For example, to utilize the palm oil mills’ solid (empty fruit bunch) and liquid (palm oil mill effluent) waste, gasification and anaerobic digestion were applied, respectively, to generate electricity to sustain the operation of the mills (Aziz et al., 2017). Actually, the types and availability of waste often comes as the first set of parameters or conditions for selecting waste management technologies and designing associated implementation and operation plans. 2. Agricultural waste Agricultural waste is the unwanted waste discarded in the process of agricultural activities and some typical examples of
Chapter 2 Waste agricultural waste are agricultural product processing waste (e.g., crop stalks), plant waste, livestock and poultry manure, rural household waste, silage plastics, fertilizer, pesticides, herbicides, wastes from farms, poultry houses and slaughterhouses, etc. (Ramírez-García et al., 2019). It is an important constituent of biomass resource and is featured by wide availability, large quantity, and biodegradability. The generation of agricultural waste experienced significant increase because of the expansion of agricultural production that has been tripled during the past 50 years (Duque-Acevedo et al., 2020). The annual lignocellulosic biomass generated by the primary agricultural sector was estimated to be about 200 billion tonnes worldwide (Ren et al., 2009). Being two of the largest developing countries and agrarian economies, China and India have abundant agricultural waste for renewable generation. China had a total agricultural waste of 1.75 109 tonnes in 2013, consisting of 9.93 108 tonnes (56.82%) of crop straw, 4.52 108 tonnes (25.85%) of livestock and poultry manure, and 3.03 108 tonnes (17.33%) of forest residues (Dai et al., 2018). For India, the biogas potential from agricultural waste via anaerobic digestion was predicted to be 65 billion m3/year in 2015 (Mittal et al., 2019). The agriculture sector is a significant contributor for GHG emissions, consisting of emissions from agricultural soils, livestock, stationary combustion sources, and off-road machinery. For example, this sector accounts for 10% of total EU-28 emissions (440 MtCO2-eq.), of which 38% is about CH4 emissions from enteric fermentation from cattle and 31% is about direct N2O emissions from agricultural soils and fertilize use (Juvyns et al., 2019). The sector accounted for 10% of UK GHG emissions in 2018, with 56% and 31% being CH4 and N2O emissions, respectively (DECC, 2015). Due to decreases in animal numbers and use of synthetic fertilizers, GHG emissions from UK agriculture decreased by 16% between 1990 and 2018. Agriculture is responsible for 9% of total United States GHG emissions, with 81%, 11%, and 6% being CO2, CH4, and N2O, respectively (EPA, 2016). The significant carbon footprint of the agriculture sector calls for more sustainable development and effective utilization of agricultural waste for decarburization. There is a long history that agricultural waste is used as an important source of energy and chemicals. The benefits of agricultural waste utilization are not only contingent upon the types of waste but also the means of utilization. Inappropriate utilization of agricultural waste such as burning in stoves has been a major cause of personal exposure to PM2.5. In China, 40% of crop straw was burned in-field and contributes to 1.036 million 11
12 Chapter 2 Waste tonnes of PM2.5 emissions every year (Clare et al., 2015; Zhang et al., 2016). Approximately 75% of agricultural biomass is discarded, directly burnt in the field, or used by farmers for household cooking, which causes the problems of low-efficiency utilization (10%) and wasting valuable biomass resources, and air pollution (e.g., N2O, SO2, CH4, and PM2.5) (Huang et al., 2019). Alternatively, being the most readily available organic waste, agricultural waste can be converted to value-added products (e.g., biohydrogen and biomethane) in such processes as anaerobic digestion, fermentation, and gasification, which serve as environmentally friendly ways of agricultural waste utilization. 3. Municipal solid waste MSW is defined as the waste generated from households and any other waste with similar compositions and properties to household waste according to Municipal Solid Waste Rules 2000 (Thomas & Soren, 2020). Sustainable MSW management becomes increasingly important due to continuous rising in its generation worldwide as the result of population expansion, rapid urbanization, and accelerated economic growth. The World Bank predicted that the global MSW generation would reach 2.2 billion tonnes per year by 2025 and 3.4 billion tonnes per year by 2050 (Kaza et al., 2018; The World Bank, 2017). In China, the yield of MSW increased at an annual rate of 8%e10%, and the total volume of MSW generated had increased from 31.3 million tonnes in 1980 to 203.6 million tonnes in 2016, which was expected to reach 480 million tonnes by 2030 (Hu et al., 2015). The MSW generation in Switzerland increased by 215% between 1990 and 2017 (Magazzino et al., 2020). The total urban MSW generation in India would be 165, 230, and 436 million tonnes by 2030, 2041, and 2050, respectively (Sharma & Jain, 2019). Effective MSW management is critical to the achievement of Sustainable Cities and Communities as part of the United Nations SDGs. The MSW generation is closely connected with the standard of living as indicated by various socioeconomic and development indices. Gross Domestic Product (GDP) and the Human Development Index (HDI) were found to be two of the most influential factors affecting the generation rates of 13 solid waste streams of 10 European countries, with waste electronic and electric equipment being most significantly influenced (Namlis & Komilis, 2019). A study on the seasonality of MSW compositions for four Eastern European cities (i.e., Georgia (Kutaisi), Lithuania (Kaunas), Russia (St. Petersburg), and Ukraine (Boryspil)) showed that economic development and climate conditions affected the MSW generation statistics significantly, with the median MSW generation rate ranging from 18.7 to 38.3 kg/capita/month
Chapter 2 Waste (Denafas et al., 2014). For developing countries like China, economic and urban (i.e., urban population) development are the major factors influencing MSW generation (Liu & Wu, 2010). 4. Properties The composition and physicochemical properties of waste are the most important factors that affect the design and implementation of waste-to-resource methods. Properties that are commonly considered upon the selection and identification of waste include ultimate composition, proximate composition, and heating value. They are often used as the input parameters in the process modeling and estimation of energy and mass flows of relevant waste-to-resource technologies and systems, which serves as the basis for the evaluation of their techno-economic feasibility and environmental impacts. The ultimate composition is about the contents of carbon (C), oxygen (O), hydrogen (H), nitrogen (N), and sulfur (S) as well as moisture (MC) and ash (ASH) in waste, based on which a chemical formula of waste could be obtained. The proximate composition accounts for such gross components as fixed carbon (FC), volatile matter (VM), ash (ASH), and moisture (MC). The compositions of waste are measured based on different mass basis. For example, the composition on the dry basis refers to the percentage contents of the different components of the waste that has been dried (i.e., without considering the moisture content), while the one on the dry and ash free basis refers to the percentage contents of the waste without considering the contents of moisture and ash. The as-received basis is based on the consideration of raw waste. The heating value of waste is an indicator of the energy content of waste that will be transformed into heat upon its full combustion. It is a critical parameter defining the energy potential of waste-to-energy generation. Due to the presence of moisture content, two different heating values, i.e., higher heating value (HHV) and lower heating value (LHV), have been defined. The former refers to the energy content covering the latent heat of vaporization of water, while the latter does not take the latent heat into consideration. The heating values of waste are closely associated with the compositions of waste. Empirical relationships have been developed to predict HHV based on the composition of waste. For example, Parikh et al. (2005) derived the correlation between HHV and proximate composition as (Parikh et al., 2005) HHV ¼ 0.3536FC þ 0.1559VM 0.0078ASH (MJ/kg) (2.1) 13
14 Chapter 2 Waste where FC ¼ 1.0%e91.5%, VM ¼ 0.92%e90.6%, and ASH ¼ 0.12% e77.7% in wt.% on a dry basis. Separately, the correlation between HHV and ultimate composition was derived as (Channiwala & Parikh, 2002) HHV ¼ 0.3491C þ 1.1783H þ 0.1005S 0.1034O 0.0151N 0.0211ASH (2.2) where 0% C 92.25%, 0.43% H 25.15%, 0% O 50.00%, 0% N 5.60%, 0% S 94.08%, 0% ASH 71.4%, 4.745 MJ/ kg HHV55.345 MJ/kg. C, H, O, N, S, and ASH represents the carbon, hydrogen, oxygen, nitrogen, sulfur, and ash contents, respectively, expressed in mass percentages on a dry basis. Table 2.1 lists the physicochemical properties of some common types of agricultural waste. It is shown that the compositions vary significantly across the different types of agricultural waste, Table 2.1 Compositions and HHV of selected agricultural waste. Ultimate composition (dafa) Proximate composition (arb) H O N ASH VM FC MC HHV Agricultural C (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (MJ/kg) References waste Rice straw 44.2 6.2 48.4 0.8 8.20 75.60 13.76 2.44 14.99 Rice husk 47.4 6.7 45.1 0.8 10.50 65.50 14.60 9.40 15.70 Cotton stalk Corn stover Wheat straw 47.09 51.89 49.0 6.17 5.45 7.01 37.55 41.48 43.2 9.14 0.84 0.70 5.14 7.3 5.70 75.16 64.5 77.60 19.70 18.8 7.20 3.42 9.4 9.50 17.81 17.1 15.10 Peanut shell 50.64 Cattle manure 49.38 6.86 6.46 41.32 39.79 1.18 3.33 1.47 10.9 70.03 15.2 23.18 3.2 5.32 70.7 20.61 3.9 a daf: dry and ash-free basis. b ar: as-received basis. Worasuwannarak et al. (2007), Younas et al. (2017) Wang et al. (2014), Worasuwannarak et al. (2007) Liu et al. (2019) Wang et al. (2011) Greco et al. (2018), Szamosi et al. (2017), Yu et al. (2016) Fermanelli et al. (2020) Wang et al. (2011)
Chapter 2 Waste 15 suggesting the importance of the feedstock selection for improved energy or chemical recovery. The sulfur content of agricultural waste is low and thus not shown in Table 2.1, suggesting the reduced emission of sulfur-related pollutants as compared to fossil fuel upon thermochemical reactions. Typical MSW components include food waste, garden waste, plastic waste, paper, cardboard, wood waste, sludge, etc., whose compositions and physicochemical properties vary considerably as shown in Table 2.2. The wide variations in the components of MSW makes segregation an important step prior to their effective treatment. However, waste segregation could be a step incurring significant energy and costs, rendering the whole waste management process economically infeasible. It is worth noting that even within a single MSW category, e.g., food waste, the physicochemical properties could change significantly depending on the specific contents of the waste, e.g., carbohydrate, protein, fats, and bones. Based on such information as the proximate Table 2.2 Properties of typical MSW. Ultimate composition (dba) MSW Proximate composition (arb) C H O N ASH VM FC MC HHV (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (wt.%) (MJ/kg) References Carbohydrate 41.8 6.2 food waste Protein food 48.2 7.1 waste Textile waste 53.79d 6.73 46.9 2.0 2.7 70.7 15.8 10.8 17.01 You et al. (2016) 29.0 8.9 6.3 67.6 13.9 12.2 21.99 You et al. (2016) 27.65 1.51 4.13 56.54 7.07 32.26 20.71c Cardboard and 38.36 paper 5.66 38.79 0.43 7.04 51.35 11.10 30.51 17.67c Sludge 4.8 27.8 5.2 50.8 15.1 7.6 Gutierrez-Gomez et al. (2021) Baawain et al. (2017), Gutierrez-Gomez et al. (2021) You et al. (2016) 35.0 a daf: dry and ash-free basis. b ar: as-received basis. c Dry basis. d Dry and ash-free basis. 26.5 14.7
16 Chapter 2 Waste and ultimate compositions, heating values, etc., MSW could be also classified into vegetables, starch food, orange peel, wood waste, printing paper, cellulose, PVC, PET, PE/PP, PS, and rubber, to promote comparable and consistent research toward waste-toenergy development (Zhou et al., 2015). 5. Waste-to-resource As mentioned in Chapter 1, SWM is a complex, interdisciplinary, and systemic project which needs to be supported by the coherent and consistent efforts of all stakeholders. A hierarchical method is necessary for waste management considering the difference in the socioeconomic and environmental values of different types of waste and the variations in associated technology availability. In addition to the prioritized 3R strategy, waste-to-resource development plays an important role for sustainably managing the waste that could not be handled by the 3R strategy. Conventionally, landfill and incineration have been adopted as the main approaches for waste disposal. Typical disadvantages of landfill include the formation of gas and liquid phase contaminants leading to air and groundwater pollution, taking up of additional land resource, and risks of infection and fire hazards. In particular, landfill-based waste management emits massive amount of methane (CH4) whose global warming potential is 25 times that of carbon dioxide (CO2) over a 100-year period. Incineration could recover energy from MSW in the form of electricity and/or heat while effectively reducing the mass and volume of MSW toward ultimate disposal. However, some of the major challenges associated with incineration include the environmentally friendly disposal of incineration ash, air pollutant (e.g., NOx and dioxin) reduction, and high off-gas flow rates requiring significant cleaning effort. Advanced technologies are available to convert waste into energy and chemicals, with the potential to achieve higher efficiency and flexibility and to better cater to the demands of end-users. Based on the main products generated, some of the relatively mature (technology readiness level >5) technologies can be classified as waste-to-energy, waste-to-biohydrogen, waste-to-biomethane, waste-to-biodiesel, waste-to-bioethanol, and waste-to-biochar, respectively, which will be the focus of the subsequent chapters of this book. These technologies are receiving increasing attention from both academics and industry for their great potential to replace the conventional methods for alleviating the current dilemma of global waste management.
Chapter 2 Waste 6. Rural waste management Rural areas account for large sources of waste generation; however, waste management in rural areas has received much less attention as compared to their urban counterparts that have developed relatively mature waste management rules and chains. A recent study from Romania showed that rural communities could contribute 85.51% of plastics into water bodies due to frequent flash floods (Mihai, 2021). Hence, the waste generation and associated environmental pollution in rural areas is predisposed to be made in an uncontrolled manner, with wide ecological, health and socioeconomic implications. Unfortunately, knowledge of effective waste management in rural areas of, especially, LMICs is limited, which renders existing measures ineffective and discourages sustainable effort. Waste management practices are further complicated by socioeconomic, environmental, and geographical factors. For example, sparsely populated remote rural areas are usually the most neglected by waste management services and might have been in a position facing various other challenges, such as poor electrification, household air pollution, and farmland contamination. Meanwhile, the demands (in terms of energy and chemicals) may vary considerably across different communities, calling for a flexibility in technology and system designing. These factors suggest that waste management in rural areas (1) will be country-specific, if not village-specific, (2) will require systematic data sets to develop a variety of solutions for flexible deployment, and (3) will be less possible to rely on existing large-scale, centralized waste treatment facilities, making small-scale, decentralized development that offers a wide range of product possibilities a potentially better option. References Ascher, S., Li, W., & You, S. (2020). Life cycle assessment and net present worth analysis of a community-based food waste treatment system. Bioresource Technology, 305, 123076. Aziz, M., Kurniawan, T., Oda, T., & Kashiwagi, T. (2017). Advanced power generation using biomass wastes from palm oil mills. Applied Thermal Engineering, 114, 1378e1386. Baawain, M., Al-Mamun, A., Omidvarborna, H., & Al-Amri, W. (2017). Ultimate composition analysis of municipal solid waste in Muscat. Journal of Cleaner Production, 148, 355e362. https://doi.org/10.1016/j.jclepro.2017.02.013 Blasenbauer, D., Huber, F., Lederer, J., Quina, M. J., Blanc-Biscarat, D., Bogush, A., Bontempi, E., Blondeau, J., Chimenos, J. M., & Dahlbo, H. (2020). Legal situation and current practice of waste incineration bottom ash utilisation in Europe. Waste Management, 102, 868e883. 17
18 Chapter 2 Waste Channiwala, S. A., & Parikh, P. P. (2002). A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel, 81(8), 1051e1063. https:// doi.org/10.1016/S0016-2361(01)00131-4 Clare, A., Shackley, S., Joseph, S., Hammond, J., Pan, G., & Bloom, A. (2015). Competing uses for China’s straw: The economic and carbon abatement potential of biochar. Gcb Bioenergy, 7(6), 1272e1282. Dai, Y., Sun, Q., Wang, W., Lu, L., Liu, M., Li, J., Yang, S., Sun, Y., Zhang, K., & Xu, J. (2018). Utilizations of agricultural waste as adsorbent for the removal of contaminants: A review. Chemosphere, 211, 235e253. DECC. (2015). UK greenhouse gas emissions, final figures. https://assets. publishing.service.gov.uk/government/uploads/system/uploads/attachment_ data/file/407432/20150203_2013_Final_Emissions_statistics.pdf. Denafas, G., Ruzgas, T., Martuzevicius, D., Shmarin, S., Hoffmann, M., Mykhaylenko, V., Ogorodnik, S., Romanov, M., Neguliaeva, E., Chusov, A., Turkadze, T., Bochoidze, I., & Ludwig, C. (2014). Seasonal variation of municipal solid waste generation and composition in four East European cities. Resources, Conservation and Recycling, 89, 22e30. https://doi.org/ 10.1016/j.resconrec.2014.06.001 s-García, F. J., & CamachoDuque-Acevedo, M., Belmonte-Urena, L. J., Corte Ferre, F. (2020). Agricultural waste: Review of the evolution, approaches and perspectives on alternative uses. Global Ecology and Conservation, 22, e00902. EC. (2008). Directive 2008/98/EC of the European Parliament and of the Council. https://www.legislation.gov.uk/eudr/2008/98/article/3. EPA. (2016). Inventory of US greenhouse gas emissions and sinks: 1990e2014. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissionsand-sinks-1990-2014. Fermanelli, C. S., Córdoba, A., Pierella, L. B., & Saux, C. (2020). Pyrolysis and copyrolysis of three lignocellulosic biomass residues from the agro-food industry: A comparative study. Waste Management, 102, 362e370. https:// doi.org/10.1016/j.wasman.2019.10.057 lez, B., & Manyà, J. J. (2018). Evolution Greco, G., Videgain, M., Di Stasi, C., Gonza of the mass-loss rate during atmospheric and pressurized slow pyrolysis of wheat straw in a bench-scale reactor. Journal of Analytical and Applied Pyrolysis, 136, 18e26. https://doi.org/10.1016/j.jaap.2018.11.007 Gutierrez-Gomez, A. C., Gallego, A. G., Palacios-Bereche, R., Tofano de Campos Leite, J., & Pereira Neto, A. M. (2021). Energy recovery potential from Brazilian municipal solid waste via combustion process based on its thermochemical characterization. Journal of Cleaner Production, 293, 126145. https://doi.org/10.1016/j.jclepro.2021.126145 Huang, Y., Zhao, Y., Hao, Y., Wei, G., Feng, J., Li, W., Yi, Q., Mohamed, U., Pourkashanian, M., & Nimmo, W. (2019). A feasibility analysis of distributed power plants from agricultural residues resources gasification in rural China. Biomass and Bioenergy, 121, 1e12. Hu, M., Guo, D., Ma, C., Hu, Z., Zhang, B., Xiao, B., Luo, S., & Wang, J. (2015). Hydrogen-rich gas production by the gasification of wet MSW (municipal solid waste) coupled with carbon dioxide capture. Energy, 90, 857e863. Hu, Y., Zhao, L., Zhu, Y., Zhang, B., Hu, G., Xu, B., He, C., & Di Maio, F. (2021). The fate of heavy metals and salts during the wet treatment of municipal solid waste incineration bottom ash. Waste Management, 121, 33e41. IAEA. (2009). IAEA safety standards: For protecting people and the environment. https://www-pub.iaea.org/MTCD/Publications/PDF/Pub1419_web.pdf.
Chapter 2 Waste Juvyns, O., Fernandez, R., Mandl, N., & Rigler, E. (2019). Annual European union greenhouse gas inventory 1990e2017 and inventory report. https://www.eea. europa.eu/publications/european-union-greenhouse-gas-inventory-2019. Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Publications. Liu, C., & Wu, X. (2010). Factors influencing municipal solid waste generation in China: A multiple statistical analysis study. Waste Management and Research, 29(4), 371e378. https://doi.org/10.1177/0734242X10380114 Liu, J., Zhong, F., Niu, W., Su, J., Gao, Z., & Zhang, K. (2019). Effects of heating rate and gas atmosphere on the pyrolysis and combustion characteristics of different crop residues and the kinetics analysis. Energy, 175, 320e332. https://doi.org/10.1016/j.energy.2019.03.044 Magazzino, C., Mele, M., & Schneider, N. (2020). The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland. Waste Management, 113, 508e520. Mihai, F.-C. (2021). Rural plastic emissions into the largest mountain lake of the Eastern Carpathians. Royal Society Open Science, 5(5), 172396. https:// doi.org/10.1098/rsos.172396 Mittal, S., Ahlgren, E. O., & Shukla, P. R. (2019). Future biogas resource potential in India: A bottom-up analysis. Renewable Energy, 141, 379e389. Namlis, K.-G., & Komilis, D. (2019). Influence of four socioeconomic indices and the impact of economic crisis on solid waste generation in Europe. Waste Management, 89, 190e200. Pant, D., Joshi, D., Upreti, M. K., & Kotnala, R. K. (2012). Chemical and biological extraction of metals present in E waste: A hybrid technology. Waste Management, 32(5), 979e990. Parikh, J., Channiwala, S. A., & Ghosal, G. K. (2005). A correlation for calculating HHV from proximate analysis of solid fuels. Fuel, 84(5), 487e494. https:// doi.org/10.1016/j.fuel.2004.10.010 Ramírez-García, R., Gohil, N., & Singh, V. (2019). Recent advances, challenges, and opportunities in bioremediation of hazardous materials. In Phytomanagement of polluted sites (pp. 517e568). Elsevier. Ren, N., Wang, A., Cao, G., Xu, J., & Gao, L. (2009). Bioconversion of lignocellulosic biomass to hydrogen: Potential and challenges. Biotechnology Advances, 27(6), 1051e1060. Sharma, K. D., & Jain, S. (2019). Overview of municipal solid waste generation, composition, and management in India. Journal of Environmental Engineering, 145(3), 4018143. c-Ivanovic, M., & Smiciklas, I. (2020). Utilization of C&D waste in radioactive Sljivi waste treatmentdcurrent knowledge and perspectives. In Advances in construction and demolition waste recycling (pp. 475e500). Elsevier. nfalvi, Z. Szamosi, Z., Tóth, P., Koós, T., Baranyai, V. Z., Szepesi, G. L., & Sime (2017). Explosion characteristics of torrefied wheat straw, rape straw, and vine shoots fuels. Energy and Fuels, 31(11), 12192e12199. https://doi.org/ 10.1021/acs.energyfuels.7b01875 The World Bank. (2017). Solid waste management. http://www.worldbank.org/ en/topic/urbandevelopment/brief/solid-waste-management. Thomas, P., & Soren, N. (2020). An overview of municipal solid waste-to-energy application in Indian scenario. Environment, Development and Sustainability, 22(2), 575e592. Wang, L., Shahbazi, A., & Hanna, M. A. (2011). Characterization of corn stover, distiller grains and cattle manure for thermochemical conversion. Biomass and Bioenergy, 35(1), 171e178. https://doi.org/10.1016/j.biombioe.2010.08.018 19
20 Chapter 2 Waste Wang, G., Silva, R. B., Azevedo, J. L. T., Martins-Dias, S., & Costa, M. (2014). Evaluation of the combustion behaviour and ash characteristics of biomass waste derived fuels, pine and coal in a drop tube furnace. Fuel, 117, 809e824. https://doi.org/10.1016/j.fuel.2013.09.080 Worasuwannarak, N., Sonobe, T., & Tanthapanichakoon, W. (2007). Pyrolysis behaviors of rice straw, rice husk, and corncob by TG-MS technique. Journal of Analytical and Applied Pyrolysis, 78(2), 265e271. https://doi.org/10.1016/ j.jaap.2006.08.002 Younas, R., Hao, S., Zhang, L., & Zhang, S. (2017). Hydrothermal liquefaction of rice straw with NiO nanocatalyst for bio-oil production. Renewable Energy, 113, 532e545. https://doi.org/10.1016/j.renene.2017.06.032 You, S., Wang, W., Dai, Y., Tong, Y. W., & Wang, C.-H. (2016). Comparison of the co-gasification of sewage sludge and food wastes and cost-benefit analysis of gasification- and incineration-based waste treatment schemes. Bioresource Technology, 218. https://doi.org/10.1016/j.biortech.2016.07.017 Yu, Y., Yang, Y., Cheng, Z., Blanco, P. H., Liu, R., Bridgwater, A. V., & Cai, J. (2016). Pyrolysis of rice husk and corn stalk in auger reactor. 1. Characterization of char and gas at various temperatures. Energy and Fuels, 30(12), 10568e10574. https://doi.org/10.1021/acs.energyfuels.6b02276 Zhang, L., Liu, Y., & Hao, L. (2016). Contributions of open crop straw burning emissions to PM2. 5 concentrations in China. Environmental Research Letters, 11(1), 14014. Zhou, H., Long, Y., Meng, A., Li, Q., & Zhang, Y. (2015). Classification of municipal solid waste components for thermal conversion in waste-toenergy research. Fuel, 145, 151e157. https://doi.org/10.1016/ j.fuel.2014.12.015
Waste-to-energy 3 Abstract This chapter explains the technical principles, process design, and influential factors of four main waste-to-energy technologies, i.e., incineration, pyrolysis, gasification, and anaerobic digestion. Associated technical and process parameters (e.g., process efficiencies) are summarized to facilitate effective and accurate process design, analysis, modeling, and optimization. The advantages and disadvantages of the different types of reactor designs are also reviewed based on incineration and gasification. Keywords: Anaerobic digestionergy; Energy; Gasification; Incineration; Process efficiency; Reactor design; Waste-to-energy. 1. Introduction The climate change crisis because of massive consumption of fossil fuels, the depletion of fossil fuel resource, and the increasing energy demands are the main factors driving the global effort in searching for affordable and low carbon renewable energy resources. Energy security defined by the International Energy Agency (IEA) refers to the uninterrupted availability of energy sources at an affordable price and is becoming a challenge facing both developed and developing countries. Renewable energy source development is not solely caused by a shift toward more sustainable energy policies but is also the consequence of relevant energy security strategies that consider diversifying energy sources using renewable energy (Lucas et al., 2016). However, the economic disadvantage of existing renewable energy sector to the fossil fuel sector is expected to persist due to the fragmented structure of the sector, which calls for a unified effort to improve public understanding of the capabilities of renewable technologies and seek political support for a transition away from fossil fuel technologies (Valentine, 2011). According to IEA, waste-to-energy (WtE, or energy-from-waste (EfW)) technologies are one of the promising solutions toward a renewable and low-carbon generation path to a 2 C warming Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00003-7 Copyright © 2022 Elsevier Inc. All rights reserved. 21
22 Chapter 3 Waste-to-energy limit (IEA, 2020). If waste is treated as a valuable resource, improper management may lead to a waste of the resource with significant environmental negativities considering the rapid increasing in waste generation. For example, about a third of our food was disposed of as waste, which accounted for 6.8% of the total GHG emissions globally (FAO, 2014). Over 50 million tonnes of sewage sludge are produced in the EU annually, and sludge disposal accounts for 40% of the total GHG emissions from wastewater treatment plants (Gherghel et al., 2019). WtE serves one of the well-established approaches that can help to recover useful energy while mitigating the challenge of SWM by reducing the mass and volume of waste. Generally, the WtE technologies can be categorized into three basic categories: direct combustion (i.e., incineration), thermochemical processing (e.g., pyrolysis and gasification), and biochemical processing (e.g., anaerobic digestion (AD) and fermentation). This chapter will introduce the major technical principles and fundamentals of the technologies with the summary of associated technical and process parameters to facilitate effective and accurate process design, analysis, modeling, and optimization. 2. Incineration Incineration-based WtE generation is based on the recovery of combustion heat in the form of thermal and/or electrical energy. It has been deployed widely at large scales and plays an important role in diverting MSW from landfills. The objectives of incineration design include volumetric reduction of waste, optimal heat and material recovery, and controlling of flue gas emissions according to relevant environmental standards (Makarichi et al., 2018). The hot combustion gas in an incineration system can be used to produce steam in a boiler, commonly in a superheatere boilereeconomizer arrangement, with the waste heat of flue gas being used to preheat water in an economizer. The produced steam can have various thermal applications or can be used to generate electricity in a combined heat and power (CHP) generation setting. To model the incineration process, four successive stages need to be considered: moisture evaporation, devolatilization and char formation, volatile combustion, and char gasification. Sophisticated 3D CFD simulations have been developed to model incineration by considering the physicochemical reactions and changes corresponding to the four stages. For preliminary design or research, a simplified method can be employed to make quick evaluation and decisions. If the molecular formula of waste can
Chapter 3 Waste-to-energy be expressed as CaHbOc þ dH2O based on ultimate analysis and air is the oxygen source, the overall incineration process can be expressed as Ca Hb Oc þ dH2 Oþ XðO2 þ 3:76N2 Þ/yCO2 þ zH2 Oþ jN2 (3.1) where the coefficients x, y, z, and j can be decided considering the mass conservation for each element (an example calculation is provided in Table 3.1). Eq. (3.1) represents the stoichiometric combustion reaction of waste CaHbOc þ dH2O and can be used to estimate, e.g., air-to-fuel ratio (AF), mass and volumetric fractions of gas products, and heating values of waste. AF is defined as the mass of air required for the stoichiometric combustion of a unit mass of waste. It is an important parameter for defining equivalence ratio (ER): the ratio of the fed air for a thermochemical process to the required air for the stoichiometric combustion of the fuel. In other words, ER is used to describe the air supply to a thermochemical process (e.g., gasification) as compared to the one for the stoichiometric combustion. An empirical correlation has been developed to link AF and HHV of fuels (Zhu & Venderbosch, 2005): AF ¼ 0:31HHV (3.2) The unit of HHV is MJ/kg. Hence, upon the estimation of AF, the HHV of fuel could be estimated as HHVðkJ=kgÞ ¼ 3225:8AF (3.3) Table 3.1 The derivation of the coefficients for the stoichiometric combustion reaction (C3.62H5.8O2.21 D 0.47 H2O) D x(O2 D 3.76N2) / yCO2 D zH2O D jN2. Elements Left-hand side Left-hand side [ Right-hand side 3.62 ¼ y 6.74 ¼ 2z z ¼ 3.37 O 2.21 þ 0.47 þ 2x ¼ 2.68 þ 2x 2.68 þ 2x 2y þ z ¼ 10.61 x ¼ 3.965 N 3.76x 2 3.76x 2 ¼ j 2 j ¼ 3.76 3.965 j ¼ 14.9084 So, the reaction is (C3.62H5.8O2.21 þ 0.47 H2O) þ 3.965 (O2 þ 3.76N2) / 3.62CO2 þ 3.37H2O þ 14.9084N2 C H 3.62 5.8 þ 0.47 2 ¼ 6.74 23
24 Chapter 3 Waste-to-energy Based on the reaction, the AF can be estimated by (using the reaction determined in Table 3.1 as an example) AF ¼ mair 3:965ðO2 þ 3:76N2 Þ ¼ mbiomass mbiomass 3:965ð16 2 þ 3:76 14 2Þ ¼ 5:443 ¼ 100 Hence, HHV ¼ 3225:8 AF ¼ 3225:8 5:443 kJ ¼ 17558 ¼ 17:558 MJ=kg. kg The reactors of incineration can be classified into three major groups, i.e., moving grate, rotary kiln, and fluidized bed. In a moving grate incinerator (Fig. 3.1), the waste on the grate is firstly heated by over-bed radiation and the primary air and combustibles of waste are released into the furnace freeboard and combust with the excess air to form a high temperature zone (Xia et al., 2020). The whole incineration process is divided into the in-bed moving grate combustion and the over-bed gas turbulent combustion. The mass-feed approach of the technology enables it to accommodate large variations in waste compositions and calorific values for great operational stability. The technology also incurs Figure 3.1 A schematic diagram of a moving grate incinerator (Leckner & Lind, 2020).
Chapter 3 Waste-to-energy minimal preprocessing in the form of shredding, waste size reduction, and the removal of bulky materials such as white goods and hazardous or explosive materials that may damage the incineration equipment (Magnanelli et al., 2020). As a result, the moving grate incineration technology is suitable for handling massive amount of wet, bulk, mixed waste at scales up to 50 tonnes of waste per hour. 88%, 94%, and 76% of the incineration plants in Europe, Germany, and the United Kingdom adopt the moving grate technology (Lu et al., 2017). Their capital and operating and maintenance costs, however, are normally higher than rotary kiln and fluidized-bed incinerators. In a fluidized-bed incinerator (Fig. 3.2), a sand-like material suspension is formed by an upward flowing airstream, which promotes heat transfer and mixing and leads to a relatively uniform temperature distribution in the reactor. A fluidized-bed incineration plant typically consists of a fluidized bed reactor, a set of heat recovery units, and a series of flue gas depollution units. The efficiency of the conventional design of fluidized-bed incinerators is sensitive to the size of feedstock particles, and homogeneous, small particles are preferred for its operation. Size reduction Figure 3.2 A schematic diagram of a (A) BFB incinerator and (B) CFB incinerator (Leckner & Lind, 2020). 25
26 Chapter 3 Waste-to-energy pretreatment can be done using, e.g., high-speed low torque and low-speed high torque hammer-mill shredders prior to fluidizedbed incineration. Fluidized-bed incinerators are suitable for treating high moisture waste such as sewage sludge with the arrangement of preheating fluidization air for feedstock drying pretreatment (Zhang et al., 2013). For high calorific waste, the temperature is controlled by either an internal heat exchanger or the amount of cold air fed to the reactor, while for very wet or low calorific waste, preheated air is commonly used as fluidizing gas. To improve the process efficiency, co-incineration is often adopted where high moisture, low calorific waste is co-combusted with high calorific fuel such as coal. For co-incineration of waste of different properties (e.g., HHV), it is important to ensure proper mixing prior to the process. The capital and operation and maintenance costs of fluidized bed incinerators are about 30% less than that of moving grate incinerators (Fitzgerald, 2013). However, their high requirements in feedstock homogeneity and high sensitivity to changes in the calorific value of feedstocks are technical challenges adversely affecting their competitiveness (Hernandez-Atonal et al., 2007). The enhanced heat transfer and mixing as well as the turbulence created in a fluidized bed reactor generally result in a higher rate and efficiency of combustion and lower air pollutant (e.g., NOx and CO) emissions as compared to that of moving grate incinerators. However, a high chlorine content in waste can lead to the problems of boiler corrosion and bed agglomeration and increased emissions of such pollutants as HCl and PCDD/Fs (Van Caneghem et al., 2012). Depending on the way in which the flowing airstream is arranged, fluidized-bed incinerators have three subcategories, i.e., bubbling fluidized bed (BFB), circulating fluidized bed (CFB), and rotating fluidized bed (RFB). For BFB (Fig. 3.2A), the sand-like particles which are originally placed at the bottom of the reactor are kept in suspension by a fluidizing airstream whose velocity ranged between 0.5 and 3.0 m/s (Van Caneghem et al., 2012). To ensure efficient combustion, the size distribution of waste particles should not be too wide such that fine particles are not fluidized out of the reactor prior to complete burnout and large particles do not experience sufficient fluidization and combustion. The particles collected in the cyclone of a BFB reactor will be sent back to the reactor to ensure complete combustion. For CFB incinerators (Fig. 3.2B), a higher air stream velocity (3.0e9.0 m/s) is used to develop an external circulation for which feedstock particles are continuously carried out of the
Chapter 3 Waste-to-energy reactor followed by reentrainment from the bottom of the reactor after cyclone separation (Leckner & Lind, 2020; Van Caneghem et al., 2012). For particles below the cut-size of the cyclone, they are carried forward to the subsequent units such as boiler and dedusting components. Secondary and tertiary air can be introduced in the riser, above the reentry point of the solids from the external circulation loop. In an RFB reactor (Fig. 3.3), an internally circulated flow was formed by an uneven distribution of the primary air over the distributor, and there are zones of strong and weak aeration corresponding to upflow and downflow sand stream, respectively (Van Caneghem et al., 2012). This imposed bed circulation further enhances the mixing between feedstock particles and sand bed material, making RFB incinerators suitable for a wide range of waste types (e.g., sludge, MSW, and industrial waste) and sizes (<30 mm). Rotary kiln incinerators (Fig. 3.4) normally consist of a rotary kiln where primary waste combustion reactions happen and a secondary combustion chamber. The generated flue gas is used to produce superheated steam for energy production (Lombardi et al., 2013). Rotary kiln incinerators have been commonly applied for destructing hazardous waste streams such as medical waste, sludge, contaminated soil, and concentrated wastewater instead of generating steam or energy (Vermeulen et al., 2012). Electricity consumption of incineration plants was reported to be 103.6 kWh/tonne of MSW, based on a mass-weighted average for 90 French MSW incineration plants (Beylot et al., 2018). Figure 3.3 A schematic diagram of an RFB incineration system (Van Caneghem et al., 2012). 27
28 Chapter 3 Waste-to-energy Figure 3.4 A schematic diagram of a rotary kiln incineration system (Kajiwara et al., 2019). The auxiliary power for an incineration power plant in China was considered to be 20.0% of the total electricity generated (Tang et al., 2020). The additional consumption of natural gas in incineration plants equipped with low and high temperature selective catalytic reduction was shown to be 12.1 MJ/tonnes and 137 MJ/ tonnes of MSW, respectively (Beylot et al., 2018). Table 3.2 lists the efficiencies of some of the incineration systems reported. 3. Pyrolysis Pyrolysis is a thermochemical process where carbonaceous materials are converted into product gas (syngas), liquid (biooil), and solid (char or biochar) products, under an condition without oxygen supply. The temperature range of the process is typically between 200 and 650 C. Depending on the heating rate and residence time, pyrolysis is subcategorized into, e.g., carbonization, slow pyrolysis, fast pyrolysis, and flash pyrolysis. Carbonization is a very slow pyrolysis process featured by a very small heating rate less than 0.01e2.0 C/s, a residence time of a couple of days,
Chapter 3 Waste-to-energy Table 3.2 Efficiency data for incineration-based WtE. Efficiency Energy Electricity (%) Heat (%) Country References Electricity 14.96 22.5 28.6 Heat e CHP 5.73 16 e e e 41.25 41.21 28.5 France China Brazil France France Germany Beylot et al. (2018) Tang et al. (2020) Martin et al. (2021) Beylot et al. (2018) Beylot et al. (2018) Mayer et al. (2020) and an ultimate temperature less than 400 C (Basu, 2018). The long residence time of the process promotes secondary char formation reactions and biochar is its primary product (>35 wt.%). Slow pyrolysis can take up to several hours to complete and has biochar as its primary product as well. A typical production of slow pyrolysis is w30 wt.% biooil, w35 wt.% char, and w35 wt.% product gas. There have been numerous applications of slow pyrolysis systems toward biochar production. BiGchar, an Australian company, employs vertical multirotary hearth furnaces for biochar production, and the auxiliary heat of the system is designed to come from partial combustion of volatiles (Garcia-Nunez et al., 2017). Biomacon, a German-based company, manufactures pyrolysis plants with cofiring systems, with feedstock moisture tolerance of up to 50 wt.% and exhaust gas recirculation for low emissions. Pyreg’s 500 kW system has two pyrolysis reactors for heating and carbonization. The design of the system makes plant installation and mobility easier. Chinese manufacturers, such as Beston (Henan) Machinery Co Ltd, have developed similar technologies to handle various types of waste such as agricultural residues, waste tires, and MSW (Williams, 2021). The process of fast pyrolysis normally takes place in a matter of seconds under the temperature condition of 425e500 C. It has biooil as the primary product and a typical generation includes w60e75 wt.% of biooil, w15e25 wt.% of biochar, and w10e20 wt.% of product gas (Basu, 2018). For a typical fast pyrolysis design, the biooil and biochar are used as fuels while the product gas is combusted in, e.g., a boiler to supply heat to the pyrolysis process to increase the overall process efficiency. 29
30 Chapter 3 Waste-to-energy New pyrolysis technologies are also being explored with microwave-assisted pyrolysis being one of the most significant and promising ones for practical applications. Microwaveassisted pyrolysis makes use of microwave heating to achieve a significant reduction in reaction time, increased quality of products, and reduced preparatory requirement of feedstock such as shredding (Motasemi & Afzal, 2013). Microwave irradiation causes molecular motion by migration of ionic species or rotation of dipolar species to generate heat via the friction among molecules. The characteristics of microwave heating are significantly different from that of conventional thermal heating: microwave heating is noncontact, of higher heating rate, material-selective, volumetric, quicker in start-up and stopping, etc. The phenomena and production of microwave-based waste treatment will potentially be different from that of thermal heating-based processes. Pyrolysis-based biochar production is receiving increasing attention due to its great potential for promoting soil quality and agricultural productivities while sequestrating carbon upon soil application. The International Biochar Initiative believes that biochar production and its extensive use can play an important part in improving the global food security status and mitigating climate change (Lehmann & Joseph, 2015). Being a carbon sequestration tool, it meets the recognition of the International Panel on Climate Change that considers carbon capture and storage (CCS) as one of the key options for climate mitigation. More details about biochar production will be given in Chapter 8. Meanwhile, the production of biooil from the pyrolysis of waste biomass offers many benefits such as less harmful effects to the environment due to lower contents of sulfur and nitrogen in the feedstock compared to fossil fuels. Biooil could be used as a fuel for direct energy production, and high quality biooil can be utilized in standard internal combustion engines with minor modifications. The production of pyrolysis is affected by various factors such as temperature, heating rate, feedstock composition, feedstock particle size, reactor design, etc. For lignocellulosic waste, the pyrolysis production is also closely associated with the relative contents of hemicellulose, cellulose, and lignin of the waste. For example, the cellulose composition is a major source for biooil production, while lignin is mainly responsible for biochar production. Hence, high lignin content agricultural residues such as cotton stalks, sugar cane trash, and rice shells serve as good feedstocks for pyrolysis-based biochar production. Pyrolysis reactors can be classified through a wide range of factors such as the required end needs of users, type of biomass being utilized, the
Chapter 3 Waste-to-energy 31 Table 3.3 Efficiency data for pyrolysis-based WtE. Efficiency Energy Electricity (%) Heat (%) Country References Electricity 27.8 2 e 27.2 42.5 (total) e e 15.4 32.5 Germany Japana China United Kingdom United Kingdom Dong et al. (2018) Portugal-Pereira & Lee (2016) Li & Feng (2018) Yang et al. (2018) Yang et al. (2017) Heat CHP a Net liquid fuel produced ¼ 199 L dry tonne/debris. design and operation mode of reactors, reactor’s portability (stationary vs. mobile), etc. Based on the design and operation mode, the classification of pyrolysis reactors is similar to that of gasification reactors which will be introduced in the next section. The energy efficiencies of pyrolysis-based WtE are listed in Table 3.3. 4. Gasification Gasification is a thermochemical process where carbonaceous materials are converted into two main products (i.e., syngas and char or biochar) under an oxygen-deficient condition. The waste gasification process with air being the agent can be expressed as Ca Hb Oc þ dH2 O þ air/CO; CO2 ; H2 O; H2 ; CH4 and N2 þ tars þ particulates (3.4) Gasification typically occurs at a temperature >500 C. The high heating value of MSW makes it a good feedstock for gasification, but there are concerns that MSW-derived biochar may not be suitable for soil application due to potential contaminants in waste feedstocks (Shackley et al., 2011). Key parameters affecting the gasification process include feedstock types, moisture contents, gasifier types, gasifier bed temperature, and gasifying agent types (Kalita & Baruah, 2018). Compared to pyrolysis, gasification can generally achieve a higher energy recovery with a reasonable biochar production (Shackley et al., 2012; Shackley et al., 2012). For example, a biochar yield of about 35% was achieved by treating rice husk in 150e350 kWe downdraft gasifiers at temperatures of 900e1100 C. The cold gas efficiency (ratio of HHV of the
32 Chapter 3 Waste-to-energy produced gas to that of raw waste) for gasification was in a range of 50%e80% with a hot gas efficiency (ratio of HHV and sensible heat of the produced gas to that of raw waste) of 90% (Dong et al., 2018). The actual efficiencies of the development are contingent upon the types of feedstocks used. The gasification process typically involves four stages, i.e., drying, pyrolysis, combustion, and reduction. During the drying stage, the moisture content in waste is vaporized by the heat normally coming from the exothermic reactions in the combustion and reduction stage. A high moisture content in waste will absorb the heat for water vaporization, reduce the reaction temperature, and decrease the energy efficiency of the overall process. The lowered temperature due to the high moisture contents might result in higher tar formation in the reactor and destabilize system operation. Additionally, a high moisture content in the feedstock can lead to feedstock feeding or fluidization problems, disrupting system operation (You et al., 2018). On the other hand, a high moisture content (<40 wt.%) might serve to supply water as a reactant to the various reactions (e.g., wateregas reaction: C (s) þ H2O (g) 4 CO (g) þ H2 (g) (DH ¼ þ131 kJ/mol) and wateregas shift reaction: CO (g) þ H2O (g) 4 CO2 (g) þ H2 (g) (DH ¼ 41 kJ/mol)) of the reduction stage, potentially promoting hydrogen production (DH refers to the heat of reaction and negative values denote exothermic reactions). The drying rate of feedstock depends on various factors such as feedstock particle surface area, the temperature difference between feedstock particles and surrounding environment, moisture and convection velocity of surrounding flows, and diffusivity of moisture within the feedstock. During the pyrolysis stage, the feedstock molecules are decomposed into condensable gases, tar, and char in the absence of oxygen. The properties (e.g., carbon content, ash content, surface area, and pH) of the char produced in the stage are associated with the types/properties of feedstocks as well as the thermochemical conditions such as temperature, residence time, pressure, etc. As pyrolysis is an intermediate stage for the gasification process, the products of this stage will undergo additional thermochemical treatment during the subsequent stages (i.e., combustion and reduction). The combustion stage involves the complete or partial combustion of carbonaceous materials and some pyrolysis gas species (e.g., CO, H2, CH4, and CnHm), producing H2O, CO2, and CO. It generates the heat needed for drying, pyrolysis, and other endothermic reactions in the reduction stage. Main reactions of this stage
Chapter 3 Waste-to-energy include C (s) þ ½ O2 (g) / CO (g) (DH ¼ 111 kJ/mol) (char partial oxidation), C (s) þ O2 (g) / CO2 (g) (DH ¼ 394 kJ/mol) (char complete oxidation), H2 (g) þ ½ O2 (g) / H2O (g) (DH ¼ 242 kJ/mol) (gas oxidation), CO (g) þ ½ O2 (g) / CO2 (g) (DH ¼ 283 kJ/mol), and CH4 (g) þ ½ O2 (g) / CO (g) þ 2H2 (g) (DH ¼ 35.7 kJ/mol) (You et al., 2018). During the reduction stage, char reacts with H2O, CO2, and H2 to produce CO, H2, CH4, and other light hydrocarbons such as acetylene (C2H2) and ethylene (C2H4). In this stage, reduction reactions dominate over the combustion reactions. The final production of this stage is affected by various factors such as temperature, feedstock properties, gasifying agent (the oxygen agent supplied to the system), pressure, and the ratio between gasifying agent and feedstock. Generally, increasing temperature promotes the production of H2 and CO and reduces the production of CO2 and CH4, hydrocarbons, and tar. The carbon conversion efficiency (i.e., the ratio of the carbon mass flow in product gas to the initial carbon mass in the feedstock) and reaction rate increase as the temperature and pressure increase. A higher moisture content decreases the CO content but increases the CO2 content in the product gas. Typical gasifying agents include air, O2, CO2, or H2O, but the most commonly used gasifying agent is air because of its low cost and readily availability. However, air being the gasifying agent brings nitrogen to the product gas, reducing the heating value of product gas against the subsequent energy generation. For example, the heating value of product gas is 4e7 MJ/Nm3 for air, compared to 10e14 MJ/Nm3 for steam. Oxygen gasification produces syngas with a heating value of 10e18 MJ/m3 but it is costly due to the pure oxygen use (You et al., 2018). CO2 is commonly used as a gasifying agent together with a catalyst to promote the conversion of char, tar, and methane into syngas, with an obvious benefit of consuming the carbon dioxide from CCS. However, an external heat source is needed when steam or CO2 is used. The presence of alkali and alkaline earth metallic (AAEM) species in the original feedstock may enhance the syngas production and char decomposition because of the catalytic effects of AAEM species. Typical reactions during the reduction stage include C (s) þ CO2 (g) 4 2CO (g) (DH ¼ þ172 kJ/mol) (Boudouard reaction), C (s) þ H2O (g) 4 CO (g) þ H2 (g) (DH ¼ þ131 kJ/mol) (wateregas reaction), CO (g) þ H2O (g) 4 CO2 (g) þ H2 (g) (DH ¼ 41 kJ/mol) (wateregas shift reaction), and C (s) þ 2H2 (g) 4 CH4 (g) (DH ¼ 75 kJ/mol) (methane reaction) (You et al., 2018). 33
34 Chapter 3 Waste-to-energy Syngas mainly consists of hydrogen (H2), carbon monoxide (CO), and carbon dioxide (CO2), with minor fractions of water (H2O), methane (CH4), and nitrogen (N2) (if air is the gasifying agent supplying the nitrogen). Syngas can be transformed into more useful energy forms such as heat and electricity or upgraded to hydrogen with a high level of purity. As compared to incineration, gasification has a higher efficiency (30% vs. 10%e20% for incineration) and lower emissions (NOx and particulate emissions). The NOx emissions for gasification are far lower (6 mg per ton dry biomass) than combustion (43.5 mg per ton dry biomass) due to lower operating temperature and limited oxygen in comparison to the direct combustion process that occurs at a temperatre greater than 1000 C with a sufficient air supply (Guo et al., 2020). Biochar, as introduced previously, is typically produced in the amount no more than 20 wt.% of raw waste for a gasification process. Conventionally, the aim of gasification is to achieve a high syngas yield for maximum energy recovery. The application of gasification biochar has often been ignored previously, but it is gaining increasing attention because of biochar’s great potential for enhancing soil quality and sequestering carbon (You et al., 2017). Additional application possibilities are also being explored including using biochar as an adsorbent for electrochemical applications or mitigating water contamination (more details in Chapter 8). Tar is an undesired by-product of gasification as it may block and corrode the piping of gasification system, increasing the technical difficulty and costs of system operation and maintenance. Hence, tar reduction and control is one of the most significant technical challenges against gasification development. There are three different types of gasification reactors: fixed bed, fluidized bed, and entrained flow (Fig. 3.5) (You et al., 2018). Fixed bed gasifiers are featured by their simple configuration and Figure 3.5 Different gasification reactor configurations: (A) updraft fixed bed, (B) downdraft fixed bed, (C) bubbling fluidized bed, (D) circulating fluidized bed, and (E) entrain flow (You et al., 2018).
Chapter 3 Waste-to-energy cost-efficiency under small-scale (10 kWe10 MW), decentralized applications. In a fixed bed gasifier, four stratified temperature zones are formed corresponding to the four gasification stages. This stratification limits the heat transfer and the mixing of gas and solids and causes an uneven temperature distribution in the reactor. Fixed bed gasifiers are further divided to downdraft and updraft designs. In an updraft fixed bed gasifier (Fig. 3.5A), the gasifying agent is supplied to the reactor from the bottom, and the feedstock is fed from the top. The upward flowing gas brings the heat from the combustion zone to the drying zone, significantly enhancing the drying efficiency. Hence, updraft fixed bed gasifiers have a higher feedstock moisture content tolerance (up to 50 wt.%) (You et al., 2018). High thermal efficiencies can be achieved because of the enhanced heat exchange between the upflowing gas flow and descending feedstock. Different from downdraft fixed bed gasifiers, the product gas exits from upper part of the reactor, so the deposited ash has a lower chance to interact with the product gas due to the filtering effect of the bed. Hence, updraft fixed bed gasifiers are suitable for feedstocks with high ash contents (up to 15 wt.%). However, updraft fixed bed gasifiers are not suitable for feedstocks with high volatile contents because the upflowing gas passing through the pyrolysis zone might introduce tar to the product gas. In a downdraft gasifier (Fig. 3.5B), the gasifying agent is introduced into the gasifier at the combustion zone and the feedstock is fed from the top of the gasifier, while the product gas exits the reactor from the bottom. This design reduces the upward heat transfer from the combustion zone to the drying zone, lowering the drying efficiency of feedstock around the top of the reactor. Hence, the moisture content of the waste treated in downdraft gasifiers needs to be lower than 20 wt.%, suggesting a need of drying pretreatment for moist waste such as food waste and sewage sludge (Anukam et al., 2016). Due to the proximity of the product gas exit and the ash deposit at the bottom, the product gas can be easily contaminated by particulates, and additional measures are needed to purify the product gas prior to its application for, e.g., energy generation. The gas flow brings the tar formed at the pyrolysis zone to the combustion zone whose relatively high temperature facilitates a thermal cracking effect on tar, reducing the tar content (1 g/m3) of product gas (Guo et al., 2020). Compared to updraft fixed bed gasifiers, the decarburization and dehydrogenation reactions in the reduction zone of downdraft gasifiers are more efficient. Downdraft fixed bed downdraft gasifiers are the most economically feasible option for small-scale application 35
36 Chapter 3 Waste-to-energy with the potential to produce clean syngas for direct energy generation (Dimpl, 2010). They were also considered as the best choice for treating certain feedstocks such as bagasse based on such criteria as syngas quality, tar content, ease and cost of operation, and scalability (Anukam et al., 2016). Similar to the fluidized bed incinerators, fluidized bed gasifiers are featured by the formation of a semisuspended bed by an upward-flowing gasifying agent. They are suitable for intermediate-scale applications (5e100 MW) (You et al., 2018). Sand-like inertial bed materials are used for fluidisation, and a postreactor separation process is needed to separate biochar particles from the bed materials. The gasification process involves the continuous mixing and output of feedstock particles, both gasified and partially gasified. At atmospheric pressure and bed temperature lower than 910 C, the total carbon conversion efficiency in fluidized bed gasifiers was reported to range from 70% to 85% for an ER of 0.2e0.3. For fluidized bed gasifiers, the tar content of product gas ranges from 2 to 20 g/m3. Fluidized bed gasifiers can be further categorized into bubbling bed and circulating bed designs. For bubbling fluidized bed gasifiers (Fig. 3.5C), the excess gas flow relative to the fluidization flow that maintains the minimum fluidization condition leads to the formation of bubbles. The bubble diameter, bubble velocity, the area fraction of bubbles, and interphase gas transfer rate increase with increasing temperature but decrease with an increasing bed particle size. For bubbling fluidized bed gasifiers, an excessively high temperature could cause problems such as ash melting, sintering, and slagging (defluidization). For circulating fluidized bed gasifiers (Fig. 3.5D), the fluidization velocity is higher than that of bubbling bed gasifiers so that the feedstock particles can be suspended across the whole riser height. An external circulation channel is designed so that the particles coming out of the main reactor can be returned to it for further treatment. A cyclone is used for separating particles from the product gas while the return leg guides the separated particles back to the reaction vessel. This recirculating mechanism increases the residence time of feedstock particles as compared to bubbling fluidized bed gasifiers. Hence, circulating fluidized bed gasifiers have a less stringent requirements on (high) temperature and can be operated at relatively low temperatures (<750 C), mitigating various ash sinteringe related problems such as fouling and corrosion (You et al., 2018).
Chapter 3 Waste-to-energy 37 Table 3.4 Efficiency data for gasification-based WtE. Efficiency Energy Reactor types Electricity (%) Heat (%) Country References Electricity Circulating fluidized bed Downdraft fixed bed e e Fluidized bed Fluidized bed 27.4 15.8e25 16e45 29.1e32.9 23.6 27 e e e Finland e Iceland Czech Italy France Dong et al. (2019) Lee et al. (2013) Safarian et al. (2020) Brynda et al. (2020) Moradi et al. (2020) Francois et al. (2013) CHP e 39 For entrained flow gasifiers (Fig. 3.5E), the waste and gasifying agent are fed together into the reactor from the top. They are normally used in large-scale applications (>50 MW), especially for integrated gasification combined cycle plants (You et al., 2018). The residence time of the process is short, ranging from a few to tens of seconds, so a high temperature is required, which is effective for removing most of the tar and achieving a high carbon conversion efficiency (95%e99%). The carbon conversion efficiency is the ratio of the carbon mass flow in the product gas to the carbon mass in the raw feedstock. However, the short residence time requires that fine feedstock particles are used in the process. This means that fibrous waste or biomass is less suitable for entrained flow gasification as the grinding of the feedstock into fine particles will incur significant energy and costs. To facilitate the feeding process, fine particles are often mixed with water to form bioslurry, and the added water can serve as a reactant (e.g., for the water-gas shift reaction) to promote H2 production. Existing efficiency data for gasification-based WtE development are listed in Table 3.4. 5. Anaerobic digestion AD is a biochemical process that makes use of anaerobic microorganisms to decompose organic waste (e.g., food waste, manure, and crop residues) into a main gas product called biogas and a by-product called digestate in an oxygen-free environment. AD biogas generally consists of 50e70 vol.% CH4, 30e50 vol.% of CO2, and trace amounts of other gases such as H2, N2, H2S, water vapor, and other impurities (Li et al., 2019). Raw biogas can be
38 Chapter 3 Waste-to-energy upgraded and used as a transport fuel or added to local gas networks to displace the use of fossil natural gas. More commonly it can be combusted to produce heat and power, e.g., in an engine after certain purification processes. Digestate is rich in nutrients (e.g., nitrogen (N), phosphorus (P), and potassium (K)) and can be used as a biofertilizer for agricultural applications. The use of digestate has such benefits as lowered global warming potential and acidification potential in comparison to mineral or chemical fertilizers (Lijó et al., 2014). Hence, upon the design of AD-based WtE generation, it is important to combine the application of digestate with the primary objective of energy generation to achieve greater environmental benefits. An AD process involves four sequential stages, i.e., hydrolysis, acidogenesis, acetogenesis, and methanogenesis. During the hydrolysis stage, large, insoluble organic polymers (e.g., fats, proteins, and carbohydrates) are broken down into the monomeric forms of long-chain fatty acids, amino acids and glucose, respectively, under the effect of an exoenzyme, water molecules, and acidogenic bacteria. A typical hydrolysis reaction is about the splitting of glycosidic links, necessary for catalytic transformation of cellulose, expressed by (Anukam et al., 2019), ðC6 H10 O5 Þn þ nH2 O/nC6 H12 O6 þ nH2 (3.5) This reaction shows that the hydrolysis of cellulose (C6H10O5) with water (H2O) forms glucose (C6H12O6) and H2 (to be used in the methanogenesis stage). For MSW, the reaction rate of the stage can be relatively low, limiting the rate of the overall AD process. Mechanical, thermal, or chemical pretreatment can be applied to improve the degradability of MSW for higher biogas yields. During the acidogenesis stage, the soluble compounds produced in the hydrolysis stage are converted into various products such as volatile fatty acids (VFAs), ammonia (NH3), CO2, hydrogen sulfide (H2S), etc., by the work of acidogenic bacteria. Typical reactions of the stage include (Anukam et al., 2019) C6 H12 O6 42CH3 CH2 OH þ 2CO2 C6 H12 O6 þ 2H2 42CH3 CH2 COOH þ 2H2 O (3.6) C6 H12 O6 /3CH3 COOH The acetic acid (CH3COOH) formed will be consumed by the methane-producing bacteria and serves as a substrate for CH4 production in the subsequent stage. It is hard to distinguish between the acidogenic and acetogenic reactions as H2 and acetate (CH3COO) are key outputs of both. Acetogenesis, or dehydrogenation, converts the simple molecules (e.g., propionic and butyric acids) produced in the
Chapter 3 Waste-to-energy acidogenesis stage into hydrogen (H2), carbon dioxide (CO2), and acetic acid (Anukam et al., 2019): þ CH3 CH2 COO þ 3H2 O/CH3 COO þ HC O 3 þ H þ 3H2 CH3 CH2 OH þ H2 O/CH3 COO þ Hþ þ 2H2 (3.7) C6 H12 O6 þ 2H2 O42CH3 COOH þ 2CO2 þ 4H Homoacetogenic bacteria can further use the H2 and CO2 formed to yield additional acetic acid. The overall efficiency of the biogas production is largely controlled by the acetogenesis stage because the reduction of CH3COO accounts for 70% of the CH4 production. In the final stage (methanogenesis), methanogenic bacteria consume the previously formed acetic acid, H2, and other compounds (e.g., formic acid and methanol) to generate CH4 and CO2. The methanogenic bacteria are highly sensitive to the changes of digester environment. The consumption of the protons and VFAs by methanogens mitigates the environment change of digester and the inhibition of biodegradation. The efficiency of this stage is primarily determined by the digestion of carbon-based compounds, such as formic acid and methanol. Two of the major CH4-producing reactions of the stage are (1) the breakdown of acetic acid performed by the acetophilic bacteria group with the process decarboxylation, and (2) the reduction of CO2 and H2 by the hydrogenophilic bacteria group (Korres et al., 2013): CH3 COOH/CH4 þ CO2 CO2 þ 4H2 /CH4 þ 2H2 O (3.8) The performance of AD depends on a variety of factors such as feedstock types, temperature, pH, retention time, etc. The composition and properties of feedstock, temperature, and pH affect the ecosystem of a digester and thus the metabolic rate of microorganisms carrying out the decomposition process. The retention time directly determines the length of time for which organic matter is left to decompose. The retention time is also closely related to the organic loading rate (the quantity of volatile solids entering a digester per day) for continuous AD reactors. A longer retention time and a low organic loading rate generally promote CH4 generation (Mao et al., 2015). Common AD waste feedstocks include animal manure, sewage sludge, and food waste whose compositions vary significantly as shown in Chapter 2. The variations in the properties of the feedstocks can lead to considerable performance differences in their AD processes. Food waste is an excellent feedstock for AD. However, the AD of food waste suffers from a potential 39
40 Chapter 3 Waste-to-energy risk of instability due to nutrient imbalance (optimal C/N ratio is between 20 and 35), and the codigestion of food waste with animal manure has been suggested as a good strategy for achieving a more stable AD process (Mao et al., 2015). Similarly, the codigestion of food waste and cow manure was able to achieve a higher biogas yield than mono-digestion of cow manure (Xing et al., 2020). Finally, depending on the content of total solids associated with the types of raw feedstocks, there are wet AD (<15% total solids) and high solid AD designs. AD microorganisms are sensitive to the environmental conditions, in particular temperature and pH. Depending on the range of temperature, AD can be classified into mesophilic (35 1 C) and thermophilic (55 1 C) processes which serve to promote the activities of different species of bacteria for the decomposition of organic materials. Upon the treatment of kitchen waste, it was found that thermophilic conditions led to higher CH4 yields than mesophilic conditions (Jiang et al., 2018). The main strengths of mesophilic AD include increased stability and lower energy demands as compared to thermophilic AD, which makes mesophilic AD more suitable for nitrogen-rich organic materials (e.g., sludge) (Yin et al., 2018). The stability of an AD system is also closely related to its pH condition. In general, a pH range between 6.8 and 7.4 is desirable for AD, although the optimal pH condition varies across the different stages with a pH of 5.5e6.5 for acidogenesis and a pH of 6.5e8.2 for methanogenesis (Mao et al., 2015). As the AD process goes on, the digester environment will get acidified and an overly acidic or alkaline environment (<6 or >8.5) inhibits the activities of microorganisms and thus reduces biogas production (Uçkun Kiran et al., 2016). The reported biogas yield ranged from 105 m3/tonne wet weight (ww) to 190 m3/tonne ww, depending on the various factors (Ascher et al., 2020). Existing efficiency data for AD-based WtE development is listed in Table 3.5. Table 3.5 Efficiency data for AD-based WtE. Efficiency Energy Electricity (%) Heat (%) References Electricity CHP 37 30 40 33 90 e 55 45 38 Di Maria et al. (2019) O’Connor et al. (2020) Masebinu et al. (2018) Huiru et al. (2019) Di Maria et al. (2019)
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44 Chapter 3 Waste-to-energy Shackley, S., Carter, S., Knowles, T., Middelink, E., Haefele, S., & Haszeldine, S. (2012). Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, Part II: Field trial results, carbon abatement, economic assessment and conclusions. Energy Policy, 41, 618e623. https:// doi.org/10.1016/j.enpol.2011.11.023 Shackley, S., Carter, S., Knowles, T., Middelink, E., Haefele, S., Sohi, S., … Haszeldine, S. (2012). Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, Part I: Context, chemical properties, environmental and health and safety issues. Energy Policy, 42, 49e58. https://doi.org/10.1016/j.enpol.2011.11.026 Shackley, S., Hammond, J., Gaunt, J., & Ibarrola, R. (2011). The feasibility and costs of biochar deployment in the UK. Carbon Management, 2(3), 335e356. https://doi.org/10.4155/cmt.11.22 Tang, Y., Dong, J., Li, G., Zheng, Y., Chi, Y., Nzihou, A., Weiss-Hortala, E., & Ye, C. (2020). Environmental and exergetic life cycle assessment of incineration- and gasification-based waste to energy systems in China. Energy, 205, 118002. https://doi.org/10.1016/j.energy.2020.118002 Uçkun Kiran, E., Stamatelatou, K., Antonopoulou, G., & Lyberatos, G. (2016). 10 Production of biogas via anaerobic digestion. In: R. Luque, C. S. K. Lin, K. Wilson, & J. B. T.-H. of B. P, Second E. Clark (Eds.), pp. 259e301). Woodhead Publishing. 10.1016/B978-0-08-100455-5.00010-2. Valentine, S. V. (2011). Emerging symbiosis: Renewable energy and energy security. Renewable and Sustainable Energy Reviews, 15(9), 4572e4578. https://doi.org/10.1016/j.rser.2011.07.095 Van Caneghem, J., Brems, A., Lievens, P., Block, C., Billen, P., Vermeulen, I., Dewil, R., Baeyens, J., & Vandecasteele, C. (2012). Fluidized bed waste incinerators: Design, operational and environmental issues. Progress in Energy and Combustion Science, 38(4), 551e582. https://doi.org/10.1016/ j.pecs.2012.03.001 Vermeulen, I., Van Caneghem, J., Block, C., Dewulf, W., & Vandecasteele, C. (2012). Environmental impact of incineration of calorific industrial waste: Rotary kiln vs. cement kiln. Waste Management, 32(10), 1853e1863. https:// doi.org/10.1016/j.wasman.2012.05.035 Williams, P. T. (2021). Hydrogen and carbon nanotubes from pyrolysis-catalysis of waste plastics: A review. Waste and Biomass Valorization, 12(1), 1e28. https://doi.org/10.1007/s12649-020-01054-w Xia, Z., Shan, P., Chen, C., Du, H., Huang, J., & Bai, L. (2020). A two-fluid model simulation of an industrial moving grate waste incinerator. Waste Management, 104, 183e191. https://doi.org/10.1016/j.wasman.2020.01.016 Xing, B.-S., Cao, S., Han, Y., Wen, J., Zhang, K., & Wang, X. C. (2020). Stable and high-rate anaerobic co-digestion of food waste and cow manure: Optimisation of start-up conditions. Bioresource Technology, 307, 123195. https://doi.org/10.1016/j.biortech.2020.123195 Yang, Y., Wang, J., Chong, K., & Bridgwater, A. V. (2018). A techno-economic analysis of energy recovery from organic fraction of municipal solid waste (MSW) by an integrated intermediate pyrolysis and combined heat and power (CHP) plant. Energy Conversion and Management, 174, 406e416. Yang, Y., Brammer, J. G., Wright, D. G., Scott, J. A., Serrano, C., & Bridgwater, A. V. (2017). Combined heat and power from the intermediate pyrolysis of biomass materials: performance, economics and environmental impact. Applied Energy, 191, 639e652.
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Waste-to-biohydrogen 4 Abstract This chapter focuses on biohydrogen recovery from waste for transportation applications. It explains the technical principles and influential factors of two main types of waste-to-biohydrogen technologies (thermochemical and biochemical). The considered thermochemical technologies include hydrothermal gasification, steam gasification, and biooil steam reformation. The considered biochemical technologies include dark-fermentation and photo-fermentation. The biohydrogen yields of the technologies are summarized. This chapter also emphasizes the importance of product upgrading, separation and purification for the transportation application of biohydrogen. The technical principles and procedures of several major upgrading, separation and purification technologies are detailed. Keywords: Waste-to-biohydrogen; Gasification; Fermentation; Product upgrading; Separation and purification; Hydrogen yields. 1. Introduction The transport sector is one of the major consumers of primary energy, responsible for around 66% of oil consumption and around 27% of global GHG emissions (Rezvani et al., 2018). Fossil fuelepowered transport has also been one of the major sources of air pollution (e.g., PM2.5 and O3), with significant health repercussions, e.g., causing 28,000e36,000 deaths a year in the United Kingdom (COMEAP, 2018). Hydrogen is the ultimate solution for meeting the sustainable energy demand and for reducing air pollutant and GHG emissions of the transport sector toward a cleaner environment. Hydrogen can be used by fuel cells in automotive applications to power pollutantefree transport. The hydrogen-powered transport is expected to play a critical role in facilitating the transition from a fossil fuelebased economy to a low-carbon economy in response to the ambition to reduce or ban petrol and diesel cars, and tackle the challenges of the depletion of fossil fuels, and global climate change. Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00008-6 Copyright © 2022 Elsevier Inc. All rights reserved. 47
48 Chapter 4 Waste-to-biohydrogen However, about 96% hydrogen is currently produced based on the transformation of fossil fuels with an annual CO2 production of 500 million tonnes, i.e., 2% of the global energyerelated CO2 emissions (IEA, 2016; Nikolaidis & Poullikkas, 2017). It is impossible to achieve the full benefits of hydrogen as a clean, versatile, and efficient transport fuel without the use of renewable energy sources for hydrogen production. Hydrogen production with locally available renewable energy sources is most attractive (Singh et al., 2015). Waste-to-biohydrogen (WtH) technologies (e.g., fermentation and gasification) serve as a promising solution for low-carbon hydrogen production with locally available waste biomass. Waste-derived biohydrogen for powering the transport sector leads to an innovative “Waste-to-Wheel” strategy upon the use of the hydrogen for fuel cell electric vehicles (FCEVs). Meanwhile, WtH also contributes to developing low-carbon waste management practices, making it a solution of dual benefits. In particular, community-based, distributed WtH systems benefit from the reduced costs and emissions of the transportation of hydrogen and waste as well as contained odor and pathogen transmission along the waste transportation. They also match well with the requirement of FCEVs on distributed hydrogen refueling stations. Communities are a key stakeholder of such WtH development by being the “supplier” of waste biomass, the “carrier” of WtH systems, and the “user” of FCEV services (You et al., 2016). Distributed WtH systems have the potential to foster a culture of energy and environmental conservation by bringing residents and communities closer to the notion of sustainable waste management (You, Tong, Armin-Hoiland, Tong, & Wang, 2017). 2. Biohydrogen production technologies Similar to WtE conversion, WtH generation can be achieved using thermochemical (i.e., gasification and pyrolysis) and biochemical (fermentation) methods, respectively. Their fundamentals and process performance will be detailed as follows. 2.1 Gasification As shown in Chapter 3, hydrogen-containing syngas is the primary product of gasification. Toward the production of highpurity hydrogen via the process of gasification, two approaches can be adopted: (1) adapting gasification for hydrogen-rich syngas production and/or (2) applying downstream technologies to
Chapter 4 Waste-to-biohydrogen improve the hydrogen purity. Typical hydrogen-rich syngas production technologies include hydrothermal gasification, steam gasification, and biooil steam reforming. 2.1.1 Hydrothermal gasification Hydrothermal gasification utilizes hot compressed water as a medium to generate hydrogen-rich gas from carbonaceous materials under near-critical temperature up to around 500 C. The technology is suitable for feedstocks with a moisture content higher than 30% such as sewage sludge and algae, and the supercritical condition reduces the energy consumption of drying pretreatment as required by normal gasification of moist feedstock. Main advantages of hydrothermal gasification include saving the need for energy-intensive drying pretreatment for feedstocks with high moisture contents, facilitating subsequent H2 utilization with an elevated pressure, tar reduction, simplified gas purification, etc. The overall hydrothermal gasification reaction can be expressed as Ca Hb Oc þ ð2a cÞH2 O/aCO2 þ ð2a c þ b=2ÞH2 (>374.3 C) (4.1) Under elevated temperature and pressure (22.1 MPa), water is under the supercritical state where the liquid and gas phase become miscible. Under the critical temperature condition, the density of water decreases drastically with the broken of hydrogen bonds in water. This leads to a significant decrease in the dielectric constant of water and a drastic increase in the solubility of organic solvent. These changes in the properties make water a nonpolar solvent and a benign reactant with high diffusivity and excellent transport properties for gasification reactions (He et al., 2014). Specifically, under the supercritical state, water serves as an acid/base catalyst for hydrogen production from the formic acid degradation via decarboxylation (HCOOH / CO2 þ H2) and dehydration (HCOOH / CO þ H2O) reactions. The reduced density of water also favors the free-radical reactions and contributes to the formation of radicals such as the hydroxyl radical with hydrogen production (e.g., H2O þ H 4 H2 þ OH). Finally, water also directly serves as a reactant for hydrogen production, and it was shown that as high as 50% hydrogen generated during hydrothermal gasification came from water (Kruse et al., 2003). Temperature is the most significant factor in affecting the production of hydrothermal gasification, and higher hydrogen yields can be achieved at a higher temperature condition. However, the production of alkanes via CeO cleavage and hydrogenation 49
50 Chapter 4 Waste-to-biohydrogen might also be promoted with increasing temperature, limiting the increase in the yield of hydrogen via CeC cleavage and the wateregas shift reaction. Catalysts have been adopted to preserve the reactions related to CeC cleavage and/or the wateregas shift reaction while suppressing CeO cleavage (Davda et al., 2005). An appropriate amount of oxidant can accelerate hydrogen production by supplying heat to the endothermic supercritical water gasification reactions, but the amount needs to be controlled as excess oxidant might lead to the consumption of H2 produced (Guo et al., 2010). Catalysts can lower the activation energy required for gasification reactions and improve the process efficiency and yields of hydrogen. Alkali salts such as NaOH, Na2CO3, and K2CO3 have been used as catalysts to promote hydrogen production while supressing the production of tar/oil and char during the hydrothermal gasification process. Major mechanisms underlying the catalytic effects include, e.g., (1) enhancing the wateregas shift reaction with CO from decarbonylation of hydroxylated carbonyl compounds, (2) enhancing CeC bonds cleavage based on the formation of formate salt, and (3) the reaction of sodium carboxylate with water, leading to the generation of additional hydrogen (He et al., 2014). Typical challenges associated with the use of the alkali catalysts are the risks of corrosion, plugging, and fouling, reducing system stability. Heterogeneous catalysts such as Ru/C, Raney-iron, Raneycobalt, and Raney-nickel have been tested for hydrothermal gasification. Using 90 wt.% Raney nickel and 10 wt.% Ru/AC as the catalyst, hydrothermal gasification of wastewater from the hydrothermal liquefaction of human feces achieved a hydrogen yield of 10.61 mol/kg dry feed (H2 content ¼ 56.3%) (Watson et al., 2017). Raney nickel catalysts prepared by leaching out aluminum from NieAl alloy with concentrated sodium hydroxide have a specific surface area (50e100 m2/g) much higher than that (<1 m2/g) of metal powder and wire catalysts. Upon the hydrothermal gasification of activated sewage sludge, a maximum hydrogen yield of 18.13 mol/kg dry sludge was achieved using 1.4 g Raney-nickel-Mo2 catalyst/g dry sludge at 450 C and 25 min. The improved yield was related to the spongy structure, large specific area, and good pore space concentration distribution of the catalyst (Chen et al., 2020). 2.1.2 Steam gasification Steam gasification utilizes steam as the gasifying agent to promote hydrogen production. It can generate the syngas with a
Chapter 4 Waste-to-biohydrogen hydrogen content of up to 60 vol.% and an HHV as high as 15e20 MJ/Nm3 (Edreis et al., 2014; Parthasarathy & Narayanan, 2014). However, an external heating source is needed to sustain the steam gasification, which poses an additional economic burden on the development. In addition to the wateregas shift reaction, the steam reforming reactions (CH4 þ H2O / CO þ 3H2 and C þ H2O / CO þ H2) are also promoted with the use of steam. Major influential factors for the hydrogen production of steam gasification include types of feedstocks, feedstock particle size, temperature, steam to biomass ratio, use of catalysts, etc. Similar to conventional gasification, smaller feedstock particles improve the heat and mass transfer and secondary cracking reactions during the steam gasification process, leading to higher process efficiencies in terms of hydrogen production. The condition of higher process temperature improves the carbon conversion efficiency by promoting the Boudouard reaction and enhances tar decomposition into gaseous products via thermal cracking reactions. There is an optimal steam-to-feedstock ratio for the maximal hydrogen production of steam gasification. When the steam-to-feedstock ratio is smaller than this optimal value, increasing the ratio promotes the reforming reactions (watere gas, wateregas shift, and reforming reactions) of carbon and methane to generate more hydrogen. However, an excessive steam-to-feedstock ratio brings down the process temperature, which serves to reduce and increase the production of hydrogen and tar, respectively. Catalysts such as dolomite, Ni-based, alkaline metal, alumina, alumina silicate, Na2CO3, K2CO3, ZnCl2, etc., have been tested for their potential to promote steam gasification. Catalysts have three potential impacts on the steam gasification process toward higher hydrogen production: (1) enhancing the heat and mass transfer between feedstock particles for a higher gasification efficiency, (2) directly promoting the hydrogen-producing reactions in the process by lowering the activation energy required by the reactions, and (3) facilitating the destruction of tars into various products including hydrogen. Upon the use of the catalysts, there is always a decision to be made such that a balance between the added costs and the effectiveness of catalysts can be achieved. 51
52 Chapter 4 Waste-to-biohydrogen 2.1.3 Biooil steam reforming The biooil steam reforming process uses steam to turn the biooil derived from fast or flash pyrolysis into a hydrogen-rich gas product. It involves two main types of reactions Steam reforming : Ca Hb Oc þ ða cÞH2 O/aCO þ ða þ ðb=2Þ cÞH2 Water gas shift: CO þ H2 O4CO2 þ H2 (4.2) This process can achieve a hydrogen concentration up to 70% (Setiabudi et al., 2020). It appears to be difficult to further increase the hydrogen concentration due to (i) the thermodynamic limitation of the wateregas shift reaction, (ii) the existence of other reactions (such as CO and CO2 methanation), and (iii) carbon formation, limiting the further generation of hydrogen. It is worth noting that the carbon formed as a result of the decomposition and dehydrogenation of oxygenates, as well as polymerization of oxygenates, can deactivate catalysts via coke deposition. Due to the special feedstock (biooil derived from pyrolysis), a biooil steam reforming system is commonly based on the combination of pyrolysis and biooil steam reforming. The pyrolysis stage serves to produce biooil mainly consisting of oxygenated compounds such as acids (acetic acid and formic acid), alcohol (methanol and phenol), aldehydes, and acetone from waste biomass at a temperature condition between 500 C and 600 C. The steam reforming stage normally happens in a fluidized bed reactor with a temperature higher than that of the pyrolysis stage. Catalysts are often used and play a critical role in promoting and stabilizing the biooil steam reforming process. Ni-based catalysts are effective to promote the steam reforming reactions via enhancing the cleavage of CeC and CeH bonds as desired by the water-gas shift reaction. They have additional strengths of being cost-effective (as compared to noble metals) and chemical and thermal stable. However, they suffer from the disadvantages of being prone to coking by carbon accumulated on the catalysts’ active sites. To relieve the problem, Ni particles need to be widely dispersed on the surface of the catalyst to supress carbon deposition. Various types of metal oxides have been used as the support for Ni-based catalysts. Ni/ZrO2, Ni/Al2O3, and Ni/MgO catalysts showed good performance (>98% of conversion) due to the redox properties, high surface area, and high oxygenate accessibility to Ni sites, respectively (Setiabudi et al., 2020). Alkaline-earth (e.g., Mg and Ca) elements can be added as promoters to improve the stability of catalysts by preventing the carbon formation from the acid sites. The addition of alkali metal
Chapter 4 Waste-to-biohydrogen species (e.g., K and Na) to the Ni catalysts enhances the catalytic steam reforming process due to the increased interaction between adsorbed species and metal sites. A less economically viable method is to use air combustion to eliminate the carbon deposition by catalyst calcination for up to 600 C. Generally, noble metal (e.g., Pt and Rh) catalysts showed excellent catalytic and carbon deposition mitigation abilities for steam reforming and wateregas shift reactions. But the high costs limit their practical applications, and they have been used as promoters of Ni-based catalysts to achieve high levels of carbon deposition resistance and catalytic stability. Rh-based catalysts show excellent performance in steam reforming of biooil but they also suffer from the problem of deactivation due to catalysts’ structural alteration caused by their support’s aging and coke deposition which suppress reforming reactions. It was shown that, for monometallic catalysts, the catalytic performance ranks as Rh > Ni > Co > Ru > Ir > Pt. Lower carbon contents were found in RheNi (12.2 wt.%) and RueNi (13.3 wt.%) catalysts as compared to Ni/CeO2eAl2O3 (15.5 wt.%). In recent, biochar has been exploited as a catalyst because of its cost-effectiveness. For example, upon the use of gasification biochar (with alkali and alkaline earth metallic (AAEM) species) to catalyze steam reforming of biooil, a maximum hydrogen yield of 89.13% and a concentration of 75.97% were achieved (Ma et al., 2017). Table 4.1 summarizes the hydrogen production of gasification and pyrolysis. It shows that steam gasification could achieve the production of a hydrogen concentration of around 70% with an optimal temperature around 700 C. The yields of hydrogen recovered from MSW are generally lower than that from agricultural waste, partially due to its relatively complex components and some components being less favorable for hydrogen recovery. 2.2 Fermentation Fermentation utilizes microorganisms to derive organic acids, alcohols, acetone, and then hydrogen and CO2 from the decomposition of carbohydrate-rich and biodegradable waste. The process parameters of the biochemical-based biohydrogen production include substrate concentration, mode and reaction conditions (e.g., temperature, pH, partial pressure of hydrogen, hydraulic retention time (HRT), etc.), feedstock and medium composition, and microorganism types. Fermentation normally occurs at ambient temperature and pressure and is less energyintensive than the thermochemical processes. The downside of 53
54 Chapter 4 Waste-to-biohydrogen Table 4.1 Summary of hydrogen production of gasification and pyrolysis under different process conditions. Feedstock Agent/ Technology Catalyst Biooil/biochar slurry Steam La0.8Ce0.2FeO3 gasification perovskite type catalysts Steam LaCo0.9Cu0.1O3 90 wt.% biooil gasification and 10 wt.% perovskite biochar type catalysts Biooil Steam Biochar reforming catalyst Biochar Steam Steam gasification Ni/BC4 Biochar from rice Steam catalyst husk for biooil reforming of biooil Sawdust Wateregas e shift reactor and oxidation Sawdust wood Steam Tyre char pellets gasification Acid treated tyre pyrolysis Cattle manure Gasification e Wheat straw Steam Ni/cotton char (biochar) gasification catalyst Corn stalk Steam CaO gasification Reactor Optimal H2 temperature concentration (%) H2 yield ( C) Fixed bed 800 82.01% e Fixed bed 800 75.33% e Fixed bed 900 75.97% 89.13% Fixed bed 800 Fixed bed 700 74% 71.20% 0.0714 kg/kg e Fluidized bed 600e650 e 33 mol.% Two-stage 900 fixed bed Two-stage 900 fixed bed 56% 39.20 mmol/g e 30.4 mmol/g Fixed bed Fixed bed 850 800 (550 pyrolysis) Fluidized bed 650 57.58% 64.02% 61.23% 0.93 m3/kg 92.08 mg/g biomass, gas yield about 90 wt.% 493.91 mL/g biomass
Chapter 4 Waste-to-biohydrogen 55 Table 4.1 Summary of hydrogen production of gasification and pyrolysis under different process conditions.dcontinued Feedstock Agent/ Technology Catalyst Gasification e Commercial a-cellulose and agricultural waste MSW Steam e gasification MSW Steam Steam gasification Reactor Optimal H2 temperature concentration (%) H2 yield ( C) Fluidized bed 1000 29.50% e Fixed bed 750 49.42% Fixed bed 800 277.67 mL/g MSW 34.34 g/kg MSW e Adapted from Lui, J., Chen, W.-H., Tsang, D. C. W., & You, S. (2020). A critical review on the principles, applications, and challenges of waste-to-hydrogen technologies. Renewable and Sustainable Energy Reviews, 134, 110365. the process is about its relatively low hydrogen yield and reaction rate. Depending on the use of light, fermentation is further categorized into dark- and photo-fermentation. 2.2.1 Dark-fermentation Dark-fermentation makes use of anaerobic bacteria to treat carbohydrates to generate hydrogen and an effluent consisting of e.g., butyric acid, acetic acid, propionic acid and ethanol under anoxic and dark conditions. It is featured by being relatively simple and cheap in terms of bioreactor design and has higher hydrogen evolution rates (the amount of hydrogen evolved in unit time) as compared to photo-fermentation. It is suitable for treating a variety of waste feedstocks such as stillage, food waste, sludge, paper waste, pomace, stalks, bagasse, etc. Major disadvantages of this technology include significant by-product generation and reactor-to-reactor variation, and low chemical oxygen demand (COD) removal (Dincer & Acar, 2015). To achieve higher energy and process efficiencies, this technology can be coupled with others such as microbial fuel- and electrolysis cells, wastewater treatment, or microalgal biorefinery (Wong et al., 2014). The dark fermentation process involves a series of biochemical reactions such as glycolysis, pyruvate degradation through the pyruvate ferredoxin oxidoreductase (PFOR) pathway or the pyruvate
56 Chapter 4 Waste-to-biohydrogen formate-lyase (PFL) pathway, and hydrogen production through the formate-hydrogen lyase or the reduction of protons (Bundhoo, 2019). There are three groups of hydrogen producers for darkfermentation including spore-forming obligate anaerobes, none spore-forming obligate anaerobes, and facultative anaerobes (Cabrol et al., 2017). Clostridium sp. achieves a hydrogen yield of 1.5e3 mol H2/mol hexose and needs to work under a condition of pH < 5.0. However, the yield of hydrogen per substrate is limited by metabolic constraints of the dark-fermentation microorganisms. The theoretical limit, referred to as the “Thauer limit,” suggests that 4 mol hydrogen can be produced per mol of glucose for dark-fermentation (Thauer et al., 1977). Recently, a hydrogen yield of 5.6 mol/mol glucose (40% higher than the Thauer limit) was achieved via the precision design of an artificial microbial consortium composed of Enterobacteriaceae (with high hydrogen evolution rates) and Clostridiaceae (with high hydrogen yields) in a ratio of 1:10,000 (Ergal et al., 2020). Non-spore-forming obligate anaerobes include Ethanoligenens harbinense, Acetanaerobacterium elongatum, Megasphaera sp., Acidaminococcus sp., and Prevotella sp. (Cabrol et al., 2017). They play a major role in ethanol-type fermentation yielding ethanol, H2 and CO2, lactic acid production, and amino acid degradation toward H2 production (Dahiya et al., 2020). Facultative anaerobes include Citrobacter sp., Klebsiella sp., Enterobacter sp., Bacillus sp., Shewanella oneidensis and Pseudomonas stutzeri. There are different types of reactor designs for darkfermentation, e.g., annular-hybrid bioreactor (AHB), continuous stirred tank reactor (CSTR), up-flow anaerobic packed bed reactor (UAnPBR), up-flow anaerobic sludge blanket (UASB), chemostat bioreactor, fixed-bed bioreactor, anaerobic fluidized bed reactor (AFBR), etc. (Gorgec & Karapinar, 2019). These reactors differ in terms of their modes of operation as well as the arrangements of mass and energy flows which are reflected by the variations in HRT, substrate concentration, techniques of immobilization, microbial support materials, etc. An AHB is made of two coaxial cylindrical reactors where fermentation broth is circulated between the inner and outer reactors periodically using circulation pumps. A fluorescent lamp can be installed inside a glass chamber and a halogen lamp can be placed outside of the reactors for illumination (Argun & Kargi, 2010). A CSTR involves the use of a mechanical stirrer to continuously agitate the medium in a tank to achieve a high mass transfer efficiency and a high rate of hydrolysis and
Chapter 4 Waste-to-biohydrogen acidification. CSTR systems have the problems of operational instability and limited hydrogen production rates due to their low HRT and difficulty in maintaining sufficient bacterial population (De Amorim et al., 2009). For a UAnPBR, the gas effluent is extracted from the top of reactor, while the influent is fed from the bottom, which leads to a relatively high productivity. A UASB is designed to manage the washout of organic biomass during fermentation and is featured by a high treatment efficiency, a short HRT, and improved stability. AFBR systems make use of an upward flow to suspend an inert support or granules in a tall column reactor where microorganisms are in the form of attached ez et al., 2013). They are featured by high biofilm (Muñoz-Pa organic loading rates, good mixing, and low HRT operation, and their treated effluent is recirculated to prevent short circuits and dead zones in the systems. Typical support materials include alginate gel, activated carbon, zeolite, polystyrene, clay, celite, etc. To improve the hydrogen productivity of dark-fermentation toward large-scale applications, feedstocks, particularly lignocellulosic feedstocks could be pretreated (using acid or thermal pretreatment) to destroy the lignin seal protecting the cellulose molecules and to facilitate their release into the solution. This will enhance the enzymatic digestibility and acidogenic fermentation with the destruction of their crystalline arrangement and depolymerization. Additionally, mixed microbial culture, i.e., inoculum can be pretreated using heat-shock to selectively destroy H2-consuming bacteria (e.g., hydrogenotrophic methanogens, propionate producers, homoacetogens, and sulfatereducing bacteria) and enrich H2-producing bacteria to enhance hydrogen production. The effectiveness of pretreatment is related to the operating conditions (e.g., pH, temperature, and H2 partial pressure) of dark-fermentation. Upon the selection of pretreatment technologies, it is also important to ensure the enhanced hydrogen recovery offsets the energy consumed by the pretreatment from an overall energy efficiency perspective. 2.2.2 Photo-fermentation Photo-fermentation utilizes anoxygenic photosynthetic bacteria to decompose organic substrate to produce hydrogen and CO2 in an anaerobic environment. Some of the main advantages of photo-fermentation include nearly complete substrate conversion and high availability of feedstocks, but it suffers from the weakness of low volumetric hydrogen production rates and conversion efficiencies (Dincer & Acar, 2015). A photo-fermentation 57
58 Chapter 4 Waste-to-biohydrogen biohydrogen production process considering acetate as the model feed substrate can be described as CH3 COOH þ 2H2 O/2CO2 þ 4H2 (4.3) Specifically, hydrogen is generated by nitrogenase of photosynthetic bacteria with photosynthetic phosphorylation. The electrons from the oxidation reaction of organic substances are transported through electron carriers to pump protons through cell membrane to produce adenosine triphosphate (ATP). Part of the used electrons are sent to ferredoxin, driving electrons to nitrogenase with the formed ATP to produce hydrogen. The hydrogen yield of photo-fermentation varies significantly depending on various factors such as feedstock types and the use of pretreatment methods. For example, for the photofermentation of wheat straw, the maximum hydrogen yields varied from 254 mL/L to 712 mL/L for pretreatment using H2SO4 and ammonia, respectively (Mirza et al., 2013). For the photo-fermentation of corn stalk and corn cob, with NaOH and HCl being used for pretreatment, the maximum hydrogen yields were reported to be 594.4 mL/g TVS and 738.1 mL/g TVS, respectively (Yang et al., 2010, 2015). Different pilot-scale photo-fermentation systems have been tested. Zhang et al. (2017) evaluated the photo-fermentative hydrogen production of a 4 m3 pilot-scale baffled continuousflow photoreactor with four sequential chambers (Fig. 4.1) Figure 4.1 A schematic of the 4 m3 pilot-scale baffled continuous flow photo-fermentative hydrogen production system (Zhang et al., 2017). 1. Intelligent control center, 2. Peristaltic pump, 3. Hydrogen producing medium tank, 4. Photosynthetic bacteria tank, 5. Solar fiber import plant, 6. Solar water heater, 7. Gas tank, 8. Gas flowmeter, 9. No.1 chamber, 10. No.2 chamber, 11. No.3 chamber, 12. No.4 chamber, 13. Thermal-insulating board, 14. Circulating pump, 15. Hot water storage tank.
Chapter 4 Waste-to-biohydrogen 59 (Zhang et al., 2017). The system treated wastewater containing 10 g/L glucose using microflora HAU-M1 at 30 C, with a light intensity of 3000 200 lux and an HRT of 24e72 h. The system achieved an overall hydrogen production rate of 234 mol/d, 310 mol/d, and 381 mol/d at an HRT of 72, 48, and 24 h, respectively. Lu et al. (2020) studied the hydrogen production of dark- and photo-fermentation based on an 11 m3 pilot-scale system consisting of sequential dark- and photo-fermentation bioreactors with solar energy being the source of heat, illumination, and power to improve the system’s carbon footprint and economic viability (Fig. 4.2). The dark-fermentation bioreactor was comprised of three 1 m3 chambers in series and the photo-fermentation bioreactor was comprised of eight 1 m3 chambers in series. Substrate was continuously pumped into chamber No.1 using a peristaltic pump at a flow rate of 150e6061 mL/min. To heat the bioreactors, hot water from the insulation tank was pumped into the internal thermal insulation layer of each chamber. A solar illuminator was used as the primary light source, while a light emitting diode system served as an auxiliary light source for photo-fermentation. The system achieved a gas production rate of 96.30 mol/m3-d and 224.68 mol/m3-d for dark- and photo-fermentation, respectively. Hybrid or integrated systems consisting of both dark- and photo-fermentation are developed to improve the yield of hydrogen from dark-fermentation via improved utilization of Figure 4.2 A schematic of the automated control bioreactor. 1. Automated control center, 2. Seed tanks, 3. Light source, 4. Agitator, 5. Dark-fermentation medium, 6. Gas tank, 7. Gas flowmeter, 8. No.1 Chamber, 9. No.2 Chamber, 10. No.3 Chamber, 11. Processing chamber, 12. No.4 Chamber, 13. No.5 Chamber, 14. No.6 Chamber, 15. No.7 Chamber, 16. Internal thermal insulation layer, 17. Optical fiberebased solar energy transport system, 18. Solar water heater, 19. Thermal insulation tank (Lu et al., 2020).
60 Chapter 4 Waste-to-biohydrogen the organic acids generated and the alleviation of process inhibition. There are commonly two configurations for the hybrid development: (1) successive dark-fermentation and photofermentation in two respective reactors (i.e., two-stage systems) and (2) simultaneous dark-and photo-fermentation in the same reactor (i.e., co-fermentation) (Zhang et al., 2020). For example, a two-stage hybrid system was used to treat chewing gum production waste with the effluent (containing mainly xylitol, butyric, acetic, lactic, and propionic acids) from the dark process being further treated in photo-fermentation with Rhodobacter sphaeroides bacteria. The hybrid system achieved a maximum hydrogen production of 0.36 L/Lmedium at a concentration of 67 g waste/L for dark-fermentation and a maximum hydrogen production of 0.80 L/Ldiluted effluent for photo-fermentation. The hybrid process achieved a total hydrogen yield of w6.7 L/Lwaste. Some of the reported hydrogen yields of dark- and photo-fermentation are listed in Table 4.2, showing the relatively higher yields for hybrid systems. It is worth noting that recent research found that biochar addition can improve the hydrogen yield and shorten lag time of the fermentation processes due to its strong capacities on pH buffering, electro/photon transfer enhancement, and biofilm formation promotion (Lui et al., 2020). This raises the possibility of developing integrated technologies that couple a thermochemical process (for biochar production) with a biochemical process toward higher process and resource efficiencies. Table 4.2 The hydrogen yields of dark- and photo-fermentation are listed in Table 4.2. Technology Feedstock Cumulative hydrogen yield References Dark-fermentation Corn stover Rice straw Corn stover Apple waste Corn stover Corn stover Bagasse 36.08 (mL/g raw material) 19.73 (mL/g raw material) 141.42 (mL/g raw material) 111.85 (mL/g raw material) 90.13 (mL/g raw material)a 439.40 (mL/g raw material)b 162.10 (mL/g raw material) Zhang et al. (2020) Asadi and Zilouei (2017) Zhang et al. (2020) Lu et al. (2016) Zhang et al. (2020) Zhang et al. (2020) Wu et al. (2010) Photo-fermentation Hybrid a Co-fermentation. Two-stage. b
Chapter 4 Waste-to-biohydrogen 3. Downstream processes The hydrogen as generated by the processes mentioned above is commonly present in a gas mixture or being contaminated by other pollutants. Toward practical applications of hydrogen, high-purity is normally required, e.g., >99.9% for high-efficient fuel cell vehicles. In this case, additional procedures of upgrading, separation, and/or purification are needed as an essential part of whole system biohydrogen development and application. 3.1 Syngas cleanup 3.1.1 Contaminants Major contaminants in syngas include tar, nitrogenous compounds, sulfur-containing inorganic compounds, hydrogen halides and halogens, and trace metals. These contaminants are originated from the volatile organic and inorganic compositions of waste and their concentrations are associated with various factors such as the type of waste, process conditions (e.g., temperature and ER), the use of catalyst, and the type of gasifier. The presence of these contaminants in syngas directly affects the process stability and efficiency with the possibility of causing such specific problems as equipment corrosion (by H2S) and fouling (by tar) and catalyst deactivation (by tar, H2S, NH3, HCl, and trace metals) (You et al., 2018). The presence of the contaminants also critically affects the downstream application of syngas and hydrogen as well as the environmental regulation compliance of the development. Improper management of the contaminants can cause various problems such as conversion efficiency reduction, catalyst deactivation, pipeline clogging, and pollutant emission. Tar refers to all the organic compounds (e.g., oxygenates, phenolic compounds, olefins, and aromatic and polyaromatic hydrocarbons (PAH)) with molecular weights greater than benzene according to EU/IEA/US-DOE (Devi et al., 2003). Primary tar components include furfural, acetates, and methoxyphenols, which are decomposed to form secondary tars (e.g., olefins and phenolics), and tertiary tars (e.g., toluene, pyrene, and naphthalene) under elevated temperature and extended processing conditions. The presence of tar risks the operation of a gasification system by blocking and corroding equipment and pipeline, reducing the energy efficiency of the process, and their release to the environment poses such health risks as headache, dizziness, nausea, and even cancer. The tar content in the product gas of gasification ranged from 50 to 500 mg/m3, while the 61
62 Chapter 4 Waste-to-biohydrogen permissible concentrations for internal combustion engines and industrial gas turbines are typically lower than 100 mg/m3 and 1 mg/m3, respectively (You et al., 2018). Ammonia (NH3) is the most abundant nitrogenous compound found in syngas with a concentration ranging between 350 and 18,000 ppmv depending on the nitrogen content in the feedstock (Abdoulmoumine et al., 2015). Around 60e80 wt.% organic nitrogen could be converted to NH3 during gasification. NH3 serves as a NOx precursor and adversely affects the subsequent application of hydrogen, e.g., inhibiting the conversion of H2 and CO into liquid transportation fuels by poisoning the catalysts in Fischere Tropsch synthesis (You et al., 2018). The sulfur composition of waste is mainly converted to hydrogen sulfide (H2S), carbonyl sulfide (COS), carbon disulfide (CS2), etc., with H2S being most significant (its concentration in syngas can be up to 3 vol.%). 3.1.2 Cleanup Syngas cleanup methods can be categorized as cold gas and hot gas ones, respectively. The cold gas method is conducted at or below room temperature and thus reduces the overall system efficiency due to the cooling of syngas and incurs additional logistics and costs for contaminant disposal. Wet cold gas cleanup units (e.g., spray and wash towers, impingement and venturi scrubbers, and wet electrostatic precipitators and cyclones) are based on the principles of absorption, adsorption, and/or filtration. They are suitable for removing multiple contaminants, simultaneously. Water has been commonly used as the absorbent in scrubbers to remove light and oxygenated tar compounds. However, it has the disadvantages of being less effective to remove nonpolar heavy and heterocyclic tar compounds, leading to low removal efficiency and incurring additional costs as further treatment of the wastewater stream is required (Abdoulmoumine et al., 2015). Oil-based scrubbers based on engine oil, diesel fuel, and vegetable oil can achieve a higher efficiency due to its ability to remove heavy and heterocyclic tar compounds and can operate at relatively high temperature to improve the overall process efficiency. For example, for benzene, the absorption efficiencies using engine oil, diesel fuel, and vegetable oil are 61.7%, 77%, and 77.6%, as compared to 24.1% using water (Abdoulmoumine et al., 2015). After absorption, the oil-based absorbents can be recovered using hot gas stripping. However, the oil-based scrubbers are more expensive and complex in terms of process operation.
Chapter 4 Waste-to-biohydrogen Ammonia can be removed using a water scrubber, a flue gas condenser, or spray or wash towers due to its water solubility. The wet-scrubbingebased method facilitates solvent recovery in the form of ammonia salt by precipitation and can achieve an ammonia removal efficiency of over 99%. Similar to the case of tar, water can be used as the liquid absorbent for ammonia and is of a high absorption efficiency. For this process, the product gas needs to be cooled down and the ammonia-dissolved water needs to be treated post the process. Acid-based (e.g., H2SO4) scrubbing has also been tested and has the potential to achieve co-absorption (e.g., absorption of NH3 and H2S). However, technical challenges exist for the corrosiveness of the acids and their low efficiency at higher ammonia concentrations (>500 ppmv) (Abdoulmoumine et al., 2015). Wet cold gas removal is based on chemical solvents (reactions) or physical solvents (absorption) such as amine-based solvents (MDEA), Selexol, aqueous Rectisol, etc., which can achieve a H2S removal efficiency of >98% and reduce the H2S content of exit gas to below 30 ppmv (note Rectisol can make it below 0.1 ppmv but is more expensive). In general, a lower sulfur content is required for chemical and fuel synthesis where catalysts are normally used and can be easily deactivated by sulfur contaminants. Cold gas removal of the sulfur contaminants in syngas adversely affects the energy efficiency of the whole process as syngas cooling is needed for, e.g., amine-based absorption processes at 40 C and refrigerated physical solvent processes at 40 C (Abdoulmoumine et al., 2015). Dry cold gas cleanup units (e.g., cyclones, adsorbing bed, filters, and dry electrostatic precipitators) are based on the principles of mechanical, physical, and electrostatic separation with the utilization of sorbents. Sulfur contaminants can be removed using porous sorbents such as activated carbon and biochar. Specific surface area, pore volume, and surface properties are important factors determining their desulfurization efficiencies. Biochar derived from the pyrolysis of used wood pallets and a mixture of food waste and sludge has been used to remove H2S in syngas, and the biochar activated using steam exhibited greater removal efficiencies due to its large specific surface area, alkaline pH, O-containing groups, and structural defects in graphene-like sheets (Hervy et al., 2018). The reported removal capacity was 65 mgH2S/g for the steam-activated biochar under dry syngas, which could be regenerated at 750 C under a N2 environment. Some of the mineral species (e.g., Ca and Fe) in the biochar serve to promote the removal process with the formation of metal sulfide and metal sulfate species. Additionally, presence of moisture 63
64 Chapter 4 Waste-to-biohydrogen can promote the adsorption efficiency of activated carbon toward enhanced desulfurization and typical removal mechanisms of H2S include dissociative adsorption and oxidation (Köchermann et al., 2015). The hot gas cleanup method is conducted at relatively high temperature (>300 C) and thus mitigates the problem of process efficiency reduction for cold gas cleanup (Abdoulmoumine et al., 2015). It centers around the use of catalysts to promote the reactions of contaminants for their removal. Dolomite (carbonate minerals of Ca and Mg), limonite (FeOx(OH)y), olivine, alkali metal catalysts, acid catalysts, nickel-based catalysts, and zeolite-based catalysts have been tested about their ability for tar removal, and conversion efficiencies between 65% and 100% have been reported in lab-scale and pilot-scale experiments. The tar removal efficiency is closely associated with the surface area, pore diameter, and pore volume of catalysts, as well as the temperature of reaction, with a higher temperature generally favoring tar cracking. Commercial catalysts could be deactivated by carbon fouling (e.g., coke deposition on nickel-based catalysts) and syngas contaminants. As a result, for sustainable and efficient catalyst use, it is important to develop catalysts of high resistance to the deactivation in addition to such favourable properties as low cost, high activity, and ease of regeneration (You et al., 2018). Biochar has been tested as a catalyst to decompose tar and its tar removal efficiencies can be comparable to some of the commercial catalysts. This opens the possibility for developing closeloop process designs for gasification and pyrolysis systems where biochar generated during the process can be reutilized. Ni or Ni promoted with Ce or La, Co or CoeZn mixed oxide, Ru and Fe are some of the most effective catalysts for ammonia decomposition. They can achieve a conversion efficiency of over 95%. The conversion efficiency depends on various conditions such as the concentration of catalysts (loading of Ni) and reaction conditions. However, the catalyst deactivation caused by coking and syngas contaminants (e.g., H2S) and economically viable large-scale application of catalysts are two the major challenges facing the hot gas methods. For example, ammonia can be decomposed to H2 and N2 with the presence of various types of catalysts (e.g., Ni-, Ru-, and Fe-based), but the decomposition process can be adversely affected by sulfur contaminants. Operation conditions of >900 C and 2e3 MPa were recommended to mitigate the sulfur poisoning problem (You et al., 2018). For the hot gas removal of sulfur contaminants, a metal oxide sorbent
Chapter 4 Waste-to-biohydrogen (e.g., CaO, ZnO, and CuO) is commonly used to react with the contaminants with the generation of a metal sulfide (e.g., CaS, ZnS, and CuS). In situ desulfurization based on single metal oxides is economical and simple, but requires that the sorbents should be thermally and mechanically stable under the condition of high temperature and friction (You et al., 2018). For example, ZnO can effectively reduce the concentration of H2S to less than 1 ppmv. However, under a high temperature (>600 C) condition, the reduction to elemental metal and metal evaporation occur (elemental zinc is formed and volatized) during the regeneration process, lowering the sorbent reactivity and H2S removal efficiency (Abdoulmoumine et al., 2015). To mitigate the problem and improve the mechanical strength, structure, and reactivity of metal oxides for sulfur sorption, other metal oxides can be incorporated into the ZnO catalyst to form mixed metal oxides. Moreover, to enhance the adsorptive capacity of the sorbents, high surface area materials (e.g., zeolites, Al2O3 and TiO2) have been used to as the support of metal oxides. For example, novel BaO-based sorbents have been developed to achieve desulfurization of below 1 ppmv and 35 ppmv H2S for fixed-bed and bubbling fluidized bed gasification, respectively (You et al., 2018). 3.2 Upgrading Syngas can be upgraded to increase the content of hydrogen by promoting the wateregas shift reaction, which can be typically achieved in a two-stage process: a stage with high-temperature (350e400 C) using a FeeCr-catalyst followed by lowtemperature (220e300 C), CueZn-catalyst-based shift and a subsequent separation stage (You et al., 2018). To achieve a high conversion efficiency and hydrogen yield, noble metal catalysts such as Pt/CeO2/Al2O3, Pt/CeO2/ZrO2, and Ru/CeO2/Al2O3 have been used. However, their performance can be affected due to the deactivation of the catalysts by sintering and contamination as well as the thermodynamic limit of CO conversion due to the presence of H2. To constantly remove H2 and thus alleviate the thermodynamic limit, membrane reactors can be used for simultaneous the wateregas shift reaction and H2 separation. Pd-based membrane reactors (e.g., PdeAg membrane (Brunetti et al., 2009)) have been used to upgrade syngas for producing pure hydrogen that is suitable for a proton-exchange membrane fuel cell. However, the practical application of Pd-based 65
66 Chapter 4 Waste-to-biohydrogen membrane is limited by its high cost (Pd price over 80 USD/g), and effort has been made to improve its economic feasibility by developing thin or ultra-thin Pd-based membranes. For example, an ultra-thin (3.6 mm) PdeAg membrane for hydrogen production achieved a CO conversion of 96% and hydrogen recovery of 84% (Brunetti et al., 2015). Additionally, the wateregas shift reaction can be catalyzed by thermophilic microorganisms such as Carboxydothermus hydrogenoformans and Rhodospirillum rubrum which convert CO and H2O to H2 and CO2 via the biological wateregas shift reaction under anaerobic conditions. The achieved productivities range from 4.7 to 125 mmol$LRWV1 h1 (RWV: reactor working volume) depending on the design of bioreactors and syngas composition (Asimakopoulos et al., 2018). This biological method is relatively low cost as compared to the chemical methods mentioned above. However, the contaminants and the various gas components (e.g., ammonia) in syngas may pose high toxicity to the microorganisms, reducing the overall hydrogen productivity. 3.3 Separation and purification There are four main types of technologies for separating and purifying syngas for high-purity hydrogen production, i.e., membrane, absorption, adsorption (e.g., pressure swing adsorption (PSA), temperature swing adsorption (TSA), electrical swing adsorption (ESA)), and cryogenic technologies. The membrane and sorption-based technologies facilitate process intensification by allowing the combination of syngas production and separation to shift the equilibrium-limited reactions toward higher hydrogen production (Voldsund et al., 2016). Membrane separation technologies have various advantages such as reduced energy consumption, reduced unit operation, simplicity and continuity of operation, and low cost. They have been extensively tested at lab- and pilot-scale studies. Hydrogen can be separated from mixed gases with a membrane because of the selective permeation of hydrogen as driven by the hydrogen partial pressure in the gas stream (Shahbaz et al., 2020). Hydrogen selective membranes produce a permeate consisting of highpurity hydrogen at lower pressure, and a retentate with impurities or other gas components (e.g., CO, CO2 and CH4) at higher pressure. The process is controlled by three physicochemical mechanisms, i.e., Knudsen diffusion, molecular sieving, and solution diffusion, which are closely related to the relative morphology (density and pore size) of membrane to the gas molecules. For example, if the pores of membrane are larger than that of the
Chapter 4 Waste-to-biohydrogen kinetic diameter of gas molecules, the molecules will all pass through the membrane via convective flow driven by the difference in partial pressures; if the pores are much smaller than the gas mean free path (e.g., for microporous membranes), small molecules will pass through mainly via Knudsen diffusion and large molecules get sieved out via the molecular sieving mechanism. Normally, solution diffusion determines the permeation of molecules through dense membranes: permeants firstly get adsorbed at the upstream side and gradually diffuse downstream through the membrane followed by desorption at the downstream side. A sweep gas such as nitrogen can be used on the other side of the membrane to improve permeation by lowering the partial pressure (Nikolaidis & Poullikkas, 2017). Depending on the material, membranes can be categorized into inorganic (e.g., metallic and ceramic) and organic (e.g., polymer or carbon). Polymeric membranes are mature technologies featured by low costs and being suitable for applications at relatively low temperatures (<100 C) and high pressure drops (Voldsund et al., 2016). The major drawbacks of polymeric membranes include low hydrogen selectivity and permeability, and low mechanical and chemical stability (being vulnerable to swelling, compaction, and contaminants such as HCl and SOx), making high-purity hydrogen production difficult. Metallic, ceramic, and carbon membranes can withstand higher temperatures between 200 and 900 C and have improved hydrogen permeability and selectivity, and thus are suitable for bulk separation of hydrogen after wateregas shift. However, these membranes normally face the challenges of poisoning caused by various gas components such as CO and H2S, high costs, and of limited mechanical stability especially for ultra-thin configurations. Upon the design of membrane technologies, the following parameters are normally considered: gas permeability and selectivity, mechanical strength, temperature, and chemical stability. Hydrogen selectivity and permeability are two critical parameters defining the capacity of membrane technologies with the former determining the purity of hydrogen recovered and the latter determining the productivity of hydrogen. In many cases, a compromise needs to be made between the two parameters based on the consideration of syngas conditions (e.g., compositions and humidity) and the adjustment of operating conditions (i.e., temperature and pressure). Adsorption technologies fix molecules on the surface of adsorbents and utilize molecules’ varied affinities for adsorbents for gas purification. PSA is one of the most used technologies for hydrogen purification with a hydrogen recovery of 60%e95% 67
68 Chapter 4 Waste-to-biohydrogen and a very high purification potential (98e99.9999 hydrogen mol.%) (Voldsund et al., 2016). When the product gas passes through an adsorbent (e.g., silica gel, activated carbon, alumina, and zeolite) column at high pressure, impurities and hydrogen are separated due to their different affinities. Upon saturation, regeneration and desorption are done by lowering the pressure and purging with hydrogen (typically from another column undergoing depressurization). Typically, a PSA process happens at a feed pressure of 20e60 atm and ambient temperature, and the pressure of hydrogen produced is slightly lower with the pressure of off-gas being 1.1e1.7 atm (Voldsund et al., 2016). With a CO2 selective adsorbent, PSA can be used to capture CO2 generated from gasification. It is worth noting that it is more difficult for PSA to separate hydrogen from oxygen and nitrogen as compared to from CO, CO2, and CH4, and to achieve satisfactory separation, increasing adsorbent volume or adding additional processes might be required. The absorption-based gas separation utilizes a liquid solvente containing scrubber column to absorb the impurities in the gas mixture. The solvent from the scrubber column is then heated, depressurized, and go through a regeneration column where absorbed components are separated from the solvent. Chemical solvents used for absorption include monoethanolamine (MEA), triethanolamine (TEA), and methyldiethanolamine (MDEA) which are featured by high reaction rates and high absorption capacities, but significant heat requirements for regeneration. On contrary, physical solvents (e.g., Rectisol and Selexol) have lower heat requirements and their regeneration can be achieved using reduced pressure and/or increased temperature. But their absorption capacity is normally lower than that of chemical solvents at the condition of low CO2 partial pressure, making them a more desirable option for high partial pressure conditions (Voldsund et al., 2016). Elevated-temperature pressure swing adsorption (ET-PSA) has been proposed to reduce the energy consumption of hydrogen purification (Fig. 4.3). It combines the use of wateregas shift catalysts and elevated-temperature (200e450 C) CO2 adsorbents and can achieve CO catalytic conversion, in situ CO2 adsorption, and CO purification in a single unit, simultaneously (Zhu et al., 2019). The elevated temperature avoids the need of precooling and reheating syngas, saving the energy consumption upon heat regeneration. Generally, ET-PSA can achieve a better compromise between hydrogen purity and recovery ratio as compared to
Chapter 4 Waste-to-biohydrogen Figure 4.3 Hydrogen upgrading and purification processes (Zhu et al., 2019). conventional PSA. A recent study proposed an ET-PSA process that achieved a hydrogen purity of 99.999% and a hydrogen recovery ratio over 95% (Zhu et al., 2018). For an adsorption cycle of TSA, a hot medium stream such as hot water or steam is used to heat the adsorption reactor followed by the use of a cooling medium stream such as water to cool the reactor (Jiang et al., 2018). The thermodynamic cycle of TSA can be described in four steps, i.e., adsorption, preheating, desorption, and precooling. In the adsorption step, gas components (e.g., CO2) are adsorbed with the adsorption heat carried away by a cooling medium (Point 1 / 2 in Fig. 4.4). In the preheating step, the adsorption reactor is heated without gas (CO2) desorption by the heating medium stream (Point 2 / 3 in Fig. 4.4). In the desorption step, the temperature of the reactor is increased followed by N2 purging. The gas will start being desorbed when the adsorption capacity exceeds the maximum value, and flow out of the reactor until the temperature reaches T4 (Point 3 / 4 in Fig. 4.4). In the precooling step, the cooling medium stream is used to cool down the reactor until the temperature of the reactor reaches T1 for initiating a new TSA cycle. Generally, PSA gains greater popularity than TSA due to various benefits such as reduced energy requirements, simple operation, and low capital costs. Moreover, for TSA, there are technical limitations and challenges about heating and cooling adsorbent materials which have high porosity and low thermal conductivity and require very high heating and cooling rates and long cycle time to achieve sufficient heat diffusion (Pahinkar et al., 2017). To reduce the long time for the desorption cycle, the contact area between the adsorbents and hot gas needs to be 69
70 Chapter 4 Waste-to-biohydrogen Figure 4.4 Schematic diagram for four-step TSA cycle: (A) process schematic; (B) adsorption isotherm diagram (Jiang et al., 2018). maximized (Bonnissel et al., 2001). However, TSA has such advantages as reduced compressor loads and the ability to utilize lowgrade heat, which suggests its potential to be integrated with waste heat recovery processes. ESA regenerates its adsorbent by increasing temperature based on Joule effect (electricity conversion to heat). This means that ESA is still applicable even though waste heat that is normally required by TSA is not available (Ribeiro et al., 2013). It also has the benefits of reduced heating time (higher heating efficiency) and increased process productivity as compared to TSA.
Chapter 4 Waste-to-biohydrogen Bonnissel proposed a rapid thermal swing adsorption process based on the development of a composite adsorbent bed with activated carbon particles and a highly conductive graphite material and on the use of thermoelectric devices for heating and cooling the bed. The high conductivity of the bed and the fast dynamics of the thermoelectric elements led to fast cycles run in 10e20 min (Bonnissel et al., 2001). Meanwhile, the consumption of electricity for the process suggests the importance of selecting proper electricity sources for improving the economic feasibility and environmental impact of the development. A typical adsorbent used for the ESA process is activated carbon which has good electrical conductivity but low adsorption capacity at low CO2 partial pressure. Other materials such as zeolites as thermally stable crystalline materials have poor conductivity and need to be embedded in a conductive matrix for Joule effecte derived heating (Ribeiro et al., 2013). Cryogenic/low-temperature separation utilizes the differences in boiling point of gas components in a mixture to achieve gas separation and requires the cooling of the gas mixture to cryogenic temperatures (<150 C) for separating hydrogen. It can be applied to achieve moderately pure hydrogen from syngas, and to recover hydrogen from the gas streams vented from hydrocracking or hydrotreating operations (Voldsund et al., 2016). The high refrigeration demand and thus high costs limit its extensive application for generating high-purity hydrogen. Finally, Fig. 4.5 gives an example of a typical process of gasification-based WtH generation. Poststream the fluidized bed reactor, there are arrangements of various cleanup, upgrading, and separation and purification units. Specifically, the cyclone is Figure 4.5 Schematic diagrams of gasification-based WtH processes (Lui et al., 2020). 71
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Chapter 4 Waste-to-biohydrogen Voldsund, M., Jordal, K., & Anantharaman, R. (2016). Hydrogen production with CO2 capture. International Journal of Hydrogen Energy, 41(9), 4969e4992. https://doi.org/10.1016/j.ijhydene.2016.01.009 Watson, J., Si, B., Li, H., Liu, Z., & Zhang, Y. (2017). Influence of catalysts on hydrogen production from wastewater generated from the HTL of human feces via catalytic hydrothermal gasification. International Journal of Hydrogen Energy, 42(32), 20503e20511. Wong, Y. M., Wu, T. Y., & Juan, J. C. (2014). A review of sustainable hydrogen production using seed sludge via dark fermentation. Renewable and Sustainable Energy Reviews, 34, 471e482. Wu, X., Li, Q., Dieudonne, M., Cong, Y., Zhou, J., & Long, M. (2010). Enhanced H2 gas production from bagasse using adhE inactivated Klebsiella oxytoca HP1 by sequential dark-photo fermentations. Bioresource Technology, 101(24), 9605e9611. https://doi.org/10.1016/j.biortech.2010.07.095 Yang, H., Guo, L., & Liu, F. (2010). Enhanced bio-hydrogen production from corncob by a two-step process: Dark-and photo-fermentation. Bioresource Technology, 101(6), 2049e2052. Yang, H., Shi, B., Ma, H., & Guo, L. (2015). Enhanced hydrogen production from cornstalk by dark-and photo-fermentation with diluted alkali-cellulase twostep hydrolysis. International Journal of Hydrogen Energy, 40(36), 12193e12200. You, S., Ok, Y. S., Tsang, D. C. W., Kwon, E. E., & Wang, C.-H. (2018). Towards practical application of gasification: A critical review from syngas and biochar perspectives. Critical Reviews in Environmental Science and Technology, 48(22e24), 1165e1213. https://doi.org/10.1080/ 10643389.2018.1518860 You, S., Tong, H., Armin-Hoiland, J., Tong, Y. W., & Wang, C.-H. (2017). Technoeconomic and greenhouse gas savings assessment of decentralized biomass gasification for electrifying the rural areas of Indonesia. Applied Energy, 208, 495e510. https://doi.org/10.1016/j.apenergy.2017.10.001 You, S., Wang, W., Dai, Y., Tong, Y. W., & Wang, C.-H. (2016). Comparison of the co-gasification of sewage sludge and food wastes and cost-benefit analysis of gasification- and incineration-based waste treatment schemes. Bioresource Technology, 218. https://doi.org/10.1016/j.biortech.2016.07.017 Zhang, T., Jiang, D., Zhang, H., Jing, Y., Tahir, N., Zhang, Y., & Zhang, Q. (2020). Comparative study on bio-hydrogen production from corn stover: Photofermentation, dark-fermentation and dark-photo co-fermentation. International Journal of Hydrogen Energy, 45(6), 3807e3814. https://doi.org/ 10.1016/j.ijhydene.2019.04.170 Zhang, Q., Lu, C., Lee, D.-J., Lee, Y.-J., Zhang, Z., Zhou, X., Hu, J., Wang, Y., Jiang, D., & He, C. (2017). Photo-fermentative hydrogen production in a 4 m3 baffled reactor: Effects of hydraulic retention time. Bioresource Technology, 239, 533e537. Zhu, X., Li, S., Shi, Y., & Cai, N. (2019). Recent advances in elevated-temperature pressure swing adsorption for carbon capture and hydrogen production. Progress in Energy and Combustion Science, 75, 100784. https://doi.org/ 10.1016/j.pecs.2019.100784 Zhu, X., Shi, Y., Li, S., & Cai, N. (2018). Two-train elevated-temperature pressure swing adsorption for high-purity hydrogen production. Applied Energy, 229, 1061e1071. https://doi.org/10.1016/j.apenergy.2018.08.093 75
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Waste-to-biomethane 5 Abstract This chapter introduces waste-to-biomethane production via the technology of anaerobic digestion. It starts by explaining the influences of four main process parameters (i.e., feedstock, temperature, pH, and retention time) toward biogas production. It summarizes the development of biogas cleanup and upgrading methods. The contamination of biogas is reviewed with a focus on H2S, siloxanes, and halogen. The formation mechanisms and cleanup methods of the contaminants are discussed. Finally, it explains the technical principles, advantages, and disadvantages of five main types of biogas upgrading technologies including pressurized water scrubbing, chemical absorption, membrane separation, pressure swing adsorption, and others. Keywords: Biogas; Biogas contamination; Cleanup and upgrading; Organic waste; Process parameters; Waste-to-biomethane. 1. Introduction AD is one of the most widely used methods for deriving biogas from organic waste such as food waste, manure, crops, sludge, and crop residues. Instead of being used for energy production, biogas, after cleanup, can be upgraded to generate high-purity (>97%) methane (biomethane) to replace fossil fuels for further energy and chemical applications such as displacing natural gas in the grid and powering the transport sector. The AD-based biogas production has been introduced in Chapter 2. This chapter will focus on the improvement of biogas yields based on the understanding of relevant influential factors, and cleanup and upgrading processes that turn biogas into high-purity methane. 2. Biogas production The yields and compositions of biogas vary considerably depending on various AD process parameters and conditions such as the type of feedstock, temperature, pH, the content of total solid, HRT, etc. Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00007-4 Copyright © 2022 Elsevier Inc. All rights reserved. 77
78 Chapter 5 Waste-to-biomethane 2.1 Feedstock A wide range of organic waste has been tested about their suitability for AD and to name just a few, animal manure, sewage sludge, municipal solid waste, food waste, crops, and crop residues. Although there is limited freedom in the selection of feedstock in some cases especially when the disposal of waste is the goal, the careful selection of an appropriate feedstock is critical for the technoeconomic feasibility of AD development. Accordingly, it is important to understand how the physicochemical properties of feedstock affect biogas yields and the underlying mechanisms. The chemical composition of feedstock is associated with the survival and growth of AD microorganisms and can be categorized into macronutrients (carbon, nitrogen, phosphorus, and sulfur) and micronutrients (iron, cobalt, nickel, zinc, selenium, tungsten, magnesium, chromium, and molybdenum), respectively, based on their roles (Mirmohamadsadeghi et al., 2019). Specifically, carbon is important for the build-up of cell structure and serves as the source for energy generation. Nitrogen is an important building block for proteins. Phosphorus is key for trapping and transferring energy in cellular activities of microorganisms. Sulfur is important for the growth of methanogens and is used in the formation of some amino acids (Choong et al., 2018). A good balance between carbon and nitrogen contents favors the AD process and a desirable carbon to nitrogen ratio was suggested to range between 20 and 30. The micronutrients are also involved in various activities (e.g., co-precipitation, enzymatic activity, and biochemical reactions) related to the growth and survival of AD microorganisms. For example, iron serves as a growth factor and a stimulating agent for the formation of ferredoxins and cytochromes which are essential in cell metabolism (Mirmohamadsadeghi et al., 2019). It can also precipitate sulfur as iron (II) sulfide, reducing the corrosion effects of H2S. Cobalt is a growth factor of acetogenic microorganisms and serves to stabilize the AD processes with higher organic loadings. The size of feedstock particles affects the degradation efficiency, stability, and VFA accumulation of AD. Generally, fine feedstock particles provide larger specific surface areas for the initial adsorption of exo-enzymes, which serve to improve the degradation rate and thus biogas production (Mirmohamadsadeghi et al., 2019). It was shown that a properly reduced food waste particle size (0.6e0.7 mm) improved the methane yield by 28%, but smaller food waste particles (w0.4 mm) led to VFA accumulation and reduced solubilization, thus reducing the methane production
Chapter 5 Waste-to-biomethane (Izumi et al., 2010). Upon the AD of organic fraction of MSW (OFMSW), smaller particle sizes caused acidification and ultimately led to a process failure at an OLR of 6 kg volatile solids (VS)/m3/day in a “dry” digester. Severe foaming was formed, failing the process for the cases of OLR greater than 5 kg VS/m3/day for small particles in a “wet” digester (Zhang & Banks, 2013). Batch experiments of the AD of rice straw showed that the methane yield increased from 107 mL/g VS to 197 mL/g VS when the particle size reduced from 20 to 0.075 mm due to an enhanced cellulose degradation rate from 27% to 93% (Dai et al., 2019). The basic morphology, dissolution abilities, and bio-liquefaction degree could be improved by a particle comminution pretreatment. Some compositions of feedstock are direct toxicants to AD microorganisms or indirectly lead to the formation of AD inhibitory compounds. For example, the metallic micronutrients and heavy metal compositions at their high concentrations (e.g., Na and K for food waste) inhibit the AD process by disrupting the function and structure of enzymes (Mirmohamadsadeghi et al., 2019). Feedstocks (e.g., food waste) with high lipid contents tend to accumulate long-chain fatty acids (LCFAs) such as oleic acid, palmitoleic acid, and linoleic acid which can compromise the cell transport system of AD microorganisms upon their adsorption on the membrane and cell walls, reducing the methane production. To mitigate the problem caused by the accumulation of LCFAs, various approaches such as adding active inoculum and co-substrate, LCFA adsorption, and discontinuous feeding can be applied (Mirmohamadsadeghi et al., 2019). VFAs and ammonia at high concentrations also inhibit and affect the stability of AD processes. For the excessive accumulation of VFAs caused by high organic loads during kitchen waste AD, it was shown that acetic acid was the main inhibitor in methanogenesis, and aceticlastic methanogenesis was more significantly affected by acetic acid than hydrogenotrophic methanogenesis (Xu et al., 2014). VFA inhibition can be mitigated by the addition of trace elements, adjustment of bioreactor pH, and bioaugmentation (i.e., the addition of functional microorganism to enhance the metabolism of methanogens) which can serve to improve the growth environment and the acid resistance of AD microorganisms (Zhao et al., 2020). Ammonia as produced via the degradation of nitrogenous matters at a proper amount is beneficial for mitigating the inhibition caused by VFAs by promoting the buffering capacity of AD. However, excessive amount of free ammonia (NH3) can inhibit the process by diffusing through the cells and disrupting the Hþ and K balance and the cell function of microorganisms (Mirmohamadsadeghi et al., 2019). It was shown that high-rate digesters 79
80 Chapter 5 Waste-to-biomethane generally experienced operational failure for total ammonia nitrogen (free ammonia nitrogen þ ammonium nitrogen) concentrations of around 1700e1800 mg/L and that AD was inhibited by a €n & free ammonia nitrogen concentration of 150 mg/L (Yenigu Demirel, 2013). It is worth noting that these numbers also depend on other process conditions such as the type of substrate, digester designs, pH, loading rate, etc. To mitigate the issues related to nutrient imbalance and/or toxicant inhibition, the method of anaerobic co-digestion based on the utilization of two or more substrates has been proposed. For example, the co-digestion of food waste and straw achieved a methane production yield of 0.392 m3/kg-VS which was 39.5% and 149.7% higher than mono-digestion of the individual feedstocks, respectively (Yong et al., 2015). The co-digestion experiments also achieved a biogas production and methane content of 0.58 m3/kg-VS and 67.62%, respectively. The co-digestion of sewage sludge and glycerol from the biodiesel industry at 35 C achieved a methane yield of 2353 94 mL/day as compared to 1106 36 mL/ d for the mono-digestion of sewage sludge (Fountoulakis et al., 2010). The added glycerol increased the active VS concentration in the reactor and the glycerol addition of a concentration <1 vol.% in the feed boosted biogas yields. 2.2 Temperature Temperature critically affects the activities of methanogens to different degrees for thermophilic (55e70 C) and mesophilic (32e45 C) AD. The higher operating temperature for thermophilic AD generally promotes microorganism growth, organic content degradation, and biogas production, and reduces the solubility of oxygen and ammonia-induced process inhibition. However, the temperature needs to be relatively stable as thermophilic methanogens are sensitive to temperature variations (Mirmohamadsadeghi et al., 2019). The greater diversity of methanogens is generally favored by mesophilic conditions, leading to higher tolerance to temperature and other environmental changes and thus a higher level of process stability. A series of batch AD experiments treating pig manure showed that the metabolic activities increased considerably as the temperature increased from 15 to 35 C, while the compositions and metabolic pathways of microbial communities remained similar (Tian et al., 2018). A greater microbial diversity associated with hydrolysis, acidogenesis, and acetogenesis was achieved at 45 C and their population decreased when the temperature increased further to 55 C, suggesting 45 C might be a turning
Chapter 5 Waste-to-biomethane point for the microbial community composition and the metabolic pathway between mesophilic and thermophilic AD. Food waste AD experiments showed that the solubilization rate of food waste was 47.5%, 62.2%, 70.0%, 72.7%, 56.1%, and 45.9% at 15 C, 25 C, 35 C, 45 C, 55 C, and 65 C, respectively, suggesting that the overall solubilization rate was significantly lower under a thermophilic condition (Komemoto et al., 2009). The biogas production was 64.7 and 62.7 mL/g-VS under the mesophilic conditions of 35 C and 45 C, respectively, which were higher than that under the thermophilic conditions, though a shorter HRT was achieved for thermophilic AD. 2.3 pH Low pH conditions caused by the generation of acidic intermediates are one of the main reasons for the failure of AD processes due to the inhibitory effect on methanogens. It was suggested that an AD system needs to have sufficient alkalinity (2500e5000 mg/L) to buffer the production of VFAs (Choong et al., 2018). However, the excessive use of buffering agents such as NaOH also poses an inhibitory risk to methanogens and high pH conditions favors the formation of free ammonia, leading to ammonia-induced inhibition (Ali et al., 2019). A pH value between 6.8 and 7.4 was reported to be ideal for methanogens to survive, and different pH conditions favored the growth of different AD bacteria (e.g., pH ¼ 6 favoring Clostridium butyricum, while pH ¼ 8 favoring Propionibacterium species) (Mao et al., 2015). Methanogenic and acidogenic microorganisms corresponding to the different steps of an AD process differ regarding their optimal pH conditions with methanogenesis and acidogenesis being most efficient at pH ¼ 6.5e8.2 and pH ¼ 5.5e6.5, respectively. However, the acclimatization and production of methane at low pH conditions have been considered as a potential method for maintaining or even increasing the methane and overall biogas yields. It was shown that aceticlastic methanogens were acclimatized to low pH down to 3.5 and accounted for 96.3% of the total methanogenic population at pH ¼ 4.5 and 86.75% at pH ¼ 3.5 (Ali et al., 2019). The methane yield at pH ¼ 4.5 after acclimatization was found to be similar to that at the neutral pH condition. 2.4 Retention time Retention time is the time it takes for a given amount of organic matter to decompose in an AD reactor or system. It affects the microbial population, metabolic pathway, and 81
82 Chapter 5 Waste-to-biomethane production of intermediates and end-products and is affected by process temperature, OLR, and substrate composition (Choong et al., 2018; Mao et al., 2015). Two different types of retention time have been defined: SRT being the average time that bacteria (solids) spend in a digester, and HRT being the volume of the reactor divided by the influent flow rate. An insufficient HRT causes such issues as reduced system efficiencies and methane yields, and increased process instability. Generally, a shorter HRT leads to the accumulation of VFAs and a long HRT and low OLR are needed to maximize the yield of methane. AD experiments based on primary sludge and a starch-rich industrial wastewater showed that soluble COD and VFA concentrations were highest at HRT ¼ 30 h and 25 C and decreased for a further increase in HRT (Maharaj & Elefsiniotis, 2001). The effects of HRT on methane production were studied based on a CSTR-based two-stage mesophilic AD system fed with olive mill wastewater, cheese whey, and liquid cow manure (Dareioti & Kornaros, 2014). It was found that the highest system efficiency was achieved at HRT ¼ 0.75 day and the methanogenic reactor had a better stability at HRT ¼ 25 days as compared to 20 days with a methane production rate of 0.33 L CH4/LR day. It was also shown that increasing SRT from 5 to 35 days was significantly related to the decreases in the biogas and methane production and VFAs concentrations (Mao et al., 2015). 3. Biogas cleanup and upgrading 3.1 Contamination In addition to the major gas components (i.e., CH4 and CO2), there often exist certain amount of contaminants in biogas depending on the process conditions such as temperature, pressure and the types of feedstocks. Typical contaminants include H2S, hydrocarbons (e.g., benzene, toluene, etc.), halogens, siloxanes, and halocarbons (Table 5.1). Toward the generation and application (e.g., in solid oxide fuel cells (SOFCs)) of highpurity methane or methane-rich gas, it is critical to efficiently remove CO2 and the contaminants. In particular, the contaminants risk the stable operation of methane applications and reduce overall process efficiencies. Some of the contaminant removal techniques are similar to what have been introduced in Chapter 4. This chapter will focus on the information specific to biogas.
Chapter 5 Waste-to-biomethane 83 Table 5.1 Concentrations (ppm) of typical biogas contaminants (Lanzini et al., 2017). H2S Siloxanes Halogens Halogens (HF, HBr, Hydrocarbons (D4, D5, etc.) (HCl) and others) Halocarbons (benzene, toluene, etc.) 121 24e63 1.8e104 80e130 0.24e2.3 0.1e0.7 0.6e1 Up to 2.9 0.2e1.4 0.2e0.8 n.a. n.a. 1 n.a. n.a. n.a. 0.16 n.a. <0.1 w1 3.1.1 H2S H2S in biogas is generated upon the degradation of sulfurcontaining organic compounds (e.g., proteins) and is most relevant to the AD processing of manure, sewage sludge, and diary streams. It is the most significant biogas contaminant and has a concentration range between 300 and 6000 ppm (or 0.1%e5%) (Lanzini et al., 2017; Wu et al., 2020). Direct use of biogas without desulfurization can cause the problem of corrosiveness to pipes and devices. In situ sulfur abatement can be achieved by injecting air or oxygen into the digester to promote the activity of sulfideoxidizing bacteria (SOB) that turn H2S to elemental sulfur (S0) (Lanzini et al., 2017). H2S can also be removed via the chemical absorption of sulfur in aqueous solutions (e.g., iron-chelated solutions) which converts H2S into S0 by means of sulfide oxidation. Iron oxide adsorbents can be used for sulfur removal but suffer from the problems of high cost and difficulty in the disposal of spent material. One of the major types is iron sponge (unit cost w0.25 USD/kg) made of iron oxide or hydroxide coated onto a supporting material such as wood shavings or wood chips (Lanzini et al., 2017). It has a theoretical removal efficiency of 0.64 kg H2S/kg Fe2O3. The desulfurization process can be operated either in a batch or continuous mode with latter being preferred for a higher removal efficiency: the continuous mode co-fed with air (oxygen) achieved a removal efficiency of 2.5 kg H2S/kg Fe2O3. SulfaTreat, Sulfur-Rite, and Media-G2 are three main types of commercial iron oxide adsorbents with SulfaTreat being the most widely used for H2S removal. SulfaTreat is a mixture of iron oxides (Fe2O3 and Fe3O4) and an activator oxide 1.6 0.7e3 0.4e1.7 n.a.
84 Chapter 5 Waste-to-biomethane (0.125e5 wt.% of the adsorbent) that is made of one or more oxides of several metals such as platinum, gold, silver, copper, cadmium, nickel, palladium, lead, etc., and serves to enhance adsorption (Lanzini et al., 2017). SulfaTreat was reported to achieve a removal efficiency of 0.11 g H2S/g SulfaTreat (Truong & Abatzoglou, 2005). Activated carbon has also been used to remove H2S in biogas. Some major factors affecting the activated carbonebased H2S adsorption include the water content, surface pH, and micropore volume of activated carbon. For example, preadsorbed water on activated carbon can enhance the adsorption capacity by promoting the dissociation of H2S followed by further oxidation to form sulfur. However, water in the gas mixture would deactivate catalytic centers and impede the adsorption process. For reactive adsorption, the micropores act as microreactors to retain chemisorbed oxygen for the oxidation and to store elemental sulfur (Seredych & Bandosz, 2006). To improve the chemical sorption of H2S, impregnated activated carbon can be developed by treating raw activated carbon with a solid or liquid compound such as sodium bicarbonate (NaHCO3), potassium hydroxide (KOH), sodium carbonate (Na2CO3), sodium hydroxide (NaOH), etc. They can achieve a sulfur removal capacity of 30 wt.% (a few hundreds of mg per g of activated carbon) with the presence of oxygen to promote H2S oxidation to elemental sulfur (S0), which is around one order of magnitude higher than that of nonimpregnated activated carbon (Lanzini et al., 2017). However, the cost of impregnated activated carbons can be 3e4 times higher than that of raw activated carbon. Besides the common physical/chemical desulfurization technologies based on the principles of absorption, adsorption, and membrane separation (as mentioned above and in Chapter 4), biodesulfurization emerges as a promising method because of its various advantages such as low energy consumption, mild operation conditions, reduced secondary pollution, high H2S removal efficiency, and being chemical catalysts free (Wu et al., 2020). Biodesulfurization relies on SOB to remove H2S in biogas via microbial metabolism. Biotrickling filters (BTF, Fig. 5.1) packed with inorganic or manufactured media use a trickling liquid phase to control pH, salt or the metabolite concentration, and the nutrients of process culture (Vikromvarasiri et al., 2017). They have the benefits of reduced environmental impacts and increased cost-effectiveness. During the biodesulfurization process, chemotrophic bacteria (e.g., Halothiobacillus neapolitanus) utilize inorganic carbon (e.g., carbon dioxide) as a carbon source and the oxidation of H2S to get
Chapter 5 Waste-to-biomethane Figure 5.1 A schematic diagram of BTF (Wu et al., 2020). chemical energy. The process can be conducted under either anoxic (anoxic BTF) or aerobic (aerobic BTF) conditions. For the former, NO 3 and NO2 can be used as the electron acceptor, but it is relatively difficult to control and expensive to remove high concentrations of H2S due to the need of nitrate (Wu et al., 2020). For the latter, oxygen (air or pure oxygen) can be used as the electron acceptor and high concentrations (1000e10,000 ppmv) of H2S can be effectively removed. H. neapolitanus isolated from activated sludge has been tested for degrading H2S to elemental sulfur or sulfate in a biotrickling filter and a maximum removal capacity of 78.57 g H2S/m3h was achieved (Vikromvarasiri et al., 2017). Major influential factors of biodesulfurization include gas flow rate, empty bed residence time (EBRT), inlet H2S concentration, and liquid recirculation rate. EBRT played a greater role in determining the removal efficiency than the other factors. A longer EBRT results in a higher removal efficiency, while the liquid recirculation rate played a minor role in affecting the removal efficiency (Vikromvarasiri et al., 2017). A short EBRT incurs a reduced contact time between bacteria and substrates and thus limited mass transfer of H2S to the liquid phase. This leads to incomplete degradation of H2S and thus a reduced 85
86 Chapter 5 Waste-to-biomethane removal efficiency (Wu et al., 2020). High inlet loading of H2S also poses a toxic risk of inhibiting the activity of the SOB. However, a longer EBRT means a lower inlet loading of H2S (for a lower concentration of H2S) and a larger volume of BTF (a higher cost of the desulfurization process). Increasing the inlet loading of H2S decreases the EBRT and the associated H2S removal efficiency. For example, as EBRT decreased from 160 to 40 s, the H2S removal efficiency decreased from 81.8% to 67.1% for a H2S concentration of 3862 ppmv (Chaiprapat et al., 2011). As the inlet loading increased from 84 g H2S/m3/h to 334 g H2S/m3/h corresponding to the decrease of EBRT from 120 to 30 s, the H2S removal efficiency decreased from 97.7% to 39.7%, which was mainly attributed to the limited mass transfer (Fortuny et al., 2011). High inlet loading and H2S concentrations are conditions favoring higher process efficiencies. To mitigate the potential issue of low removal efficiency due to the limited mass transfer of H2S, it is important to increase the contact between biofilm and substrates, which can be achieved using packing materials (e.g., polypropylene) with high specific surface areas, porosity, hydrophilicity, and water retention capacities (Wu et al., 2020). Additionally, increasing the alkalinity of the medium can enhance the mass transfer of H2S to the liquid phase as of the reaction H2S þ OH/H2O þ HS. An acid environment is harmful to some SOB, and experiments have shown that the removal efficiency of H2S decreased as the decrease of the pH value of recirculation liquid (Jin et al., 2005). An alkaline environment (pH ¼ 7e8.5) might serve to mitigate the acidity-associated toxic effect and promote the activity of bacteria. 3.1.2 Siloxanes Siloxanes, also known as volatile methyl siloxanes (VMS), are a group of polymeric compounds of silicon and oxygen bonds with organic chains attached to the silicon atoms. Siloxanes are normally derived not only from the AD treatment of sewage sludge containing silicone-based compounds such as personal care products, adhesives and coatings, cosmetics, etc., but also from the addition of antifoaming agents to AD. Most common siloxanes in biogas are cyclic volatile polydimethylsiloxanes (D4 (octamethylcyclotetrasiloxane) and D5 (decamethylcyclopentasiloxane)) which can account for 90% of the overall silicon content of biogas (Lanzini et al., 2017). The presence of siloxanes in biogas is particularly harmful for the energy application of biogas because silicates (SiO2) and microcrystalline can be formed during the combustion of siloxanescontaining biogas which can potentially damage engine parts
Chapter 5 Waste-to-biomethane upon their deposition which adversely affects heat conduction and lubrication (Santos-Clotas et al., 2019). Even a small amount of siloxanes in biogas is highly detrimental to SOFCs. For example, the presence of 1 ppm D4 siloxane was found to degrade SOFC with WeNieCe as anode by the formation of silica in the anode (Escudero & Serrano, 2019). Siloxanes decomposed very quickly to SiO2 and the vapor phase Si in the form of Si(OH)4 or SiO further diffused to the anode/electrolyte region and ultimately precipitated. It was shown that D4 significantly reduced the performance of nickel-based anode supported SOFCs even at ultra-low levels of 78e178 ppb(v) due to the formation and deposition of silicon dioxide onto the anode porous sites (Papurello & Lanzini, 2018). It reduced the anode porosity and fuel flow channels for supplying the fuel to active sites. A two-step chemical reaction mechanism has been proposed for D5 siloxane deposition: (1) D5 reacts with water and the methyl groups are converted to carbon monoxide and hydrogen, and (2) the remaining orthosilicic acid gas travels through the anode and decomposed into silicon dioxide and water, leading to the accumulation of Si in the anode and thus a higher anode polarization and ohmic loss (Haga et al., 2008). Solid sorbents such as activated carbon, zeolite, and silica gel can be used to remove siloxanes. The reported adsorption capacity varies considerably depending on the experimental conditions. For example, wood-based chemically activated carbon was shown to have an adsorption capacity of 1732 93 mg/g for 1000 ppm (v/v) of D4 with dry N2 as the carrier gas, but the capacity would be halved when the concentration of D4 was lowered to a typical value (1.45 ppm) found in biogas (Cabrera-Codony et al., 2014). Moreover, other gas components such as CH4 and CO2 as well as humidity served to reduce the adsorption capacity of activated carbon. In another study, the D4 adsorption capacity of commercial activated carbon with N2 as the carrier gas was in the range of 5.6e19.2 mg/g, and the adsorption capacity was positively related to the BrunauereEmmetteTeller surface area, pore volume, and pH value of activated carbon (Matsui & Imamura, 2010). It is worth noting that the presence of relatively nonvolatile, organic sulfur or halogenated compounds in biogas adversely affected the adsorption capacity of activated carbon toward siloxanes (Lanzini et al., 2017). 3.1.3 Halogen Typical halogen trace contaminants in biogas are chlorinebased including HCl and CH3Cl. Upon biogas SOFC applications, halocarbons decompose quickly into halogens which can get adsorbed onto electrocatalyst surfaces and affect cell operation 87
88 Chapter 5 Waste-to-biomethane in different ways depending on the temperature: (1) at 650 C, the adsorbed halogens occupy active sites, reducing the catalytic activity and preventing fuel dissociation; (2) at higher temperatures such as 700 C, chlorine reacts with the nickel catalyst to form nickel chloride which is sublimed and degrades the performance of SOFC operation (Reeping et al., 2017). HCl is considered as one of the most detrimental compounds on SOFCs performance in biogas by particularly limiting the electrochemical processes at electrodes of SOFCs (Papurello & Lanzini, 2018). Experiments based on nickel-based anode-supported SOFCs showed that the threshold concentration limit is 40 ppm(v) for SOFC applications, which makes the biogas generated from common waste (e.g., sludge) a less concern for SOFC applications as the halogen content is generally below 1 ppm (Lanzini et al., 2017). 3.2 Upgrading To generate high-purity biomethane toward its practical application in pipelines and for SOFCs, raw biogas needs to be upgraded to remove the impurities and achieve a higher methane content. The methane purity needs to be typically higher than 97% for injecting into natural gas grid and powering vehicles (Yousef et al., 2019). Like the case of biohydrogen, the cleanup technologies of biogas can be classified as “dry” and “wet” ones, and are based on the principles of absorption, adsorption, cryogenic, or membrane technologies. This chapter will focus on biogas with four most common technologies, i.e., pressurized water scrubbing (PWS), chemical absorption, membrane separation, and pressure swing adsorption (PSW). The basic performances of the technologies are compared in Table 5.2. 3.2.1 Pressurized water scrubbing PWS separates CH4 from CO2 and some other contaminants such as H2S based on their solubility differences in water. The solubility of NH3, H2S, CO2, and CH4 in water at 25 C and 0.1 MPa partial pressure is 280,000, 1020, 340, and 13.2 mmol/ kg/MPa, respectively (Abdeen et al., 2016). The solubility of gas upon its absorption is dependent on the solvent physical and chemical properties, and the physical absorption process is usually operated at high pressure and low temperature to increase the solubility. The use of water offers the benefits of low cost and environmentally friendliness. However, the method of water scrubbing might lead to a higher loss of methane. Based on a comparison of methane loss among a water scrubber, amine, and membrane-based biogas upgrading plant, the water scrubbing
Chapter 5 Waste-to-biomethane Table 5.2 The comparison of the performance of four common biogas cleanup and upgrading technologies. Capacity Technology (m3/h) Power input (kWh/m3) Methane purity (%) Methane loss (%) PWS 0.25 0.27 0.29 97.0 98.7 94.2 0.5 1.0 0.77 0.68 0.87 e e 0.27 0.24 0.2e0.3 99.0 98.7 97.5 98.0 98.0 98.0 98.0 0.1 0.03 0.5 1.97 0.5 9.71 1.8e2.0 Chemical absorption Membrane separation PSA 120 500 5 1800 500 300 1000 e 500 e technology had a methane loss up to 1.97%, much higher than the membrane technology (0.56%) and the amine-based upgrading technology (0.04%) (Kvist & Aryal, 2019). High-pressure water scrubbing (HPWS) is featured by its simple operation (Wang et al., 2020). To improve the CO2 solubility in water, a large amount of water needs to be circulated for HPWS, leading to a large system size. Fig. 5.2 shows the scrubber-flashstripper high pressure water scrubbing (SFS-HPWS) technology. The CO2 in biogas is absorbed in a packed column high pressure scrubber at an operating pressure condition between 0.8 and 1.2 MPa that serves to increase the solubility of CO2 in water and to achieve high-purity biomethane. The scrubbing solvent is subsequently used in a flash tank at reduced pressure of about 0.11e0.2 MPa to gather the release gas (being rich in CH4 and CO2) that is mixed with the input gas and reinjected into the scrubber. The remaining solvent is used in a low pressure packed column stripper with air as the striping agent to desorb CO2 and regenerate scrubbing water which is pumped back into the scrubber. The sizes of the absorber and flash vessels are related to the properties of solvents and operating conditions (i.e., temperature and pressure), while the size of the desorber is related to both the method of desorption and the properties of the solvents (Wang et al., 2020). Ionic liquids (ILs) with anions and cations (e.g., aqueous N-butyl-N-methylmorpholinium acetate) Reference Rotunno et al. (2017) Barbera et al. (2019) Kapoor, Subbarao, and Vijay (2019) Bauer et al. (2013) Barbera et al. (2019) Haider et al. (2016) He et al. (2018) Bozorg et al. (2020) Barbera et al. (2019) Singhal et al. (2017) 89
90 Chapter 5 Waste-to-biomethane Figure 5.2 A schematic diagram of the SFS-HPWS technology (Wylock & Budzianowski, 2017). have been tested for their negligible volatility, good thermal stability, tunability, reduced equipment size, and lower energy usage (Ma et al., 2019). Energy can be saved by replacing the desorber with the flash vessel for IL-based absorbents. 3.2.2 Chemical absorption Chemical absorption is performed by covalently bonding a gas into the molecules of an absorbing liquid and has the potential of removing CO2 and H2S simultaneously. It is generally more efficient in absorbing CO2 even at normal pressure and ambient temperature due to (i) the strong covalent bonds between the chemical solvent molecules and the CO2 molecules (Abdeen et al., 2016) and (ii) the reaction of the absorbed chemicals with the active components in the liquid phase. The chemical absorption method can achieve a methane purity of 99 vol.% (Nguyen et al., 2020). As shown in Fig. 5.3, a chemical absorption process is normally carried out in a packed column attached to a stripping column corresponding to a reboiler-equipped desorption unit (Kapoor, Ghosh, et al., 2019). The biogas is injected in the packed bed absorber operating at 1e2 bar (0.1-0.2 MPa) from the bottom
Chapter 5 Waste-to-biomethane Figure 5.3 Biogas upgrading by chemical absorption (amine scrubbing) (Kapoor, Ghosh, et al., 2019). and the chemical solvent is supplied countercurrently. The chemical absorption column can be represented using an ideal plug flow reactor where the mixing is considered in the radial direction and not in the axial direction (Abdeen et al., 2016). It is critical to choose an appropriate solvent for efficient chemical absorption, which is generally based on the solubility difference between methane and impurity gas components in the solvent. Major chemical absorption solvents for biogas include amines and caustic/alkaline solvents. Using the chemical absorption of CO2 as an example, amine-based solution can be used to absorb CO2 by forming covalent bonds between amines and CO2. Some commonly used amines include MEA, TEA, methyldiethanolamine (MDEA), diglycolamine (DGA), DEA, and piperazine (PZ) and they are featured by high selectivity against CO2 (Abdeen et al., 2016). The chemical reaction between CO2 and amine enhances the mass transfer of CO2 from the gas to the liquid phase. This serves to maintain the concentration gradient of CO2 in the two phases, and a molar flow ratio between amine and CO2 of at least 4 was recommended for amine scrubbing (Abdeen et al., 2016). These solvents do not react with but only dissolve methane (0.1%e0.2%) during the absorption process. This mitigates the loss of methane and saves the need of lean gas postcombustion while achieving 91
92 Chapter 5 Waste-to-biomethane higher CO2 separation as compared to the water-based scrubbing technology (Sun et al., 2015). Major disadvantages of amine-based chemical absorption include amine degradation, equipment corrosion, and potential generation and emission of pollutants (e.g., nitrosamines and nitramines) (Nguyen et al., 2020). The degradation of amines can be oxidative or thermal (with the former being dominant in pilot and industrial plants) (Fytianos et al., 2016). It happens upon the irreversible reactions between amines and CO2, O2, or other substances (e.g., solid particles, SO2, and NOx), which leads to the loss of amine solvent and solvent absorption capacity, foaming, and other problems such as increased corrosion and solvent disposal costs (Abdeen et al., 2016). Some of the oxidative degradation compounds include ammonia, aldehydes, and carboxylic acids, while major thermal degradation compounds include 1-(2-hydroxyethyl)-2-imidazolidone (HEIA), N-(2-hidroxyethyl)ethylenediamine (HEEDA), and 2-oxazolidone (OZD) with HEIA being the dominant one. It is beneficial to limit amine degradation by removing biogas impurities prior to amine-based absorption. It is also possible to mitigate amine degradation and its adverse effects by selecting relatively a degradation-resistant solvent (e.g., piperazine and MDEA), lowering CO2 loading and temperature, and increasing the amine concentration. Some amines (e.g., MEA) and their by-products after degradation are corrosive or tend to increase corrosion, which can be further enhanced by such conditions as high temperature, high CO2 loading and partial pressure and amine concentration, and rough surface conditions (Abdeen et al., 2016). For example, heat-stable salts can be formed from the anions of the oxidative degradation product carboxylic acids and MEA and is a corrosive agent with formate and oxalate being the most damaging. The saturated scrubbing solution is generally regenerated at 100e120 C to break down the binding between CO2 and solvent molecules followed by cooling down to 40 C for reuse. This is an energy intensive process and can consume 0.4 to 0.8 kWh/Nm3 of biogas, or about 15%e30% of the energy harvested from AD (Nguyen et al., 2020). To achieve optimal energy management for the biogas upgrading process, it is important to select the right amine solution (a high absorption capacity lowering the demand for regeneration) and optimize the design and conditions of system heat exchange and operation (e.g., gas flow rate, temperature, and stripper type and design). Caustic solvents (potassium hydroxide (KOH), sodium hydroxide (NaOH), and calcium hydroxide (CaOH)) have been applied to chemically absorb CO2, and are relatively cheap and available as
Chapter 5 Waste-to-biomethane compared to amines. NaOH has a greater CO2 capture capacity as compared to MEA (Abdeen et al., 2016). For example, theoretically, 0.9 and 1.39 tonnes of NaOH and MEA are needed to capture 1 tonne of CO2 (Yoo et al., 2013). KOH is relatively expensive but K2CO3 as the product of the chemical reaction between KOH and CO2 can be used in some industrial applications, which might help to offset the higher cost of KOH. The use of CaOH to absorb CO2 and form CaCO3 is called mineral carbonation which is slow under the condition of ambient temperature and pressure (Abdeen et al., 2016). Increasing temperature and pressure and adjusting the liquidesolid ratio can be used to enhance the reaction rate of mineral carbonation. As compared to amine-based solvents, the major drawbacks of alkali solvents include slow absorption rates and difficulty in solvent regeneration. The absorption process generates aqueous alkali salts (e.g., Na2CO3 and K2CO3 salts) which are thermally stable. Conventionally, NaOH or KOH can be regenerated based on a process called causticization based on the reaction between Na2CO3 or K2CO3 and lime (Ca(OH)2) and the decomposition of CaCO3 into CaO and CO2. However, the decomposition reaction is an endothermic reaction with a high energy requirement (178 kJ ∙ (mol/CO2)) (Abdeen et al., 2016). This method also suffers from other problems like a limited efficiency and the production of a low alkalinity solvent. Moreover, the direct use of lime for the causticization of alkaline solutions could involve the emission of significant CO2 upon the production of lime. Calciumcontaining waste, in this case, serves as a better option in terms of a lower carbon footprint. An alternative technology was developed and could reduce the high-grade heat requirement by 50% and the maximum temperature by at least 50 C as compared to the conventional causticization process: anhydrous Na2CO3 was separated from the concentrated NaOH solution using a two-step precipitation and crystallization process followed by the causticization of the anhydrous sodium carbonate using sodium trititanate (Mahmoudkhani & Keith, 2009). 3.2.3 Membrane separation The technical principles of membrane separation for biogas upgrading are similar to the ones for syngas upgrading; that is, it is based on the permeation of molecules through a semipermeable surface under high pressure to separate other gas molecules from methane. Similarly, there are several different types of membranes that can be used for biomethane separation, including inorganic, polymeric, and mixed matrix membranes. The permeation driving force can be developed by (i) compression in the feed side, 93
94 Chapter 5 Waste-to-biomethane (ii) vacuum in the permeate side, or (iii) the application of a sweep gas in the permeate side (Baena-Moreno et al., 2020). For (i), the feed gas is pressurized while the permeate side operates at a lower, ambient pressure, leading to the production of a pressurized retentate that facilitates high-pressure downstream applications such as feeding into the natural gas grid. The vacuum-based method applies to relatively small volume of biogas and generally requires a compression stage downstream for practical implementation. The sweep gas-based method leads to the dilution of the permeate flow and incurs an extra cost due to the use of the sweep gas. Both single-stage and multistage configurations have been developed for membrane separationebased biogas upgrading. The single-stage configuration featured by its simplicity and low cost involves the use of a single membrane and has the drawback of a high methane loss. To improve the methane recovery, the permeate can be partially recirculated. The multistage configuration interconnecting two or more membrane modules in series generally achieve a higher methane purity and a lower methane loss (Fig. 5.4) (Baena-Moreno et al., 2020). The most basic multistage configuration (Fig. 5.4(a)) utilizes the recirculation of the permeate of the second module to reduce methane losses. Another multistage configuration (Fig. 5.4(b)) uses two compression stages to increase the methane purity and reduce the methane loss, Figure 5.4 Multistage configuration: (a) Second membrane module and permeate recirculation, (b) Second membrane module, a retentate recirculation and two compressors, (c) Without recirculation of the second membrane module retentate, (d) With sweep gas use and a second membrane module with permeate recirculation (Baena-Moreno et al., 2020).
Chapter 5 Waste-to-biomethane incurring high energy consumption. The third multistage configuration (Fig. 5.4(c)) does not involve the permeate recirculation and has a reduced operational cost. In the fourth configuration (Fig. 5.4(d)), a sweep gas is used in the second stage to improve its efficiency which incurs additional capital and operating expenses due to the use of the sweep gas. The power consumption upon the use of gas compressors/vacuum pumps and the compressor and membrane capital costs account for the major cost components of the membrane separation technologies (Bozorg et al., 2020). It becomes important to select the right membrane, configuration, and operating conditions to lower the costs for higher economic profitability. 3.2.4 Pressure swing adsorption PSA separates CH4 from N2, O2, and CO2 based on molecular size exclusion and selective adsorption of the unwanted gas components on the adsorbent surface under high pressure. This is associated with the differences of molecule sizes between CH4 and other gas components (3.8 Å for CH4 compared to 3.4 Å for CO2, 3.64 Å for nitrogen, and 2.65 Å for water) (Vilardi et al., 2020). The technical principle of a PSA cycle is similar to the one applied for syngas upgrading and is based on a four-stage mechanism, i.e., adsorption, blow-down desorption, purge, and repressurization. PSA can achieve a methane concentration of 96%e98%, while the methane loss can be up to 4% in the off-gas stream that can be burned using a flameless oxidation burner (Abd et al., 2021). Major influential factors of PSA-based separation include adsorbent pore size, surface morphology, operating temperature and pressure, etc. The advantages of the PSA technology include system compactness, low energy requirement (energy consumption ranging from 0.15 to 0.3 kWh/Nm3), simplicity, no wastewater generation (a dry process), and safety (Abd et al., 2021). The operating pressure and temperature of the adsorption process are typically between 4e10 bars and 50e60 C, respectively, while the regeneration process is normally under 0.1e0.2 bars (0.01e0.02 MPa) taking place in around 2e3 min (Abd et al., 2021). The adsorption capacity of PSA is related to the biogas composition, the material of adsorbent, the process cycle design (e.g., the more the number of columns, the higher purity and recovery achieved), and operating conditions (e.g., adsorption pressure). To reduce the adverse impact of sulfur contaminants (e.g., H2S) on PSA, a pretreatment process based on such technologies as adsorption, absorption, and membrane is normally included for removing the contaminant. The processing capacity of PSA biogas upgrading systems typically range from 200 to 2000 Nm3/h. 95
96 Chapter 5 Waste-to-biomethane 3.2.5 Other upgrading methods The approaches described above are the four most used biogas upgrading techniques. Cryogenic separation utilizes the differences of the boiling points (e.g., methane is 111.5 K for methane vs. 194.8 K for CO2) of the different gas components and thus the phenomena of gas liquefaction under different temperature and pressure to separate gases. During the process, biogas is cooled down and compressed to a condition of specific temperature and pressure for liquefying CO2, and the equipment of the process including compressor, heat exchange, cooler, etc., can consume 5%e10% of the energy from methane production (Abd et al., 2021). Cryogenic separation can achieve a high level of methane purity and a low methane loss (<1%), and can produce high-purity liquid CO2 (up to 98%) with the additional benefits of ease of storage and use. However, the technology still faces up with a variety of challenges against its practical application, including significant energy consumption for refrigeration and the formation of dry ice that blocks pipes risking the stability of system operation (Yousef et al., 2018). References Abdeen, F. R. H., Mel, M., Jami, M. S., Ihsan, S. I., & Ismail, A. F. (2016). A review of chemical absorption of carbon dioxide for biogas upgrading. Chinese Journal of Chemical Engineering, 24(6), 693e702. Abd, A. A., Othman, M. R., Naji, S. Z., & Hashim, A. S. (2021). Methane enrichment in biogas mixture using pressure swing adsorption: Process fundamental and design parameters. Materials Today Sustainability, 100063. Ali, S., Hua, B., Huang, J. J., Droste, R. L., Zhou, Q., Zhao, W., & Chen, L. (2019). Effect of different initial low pH conditions on biogas production, composition, and shift in the aceticlastic methanogenic population. Bioresource Technology, 289, 121579. Baena-Moreno, F. M., le Sache, E., Pastor-Perez, L., & Reina, T. R. (2020). Membrane-based technologies for biogas upgrading: A review. Environmental Chemistry Letters, 18(5), 1649e1658. Barbera, E., Menegon, S., Banzato, D., D’Alpaos, C., & Bertucco, A. (2019). From biogas to biomethane: A process simulation-based techno-economic comparison of different upgrading technologies in the Italian context. Renewable Energy, 135, 663e673. Bauer, F., Hulteberg, C., Persson, T., & Tamm, D. (2013). Biogas upgrading-Review of commercial technologies. Bozorg, M., Ramírez-Santos, Á. A., Addis, B., Piccialli, V., Castel, C., & Favre, E. (2020). Optimal process design of biogas upgrading membrane systems: Polymeric vs high performance inorganic membrane materials. Chemical Engineering Science, 225, 115769. n, M. A., Sa nchez-Polo, M., Martín, M. J., & Cabrera-Codony, A., Montes-Mora Gonzalez-Olmos, R. (2014). Biogas upgrading: Optimal activated carbon properties for siloxane removal. Environmental Science & Technology, 48(12), 7187e7195.
Chapter 5 Waste-to-biomethane Chaiprapat, S., Mardthing, R., Kantachote, D., & Karnchanawong, S. (2011). Removal of hydrogen sulfide by complete aerobic oxidation in acidic biofiltration. Process Biochemistry, 46(1), 344e352. Choong, Y. Y., Chou, K. W., & Norli, I. (2018). Strategies for improving biogas production of palm oil mill effluent (POME) anaerobic digestion: A critical review. Renewable and Sustainable Energy Reviews, 82, 2993e3006. Dai, X., Hua, Y., Dai, L., & Cai, C. (2019). Particle size reduction of rice straw enhances methane production under anaerobic digestion. Bioresource Technology, 293, 122043. Dareioti, M. A., & Kornaros, M. (2014). Effect of hydraulic retention time (HRT) on the anaerobic co-digestion of agro-industrial wastes in a two-stage CSTR system. Bioresource Technology, 167, 407e415. Escudero, M. J., & Serrano, J. L. (2019). Individual impact of several impurities on the performance of direct internal reforming biogas solid oxide fuel cell using W-Ni-CeO2 as anode. International Journal of Hydrogen Energy, 44(36), 20616e20631. Fortuny, M., Gamisans, X., Deshusses, M. A., Lafuente, J., Casas, C., & Gabriel, D. (2011). Operational aspects of the desulfurization process of energy gases mimics in biotrickling filters. Water Research, 45(17), 5665e5674. Fountoulakis, M. S., Petousi, I., & Manios, T. (2010). Co-digestion of sewage sludge with glycerol to boost biogas production. Waste Management, 30(10), 1849e1853. Fytianos, G., Ucar, S., Grimstvedt, A., Hyldbakk, A., Svendsen, H. F., & Knuutila, H. K. (2016). Corrosion and degradation in MEA based postcombustion CO2 capture. International Journal of Greenhouse Gas Control, 46, 48e56. Haga, K., Adachi, S., Shiratori, Y., Itoh, K., & Sasaki, K. (2008). Poisoning of SOFC anodes by various fuel impurities. Solid State Ionics, 179(27e32), 1427e1431. Haider, S., Lindbråthen, A., & Hägg, M.-B. (2016). Techno-economical evaluation of membrane based biogas upgrading system: A comparison between polymeric membrane and carbon membrane technology. Green Energy & Environment, 1(3), 222e234. He, X., Chu, Y., Lindbråthen, A., Hillestad, M., & Hägg, M.-B. (2018). Carbon molecular sieve membranes for biogas upgrading: Techno-economic feasibility analysis. Journal of Cleaner Production, 194, 584e593. Izumi, K., Okishio, Y., Nagao, N., Niwa, C., Yamamoto, S., & Toda, T. (2010). Effects of particle size on anaerobic digestion of food waste. International Biodeterioration & Biodegradation, 64(7), 601e608. Jin, Y., Veiga, M. C., & Kennes, C. (2005). Effects of pH, CO2, and flow pattern on the autotrophic degradation of hydrogen sulfide in a biotrickling filter. Biotechnology and Bioengineering, 92(4), 462e471. Kapoor, R., Ghosh, P., Kumar, M., & Vijay, V. K. (2019). Evaluation of biogas upgrading technologies and future perspectives: A review. Environmental Science and Pollution Research, 26(12), 11631e11661. Kapoor, R., Subbarao, P. M. V., & Vijay, V. K. (2019). Integration of flash vessel in water scrubbing biogas upgrading system for maximum methane recovery. Bioresource Technology Reports, 7, 100251. Komemoto, K., Lim, Y. G., Nagao, N., Onoue, Y., Niwa, C., & Toda, T. (2009). Effect of temperature on VFA’s and biogas production in anaerobic solubilization of food waste. Waste Management, 29(12), 2950e2955. Kvist, T., & Aryal, N. (2019). Methane loss from commercially operating biogas upgrading plants. Waste Management, 87, 295e300. 97
98 Chapter 5 Waste-to-biomethane Lanzini, A., Madi, H., Chiodo, V., Papurello, D., Maisano, S., & Santarelli, M. (2017). Dealing with fuel contaminants in biogas-fed solid oxide fuel cell (SOFC) and molten carbonate fuel cell (MCFC) plants: Degradation of catalytic and electro-catalytic active surfaces and related gas purification methods. Progress in Energy and Combustion Science, 61, 150e188. Maharaj, I., & Elefsiniotis, P. (2001). The role of HRT and low temperature on the acid-phase anaerobic digestion of municipal and industrial wastewaters. Bioresource Technology, 76(3), 191e197. Mahmoudkhani, M., & Keith, D. W. (2009). Low-energy sodium hydroxide recovery for CO2 capture from atmospheric airdthermodynamic analysis. International Journal of Greenhouse Gas Control, 3(4), 376e384. Mao, C., Feng, Y., Wang, X., & Ren, G. (2015). Review on research achievements of biogas from anaerobic digestion. Renewable and Sustainable Energy Reviews, 45, 540e555. https://doi.org/10.1016/j.rser.2015.02.032 Ma, C., Shukla, S. K., Samikannu, R., Mikkola, J.-P., & Ji, X. (2019). CO2 separation by a series of aqueous morpholinium-based ionic liquids with acetate anions. ACS Sustainable Chemistry & Engineering, 8(1), 415e426. Matsui, T., & Imamura, S. (2010). Removal of siloxane from digestion gas of sewage sludge. Bioresource Technology, 101(1), S29eS32. Mirmohamadsadeghi, S., Karimi, K., Tabatabaei, M., & Aghbashlo, M. (2019). Biogas production from food wastes: A review on recent developments and future perspectives. Bioresource Technology Reports, 7, 100202. Nguyen, L. N., Kumar, J., Vu, M. T., Mohammed, J. A. H., Pathak, N., Commault, A. S., Sutherland, D., Zdarta, J., Tyagi, V. K., & Nghiem, L. D. (2020). Biomethane production from anaerobic co-digestion at wastewater treatment plants: A critical review on development and innovations in biogas upgrading techniques. The Science of the Total Environment, 142753. Papurello, D., & Lanzini, A. (2018). SOFC single cells fed by biogas: Experimental tests with trace contaminants. Waste Management, 72, 306e312. Reeping, K. W., Bohn, J. A., & Walker, R. A. (2017). Chlorine-induced degradation in SOFCs operating with biogas. Sustainable Energy & Fuels, 1(6), 1320e1328. Rotunno, P., Lanzini, A., & Leone, P. (2017). Energy and economic analysis of a water scrubbing based biogas upgrading process for biomethane injection into the gas grid or use as transportation fuel. Renewable Energy, 102, 417e432. Santos-Clotas, E., Cabrera-Codony, A., Boada, E., Gich, F., Muñoz, R., & Martín, M. J. (2019). Efficient removal of siloxanes and volatile organic compounds from sewage biogas by an anoxic biotrickling filter supplemented with activated carbon. Bioresource Technology, 294, 122136. Seredych, M., & Bandosz, T. J. (2006). Desulfurization of digester gas on catalytic carbonaceous adsorbents: Complexity of interactions between the surface and components of the gaseous mixture. Industrial & Engineering Chemistry Research, 45(10), 3658e3665. Singhal, S., Agarwal, S., Arora, S., Sharma, P., & Singhal, N. (2017). Upgrading techniques for transformation of biogas to bio-CNG: A review. International Journal of Energy Research, 41(12), 1657e1669. Sun, Q., Li, H., Yan, J., Liu, L., Yu, Z., & Yu, X. (2015). Selection of appropriate biogas upgrading technology-a review of biogas cleaning, upgrading and utilisation. Renewable and Sustainable Energy Reviews, 51, 521e532. Tian, G., Yang, B., Dong, M., Zhu, R., Yin, F., Zhao, X., Wang, Y., Xiao, W., Wang, Q., & Zhang, W. (2018). The effect of temperature on the microbial communities of peak biogas production in batch biogas reactors. Renewable Energy, 123, 15e25.
Chapter 5 Waste-to-biomethane Truong, L.-A., & Abatzoglou, N. (2005). A H2S reactive adsorption process for the purification of biogas prior to its use as a bioenergy vector. Biomass and Bioenergy, 29(2), 142e151. Vikromvarasiri, N., Champreda, V., Boonyawanich, S., & Pisutpaisal, N. (2017). Hydrogen sulfide removal from biogas by biotrickling filter inoculated with Halothiobacillus neapolitanus. International Journal of Hydrogen Energy, 42(29), 18425e18433. Vilardi, G., Bassano, C., Deiana, P., & Verdone, N. (2020). Exergy and energy analysis of biogas upgrading by pressure swing adsorption: Dynamic analysis of the process. Energy Conversion and Management, 226, 113482. Wang, H., Ma, C., Yang, Z., Lu, X., & Ji, X. (2020). Improving high-pressure water scrubbing through process integration and solvent selection for biogas upgrading. Applied Energy, 276, 115462. Wu, J., Jiang, X., Jin, Z., Yang, S., & Zhang, J. (2020). The performance and microbial community in a slightly alkaline biotrickling filter for the removal of high concentration H2S from biogas. Chemosphere, 249, 126127. Wylock, C. E., & Budzianowski, W. M. (2017). Performance evaluation of biogas upgrading by pressurized water scrubbing via modelling and simulation. Chemical Engineering Science, 170, 639e652. Xu, Z., Zhao, M., Miao, H., Huang, Z., Gao, S., & Ruan, W. (2014). In situ volatile fatty acids influence biogas generation from kitchen wastes by anaerobic digestion. Bioresource Technology, 163, 186e192. € n, O., & Demirel, B. (2013). Ammonia inhibition in anaerobic digestion: Yenigu A review. Process Biochemistry, 48(5e6), 901e911. Yong, Z., Dong, Y., Zhang, X., & Tan, T. (2015). Anaerobic co-digestion of food waste and straw for biogas production. Renewable Energy, 78, 527e530. Yoo, M., Han, S.-J., & Wee, J.-H. (2013). Carbon dioxide capture capacity of sodium hydroxide aqueous solution. Journal of Environmental Management, 114, 512e519. Yousef, A. M., El-Maghlany, W. M., Eldrainy, Y. A., & Attia, A. (2018). New approach for biogas purification using cryogenic separation and distillation process for CO2 capture. Energy, 156, 328e351. Yousef, A. M., El-Maghlany, W. M., Eldrainy, Y. A., & Attia, A. (2019). Upgrading biogas to biomethane and liquid CO2: A novel cryogenic process. Fuel, 251, 611e628. Zhang, Y., & Banks, C. J. (2013). Impact of different particle size distributions on anaerobic digestion of the organic fraction of municipal solid waste. Waste Management, 33(2), 297e307. Zhao, W., Huang, J. J., Hua, B., Huang, Z., Droste, R. L., Chen, L., Wang, B., Yang, C., & Yang, S. (2020). A new strategy to recover from volatile fatty acid inhibition in anaerobic digestion by photosynthetic bacteria. Bioresource Technology, 311, 123501. Further reading De Arespacochaga, N., Valderrama, C., Mesa, C., Bouchy, L., & Cortina, J. L. (2014). Biogas deep clean-up based on adsorption technologies for Solid Oxide Fuel Cell applications. Chemical Engineering Journal, 255, 593e603. Gandiglio, M., Lanzini, A., Santarelli, M., & Leone, P. (2014). Design and balanceof-plant of a demonstration plant with a solid oxide fuel cell fed by biogas from waste-water and exhaust carbon recycling for algae growth. Journal of Fuel Cell Science and Technology, 11(3). 99
100 Chapter 5 Waste-to-biomethane Papadias, D., & Ahmed, S. (2012). Biogas impurities and cleanup for fuel cells. In Proceedings of the presentation about biogas technologies and integration with fuel cells. The NREL/DOE biogas and fuel cells workshop, golden, CO, USA (pp. 11e13). Rasi, S. (2009). Biogas composition and upgrading to biomethane (Issue 202). University of Jyväskylä.
Waste-to-bioethanol 6 Abstract This chapter introduces the definitions of first-generation and secondgeneration bioethanol with the latter being the focus. It gives an overview of the technical principles of the bioethanol production based on saccharification and fermentation. It explains the different types of configuration designs including Separate Hydrolysis and Fermentation (SHF), Simultaneous Saccharification and Fermentation (SSF), Presaccharification and Simultaneous Saccharification and Fermentation (PSSF), and Simultaneous Saccharification and Co-fermentation (SSCF) and their respective advantages and disadvantages. The influences of typical process factors (e.g., temperature, sugar concentration, pH, fermentation time, agitation speed) toward bioethanol yields are reviewed and existing bioethanol yield data are summarized. This chapter also highlights the importance of pretreatment and yeast selection toward bioethanol production and development. Keywords: Bioethanol yields; Distillation; Pretreatment; Saccharification and fermentation; Waste-to-bioethanol; Yeasts. 1. Introduction Ethanol has been widely used to partially replace fossil fuels in the transportation sector for its potential to increase domestic energy security, reduce GHG and air pollutant emissions, and promote the development of remote, rural industries, and communities (Stephen et al., 2012). For example, it is used as a gasoline blend stock in the United States since the 1980s, and E10 petrol containing 10 vol.% of renewable ethanol is supported by annual production of 14 billion gallons of corn starch-based ethanol (Hoekman & Broch, 2018). The global ethanol production increased from 98.3 billion liters in 2016 to 98.6 billion liters in 2017 with record levels in the United States and sharp increases in India and China; 72% of biofuel production was accounted for by ethanol followed by biodiesel (23%) and hydrotreated vegetable oil (4%) (Sawin et al., 2017). First-generation bioethanol that is derived from sugarcanes or starch (corn or sorghum) dominates the whole ethanol production, with 85% contributed by the United States and Brazil Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00012-8 Copyright © 2022 Elsevier Inc. All rights reserved. 101
102 Chapter 6 Waste-to-bioethanol Figure 6.1 Schematics diagrams of (a) first-generation and (b) second-generation bioethanol production (Lennartsson et al., 2014). (Bertrand et al., 2016). The procedure of first-generation bioethanol production is relatively mature and could be divided into seven steps (Fig. 6.1(a)): (i) feedstock pretreatment consisting of milling and liquefaction, (ii) hydrolysis or saccharification where sugar monomers are released, (iii) fermentation with yeasts to produce ethanol (w10% (w/v)) and carbon dioxide, (iv) ethanol purification (concentrations >99.7%) by distillation and dehydration, (v) centrifugation of the stillage consisting of residual substrate, yeasts, and fermentation by-products to form thin stillage and centrifugation solids, (vi) thin stillage evaporation and resupplied to (i), and (vii) production of Distillers Dried Grains and Solubles as an animal feed. Though first-generation bioethanol is expected to keep playing a major
Chapter 6 Waste-to-bioethanol role in overall bioethanol production, much attention has been paid to its potential environmental impacts on land use, water resource, and particularly feed and food production due to the use of food-based feedstock (leading to the so-called “food versus fuel” debate). Indeed, the feedstock production stage including agricultural production is a major contributor to the adverse environmental impact of first-generation bioethanol. Second-generation ethanol utilizes lignocellulosic feedstocks that are not suitable for human consumption such as inedible energy crops and oils, agricultural and municipal wastes, algae, etc. These feedstocks are featured by their high abundance and availability, e.g., lignocellulose is the most abundant polymer, and by the benefit of not directly causing the “food versus fuel” competition. Moreover, extensive LCA studies showed that secondgeneration bioethanol has better environmental and energy performance as compared to first-generation bioethanol (Stephen et al., 2012). Saccharification and fermentation processes serve as one of the main technologies for recovering bioethanol from waste and have significant carbon abatement potential. This chapter will focus on the processes for producing second-generation bioethanol from lignocellulosic waste such as wheat straw, corn stover, rice husk, banana residues (pseudostem and rachis), biodegradable municipal waste, etc. 2. Saccharification and fermentation Saccharification and fermentation processes typically involve a four-step, sequential procedure (Fig. 6.1(b)): (i) pretreatment to separate the hemicelluloses from the polymerous cellulose chains and the interwoven lignin, (ii) enzymatic hydrolysis or saccharification to release sugar monomers from the cellulose and hemicellulose, (iii) fermentation of the monomers with yeasts (e.g., Saccharomyces cerevisiae), to produce ethanol, and (iv) distillation and dehydration-based ethanol purification and the recovery of coproducts such as lignin, extractives, and unhydrolyzed cellulose. Different configuration designs have been developed including Separate Hydrolysis and Fermentation (SHF), Simultaneous Saccharification and Fermentation (SSF), Presaccharification and Simultaneous Saccharification and Fermentation (PSSF), and Simultaneous Saccharification and Co-fermentation (SSCF). SHF refers to the arrangement where the enzymatic hydrolysis step releasing sugars is separate from the yeast fermentation processing of the sugars. SHF relies on the use of separate vessels where different process conditions (e.g., temperature and pH) can be set up for enzymes and yeasts to optimize the yield of the two 103
104 Chapter 6 Waste-to-bioethanol Figure 6.2 Flowchart of the SHF process. Adapted from de Barros, E. M., Carvalho, V. M., Rodrigues, T. H. S., Rocha, M. V. P., & Gonçalves, L. R. B. (2017). Comparison of strategies for the simultaneous saccharification and fermentation of cashew apple bagasse using a thermotolerant Kluyveromyces marxianus to enhance cellulosic ethanol production. Chemical Engineering Journal, 307, 939e947. https://doi.org/https://doi.org/10.1016/j.cej.2016.09.006). steps, respectively (Guerrero et al., 2018). Moreover, the separate vessel arrangement allows the addition of a sterilizing process to treat the saccharified solution to reduce the risk of contamination. However, the dual vessel usage increases the capital cost. It was shown that the capital cost of an SHF-based sugarcane straw-to-bioethanol plant was about 50% higher than that of SSF- and PSSF-based ones (Mesa et al., 2017). For the SHF process (Fig. 6.2), the degree of saccharification that controls the ethanol production can be affected by various factors such as pretreatment methods, hydrolysis conditions (e.g., time and temperature), cellulase dosage, and substrate concentration. For example, it was shown that, to achieve a mean maximum degree of saccharification of 82.1% for bioethanol recovery from paper sludge using the SHF process with cellulase, the hydrolysis time, substrate concentration, and cellulase dosage needed to be 82.7 h, 40.8 g/L, and 18.1 FPU/g substrate, respectively (Peng & Chen, 2011). With fermentation using S. cerevisiae GIM-2, a sugar-to-ethanol conversion rate of 34.2% and an ethanol yield of 190 g/kg of dry paper sludge were achieved. In the SSF process (Fig. 6.3), enzymatic hydrolysis and fermentation happens simultaneously in the same reactor meaning the associated yeasts ferment the sugars as soon as they are released by enzyme hydrolysis. This arrangement mitigates the problem of enzyme inhibition and increases the rate of cellulose hydrolysis because relevant sugars that can inhibit the activity of the Figure 6.3 Flowchart of SSF. Adapted from de Barros, E. M., Carvalho, V. M., Rodrigues, T. H. S., Rocha, M. V. P., & Gonçalves, L. R. B. (2017). Comparison of strategies for the simultaneous saccharification and fermentation of cashew apple bagasse using a thermotolerant Kluyveromyces marxianus to enhance cellulosic ethanol production. Chemical Engineering Journal, 307, 939e947. https://doi.org/https://doi.org/10.1016/j.cej.2016.09.006).
Chapter 6 Waste-to-bioethanol cellulose are quickly removed and converted into ethanol (Cheung & Anderson, 1997). Comparative experiments showed that SSF processing of Arundo donax biomass pretreated with diluted acid generated 0.26 g/L/h and 25.0 g/L of ethanol which were higher than that (0.17 g/L/h and 24 g/L) for SHF processing of the biomass (Loaces et al., 2017). Higher ethanol concentrations were also obtained for SSF processing of South African grass (Eragrostis curvula) with acid pretreatment than SHF: for pretreatment using acid mine drainage, the ethanol concentration reached 14.43 g/L for SHF as compared to 14.83 g/L for SSF at a biomass solid loading of 20 wt.%; for pretreatment using 1 wt.% H2SO4, the ethanol concentration reached 19.20 g/L for SHF as compared to 22.25 g/L for SSF at a biomass solid loading of 20 wt.% (Burman et al., 2019). Additional benefits of this configuration include a reduced capital cost as less equipment is needed, lowered enzyme requirements, mitigation of the inhibition caused by high ethanol concentration, reduction in the processing time, etc. (Cheung & Anderson, 1997). However, it is hardly possible to have a single temperature to optimize both enzymatic hydrolysis and fermentation considering the distinction in the optimal temperature conditions for hydrolytic enzymes (45e50 C) and fermentation yeasts (30e37 C) (Palacios et al., 2019). Hence, an intermediate temperature and a thermotolerant yeast are needed for better compatibility of enzyme hydrolysis and fermentation. A higher biomass loading is also preferred for SSF to achieve higher ethanol production. PSSF (Fig. 6.4) is a variation of the SSF process by applying a short-period (8e24 h), prehydrolysis stage before SSF and the stage can be operated under optimal conditions to produce the substrate for SSF at a relatively high hydrolytic rate. This method can be used to mitigate SSF’s problem about enzymatic hydrolysis constraints caused by the differences in the optimal temperature between enzymes and yeasts. The PSSF method also serves to reduce the viscosity of substrate slurry, making it a favorable option for the cases of high solid concentrations (Jørgensen et al., 2007). Hence, it is expected that PSSF combines the Figure 6.4 Flowchart of PSSF. Adapted from de Barros, E. M., Carvalho, V. M., Rodrigues, T. H. S., Rocha, M. V. P., & Gonçalves, L. R. B. (2017). Comparison of strategies for the simultaneous saccharification and fermentation of cashew apple bagasse using a thermotolerant Kluyveromyces marxianus to enhance cellulosic ethanol production. Chemical Engineering Journal, 307, 939e947. https://doi.org/https://doi.org/10.1016/j.cej.2016.09.006. 105
106 Chapter 6 Waste-to-bioethanol strengths of SHF and SSF and higher ethanol yields can be achieved based on the selection of an appropriate time for the prehydrolysis stage (de Barros et al., 2017). The SSCF configuration is another variation of SSF by co-fermenting pentoses (mainly xylose) together with glucose using a special type of microorganism such as genetically modified yeast strains (e.g., S. cerevisiae). To improve the performance of the strains, it is key to enhance the microorganism’s tolerance to environmental conditions (Laluce et al., 2012). Similar to SSF, SSCF has such advantages as mitigated inhibition (e.g., to cellulases or b-glucosidases) by the removal of sugars generated during enzymatic hydrolysis, reduced capital costs, and improved ethanol yields as compared to SHF (Koppram et al., 2013). SSCF is preferably conducted at high contents of water-insoluble solids in a fed-batch mode to improve mixing and mass transfer and thus the concentration of ethanol produced. This also promotes efficient cofermentation of glucose and xylose with the possibility of lowering the glucose concentration by, e.g., fermentation of free hexoses before the addition of enzymes. Extensive studies have been conducted to compare the performance of the different configurations in terms of bioethanol productivity. For example, toward bioethanol recovery from the cashew apple bagasse after acidicealkaline (first stage: 0.6 M H2SO4 at 121 C for 15 min and second stage: 1 M NaOH at 121 C for 30 min) pretreatment, the performance of SSF and its variation (PSSF) is compared (de Barros et al., 2017). Under a biomass load of 7.5% and 10%, the ethanol concentrations and yields of SSF were similar to that of PSSF, while under a load of 15%, the ethanol concentration (58.67 g/L) and yield (92.67%) of SSF were greater than that (50.11 g/L and 79.51%) of PSSF. Experiments based on pretreated olive tree pruning biomass showed that SHF with S. cerevisiae achieved the largest ethanol yield followed by PSSF with S. cerevisiae, SSCF with Escherichia coli and SSF with S. cerevisiae (Fernandes-Klajn et al., 2018). The yields of bioethanol production are also influenced by various process factors such as temperature, sugar concentration, pH, fermentation time, agitation speed, etc. (Azhar et al., 2017). Appropriate temperature (20e35 C) is essential for the growth and viability of microorganisms: S. cerevisiae has an optimal temperature around 30 C, whereas the optimal temperature for the yeasts of immobilized cells is slightly higher due to its ability to transfer heat to inside the cells. Increasing the sugar concentration up to around 150 g/L is appropriate for promoting fermentation, but further increases might have a limited effect
Chapter 6 Waste-to-bioethanol on bioethanol production as the available sugar may exceed the uptake capacity of microorganisms. pH critically affects the survival and growth of yeasts and rates of fermentation, and an optimal range of pH exists for maximizing ethanol production of a specific process. For example, for SHF processing of whey and rice waste with S. cerevisiae Y904, increasing pH from 3.5 to 4.5 increased the ethanol concentration from 4.5 g/L to 11.5 g/L, while further increasing pH to 5.5 reduced the ethanol concentration to 6.1 g/L (Rocha et al., 2013). For SSF processing of pretreated rice straw, the enzyme retained 82% of its activity at pH ¼ 5.0 which decreased gradually for pH > 7.0 and the enzyme was inactive at pH ¼ 9.0 (Akhtar et al., 2017). Under the optimal conditions where pH was equal to 4.5 at 30 C (other conditions: substrate loading ¼ 11% (w/v); enzyme concentration ¼ 0.5% (v/v)), an ethanol yield of 0.38 g/g was achieved. The ethanol yield can also be affected by fermentation time (normally between 24 and 72 h) with a short time leading to inadequate growth of microorganisms and a long time leading to microbial inhibition due to high concentration of ethanol (Azhar et al., 2017). An optimal fermentation time of 61.5 h was reported for SSF of very high gravity potato mash with the achievement of an ethanol yield of 16.61% (v/v) or 89.7% of the theoretical yield (Srichuwong et al., 2009). An optimal fermentation time of 24 h was reported for SHF of bamboo (Li et al., 2014). Mixing is important for saccharification by ensuring an even distribution of enzymes in the broth and facilitating effective heat and mass transfer, particularly for high solids saccharification. The degree of mixing can be adjusted by controlling the agitation speed of an installed impeller and a speed of up to 200 rpm can be used. The agitation speed will also affect the permeability of nutrients through the cells of microorganisms and the removal of ethanol from the cell (Azhar et al., 2017). Upon fed-batch saccharification of pretreated rice straw, increasing the agitation speed from 30 to 80 rpm was found to increase the glucose titers from 115.1 g/L to 132.6 g/L, while a further increase in the speed did not significantly promote the saccharification yield (Jung et al., 2017). Finally, the types of lignocellulosic feedstocks also affect the ethanol yield for a given process conditions. Various types of waste have been tested for their potential to generate bioethanol such as pineapple leaf waste, pomegranate peels, orange peel waste, lemon peel waste, bread residues, coffee residue, MSW, sugarcane waste, etc. Their ethanol productions are summarized in Table 6.1. 107
108 Chapter 6 Waste-to-bioethanol Table 6.1 Bioethanol yields for different types of lignocellulosic feedstocks. Feedstock Configuration Ethanol concentration or yield Reference Pineapple leaf waste Bread residues Pomegranate peel waste Waste paper Citrus peel waste Coffee residue Kitchen waste Barley straw SSF SHF SHF PSSF SSF SSF PSSF PSSF 7.12%(v/v) 350 g/kg bread dry matter 15.2e15.6 g/L 32 g/L (91.8%) 14.4e29.5 g/L (90.2%e93.1%) 15.3 g/L (87.2%) 43.9e45.0 g/L (88.9%e91.2%) 46.62 g/L Chintagunta et al. (2017) Ebrahimi et al. (2008) Talekar et al. (2018) Nishimura et al. (2016) Choi et al. (2015) Choi et al. (2012) Wang et al. (2017) Paschos et al. (2020) 3. Pretreatment Feedstock pretreatment is often needed to increase the efficiency of enzymatic hydrolysis by disrupting the complex structure of lignocellulosic biomass for higher accessibility of cellulase to cellulose substrate. There are three types of pretreatment methods, i.e., physical, physicochemical, and biological, respectively. The physicochemical method is based on the application of special compounds and conditions to change the physicochemical properties of lignocellulosic material and can be categorized into steam explosion, liquid hot water pretreatment, dilute acid pretreatment, lime pretreatment, etc. (Agbor et al., 2011). As the most used physicochemical method, steam explosion involves hydrolyzing hemicellulose using high pressure saturated steam (160e240 C and 0.7e4.8 MPa) from a few seconds to minutes followed by an explosive decompression. The hydrolysis of hemicellulose is done by the formed acetic acid or other acids and the use of steam serves to efficiently heat cellulose to a target temperature without significantly diluting the released sugars (Mosier et al., 2005). After steam explosion pretreatment, hemicellulose is removed and enzymes’ accessibility to cellulose fibrils gets improved. The advantages of the steam explosion method include less use of chemicals, appropriate dilution of sugars released, and low energy consumption. However, this method faces various challenges including (i) the risk of condensation and precipitation of soluble lignin components and reduced digestibility due to the incomplete destruction of ligninecarbohydrate matrix, (ii) partial destruction of hemicellulose xylan, (iii) potential formation of fermentation inhibitors at higher temperature, and (iv) the requirement of hydrolysate washing which might remove soluble sugars and reduce the overall saccharification yield by 20%e25% (Agbor et al., 2011).
Chapter 6 Waste-to-bioethanol The liquid hot water pretreatment method uses liquid water at an elevated temperature to hydrolyze hemicellulose and remove lignin for higher accessibility of cellulose. Depending on the relative flow of water and feedstock, the pretreatment can be conducted in a cocurrent, countercurrent, or flow-through reactor configuration (Agbor et al., 2011). During the process, hot water cleaves hemiacetal linkages and releases acids upon hydrolysis, facilitating the cleavage of ether linkages in feedstocks. Due to a reduced temperature, the problem of fermentation inhibitor formation as happened in steam explosion is mitigated. Accordingly, the major strengths of liquid hot water pretreatment include reduced formation of degradation products, saving the need for a washing or neutralizing step, and the relatively low cost of the solvent; however, this process incurs high energy consumption for the downstream process due to the large volume of water used. Various acids (e.g., hydrochloric acid, nitric acid, phosphoric acid, and sulfuric acid) have been tested and, generally, they are mixed (or put in contact with) with biomass and heated to a desirable temperature (140e215 C) to dissolve hemicellulose and increase the accessibility of cellulose in biomass for between a few seconds to a few minutes (Agbor et al., 2011). It can effectively promote the yield of sugar from the hemicellulose of lignocellulosic feedstock while reducing the consumption of acid. The dilute acid pretreatment method has various advantages including relatively high reaction rates and improved hydrolysis of cellulose and hemicellulose. For example, during the SHF processing of rice straw, dilute acid pretreated straw had a higher sugar yield (0.72 g/g) than steam pretreated straw (0.60 g/g) and unpretreated straw (0.46 g/g) during 48-h enzymatic hydrolysis (Abedinifar et al., 2009). However, this method incurs a neutralization process (e.g., using calcium hydroxide) prior to fermentation and may lead to the production of fermentation inhibitors and affect the efficiency of the subsequent fermentation process; moreover, the corrosiveness of some acids such as sulfuric acid suggests the need of using more corrosionresistant material for vessel construction featured by a relatively high cost (Mosier et al., 2005). Lime pretreatment is based on the use of calcium hydroxide (Ca(OH)2) as well as low temperature and pressure conditions to solubilize hemicellulose and lignin for higher enzymatic digestibility. The process of lime pretreatment involves deactylation and partial delignification leading to the opening of “acetyl valves” and “lignin valves” (Agbor et al., 2011). Lime pretreatment can be carried out by mixing lime with water and spraying the mixture 109
110 Chapter 6 Waste-to-bioethanol (slurry) onto biomass (<10 mm) followed by the storage of the biomass in a pile for hours to up to weeks (Mosier et al., 2005). A series of switchgrass lime pretreatment experiments suggested an optimal pretreatment condition was with time ¼ 2 h, temperature ¼ 100 C and 120 C, loading ¼ 0.1 g Ca(OH)2/g dry biomass, water loading ¼ 9 mL/g dry biomass (Chang et al., 1997). Under the optimal condition, the yield of 3-d total sugar (glucose þ xylose) was 7 times of the unpretreated case, and about 10% glucan, 26% xylan, and 29% lignin were solubilized. The advantages of lime pretreatment include the relative low cost of lime, easy recovery from water (as insoluble calcium carbonate by reaction with carbon dioxide), reduced energy consumption by low temperature (<100 C) operation; however, the process is less effective for high lignin material such as softwood and a relatively long treatment time (hours or even days) is required (Mosier et al., 2005). The physical method refers to the use of physical processes like mechanical comminution or extrusion to increase the accessibility of enzymes to carbohydrates. The mechanical comminution process can be achieved using chipping, grinding, milling, or a combination of them (particle sizes of 10e30 mm for chipping and of 0.2e2 mm for milling or grinding) and aims to reduce the particle size and crystallinity of lignocellulosic feedstock for a higher specific surface and a lower degree of polymerization (Alvira et al., 2010). For fibrous feedstock, this may incur significant energy consumption and costs. The extrusion process modifies the physicochemical properties (defibrillation, fibrillation, and shortening of fibers) of lignocellulosic feedstock by heating, mixing, and shearing it in an extruder. Key control parameters of the process include screw speed and barrel temperature. The biological method is based on the use of fungus (e.g., brown, white, and soft rot fungi) to break down the ligninehemicellulose matrix and is featured by low energy requirements and mild environmental conditions for operation (Ma et al., 2010). Brown rot fungi (e.g., Coniophora puteana and Postia placenta) can degrade both the cellulose and hemicellulose components of feedstock, which is initiated by the Early Stage Decay Mechanism that causes rapid and extensive depolymerization of cellulose and hemicellulose (Ray et al., 2010). Several hypotheses have been made about the degradation mechanism based on wood pretreatment: (i) reactive oxygen species serves to breakdown the lignocellulose complex with the dominant hydroxyl radical being produced via the Fenton reaction (H2O2 þ Fe2þ þ Hþ / H2O þ Fe3þ þ $OH) (i.e., a hydroxyl radicalebased depolymerization), and (ii) brown rot fungi secret various organic acids (e.g., oxalic) which can
Chapter 6 Waste-to-bioethanol reduce wood pH and depolymerize hemicellulose and cellulose by acid catalyzed hydrolysis (i.e., localized acid pretreatment). White (e.g., Phanerochaete chrysosporium, Irpex lacteus, Phlebia radiata, and Rigidoporus lignosus) and soft rot (e.g., Chalara parvispora and Trichoderma reesei) fungi attack both lignin and cellulose with the production of enzymes such as lignin peroxidases, polyphenol oxidases, manganese-dependent peroxidases, and laccases (Agbor et al., 2011). The most effective biological pretreatment method is based on the use of white rot fungi. I. lacteus-pretreated wheat straw was fermented using the yeast Pachysolen tannophilus achieved a bioethanol yield of 163 mg/g raw wheat straw, which was 23%e35% higher than that of a bioethanol process with steam explosion-based pretreatment and the yeast S. cerevisiae (LópezAbelairas et al., 2013). Studies have been conducted to compare the performance of different white rot fungi in pretreating lignocellulosic materials. It was shown that Pleurotus ostreatus and Pleurotus pulmonarius had greater modification effects on the lignin content of Eucalyptus grandis sawdust than Ganoderma lucidum, P. chrysosporium, and Trametes sp. (Castoldi et al., 2014). Specifically, an easily hydrolysable cellulose fraction was generated and its content followed the sequence: P. ostreatus (16.7% of total cellulose) > P. pulmonarius (15.4%) > Trametes sp. (10.1%) [ P. chrysosporium (2.8%) z no treatment (2.8%), and P. ostreatus and P. pulmonarius pretreatment was more efficient in generating the second hydrolysable fraction. However, the productivity of the biological method is relatively low due to the requirement of a long cultivation period and due to the loss in polysaccharide components during fungal growth, which adversely effects the economics of the method. Hybrid methods that combine the biological and other ones have also been proposed to achieve a higher pretreatment efficiency. For example, upon enzymatic hydrolysisebased bioethanol recovery from water hyacinth, the sugar yield of enzymatic hydrolysis using a hybrid pretreatment method (Echinodontium taxodii þ 0.25% H2SO4) was 1.12e2.11 times of that using an acid pretreatment method (Ma et al., 2010). Accordingly, the ethanol yield via the SHF process with S. cerevisiae achieved 0.192 g/g of dry matter for the case based on the hybrid pretreatment method, which was about 1.34 times of that for the case based on the acid pretreatment method. 4. Yeasts Yeasts are single-celled fungi that are capable of reproducing by budding or fission and forming spores which are not enclosed 111
112 Chapter 6 Waste-to-bioethanol in a fruiting body (Azhar et al., 2017). S. cerevisiae is the most widely used yeast for bioethanol production. It gains popularity due to various advantages including a high ethanol tolerance (up to 15% ethanol), potential to achieve a high ethanol concentration (10%e12%), safety in use, tolerating a wide range of pH conditions to prevent the growth of spoilage organisms, high sugar conversion ratios; however, S. cerevisiae is sensitive to a high temperature condition (>35 C), glucose repression, lactic acid bacteria and various compounds released during enzymatic hydrolysis, and it cannot ferment xylose and arabinose which account for 20% and 5% of the sugars in lignocellulosic biomass (Vertès et al., 2007). For example, furan derivatives (e.g., furfural and 5-hydroxymethylfurfural (HMF)) and phenolic compounds (e.g., vanillin and 4-hydroxybenzoic acid (HB)) released during the breakdown of sugars and lignin, respectively, were shown to be fermentation inhibitors and can reduce enzymatic and biological activity (Endo et al., 2008). The weakness of S. cerevisiae limits the potential of the yeast-based bioethanol process to achieve a higher efficiency and process robustness. Efforts have been made to explore the use of alternatives for yeast-based bioethanol production, but optimal selection of yeast is also contingent upon various factors such as lignocellulosic biomass types and process conditions. Bioethanol recovery from SSF processing of rice straw was experimentally compared among the use of three different types of microorganisms, i.e., Mucor indicus, Rhizopus oryzae, and Saccharomyces cerevisiae (Abedinifar et al., 2009). M. indicus and R. oryzae are zygomycetes filamentous fungi and can assimilate both hexoses and pentoses. Experiments showed that the use of M. indicus achieved ethanol, biomass, and glycerol yields of 0.36e0.43 g/g, 0.11e0.17 g/g, and 0.04e0.06 g/g, respectively, which were comparable with that (0.37e0.45 g/g, 0.04e0.10 g/g, and 0.05e0.07 g/g) of S. cerevisiae and higher than that of R. oryzae (0.33e0.41 g/g, 0.06e0.12 g/g, and 0.03e0.04 g/g). Pichia stipites is one type of pentose-utilizing yeasts and is featured by broad substrate specificity and no vitamin requirement for pentose utilization. Upon bioethanol recovery from Prosopis juliflora (Mesquite) pretreated using dilute H2SO4 (3.0%, v/v), the use of P. stipites to ferment hemicellulosic hydrolysate achieved an ethanol production and a yield of 7.13 g/ L and 0.39 g/g, which were smaller than that (18.52 g/L and 0.49 g/ g) of S. cerevisiae (Gupta et al., 2009). Five thermotolerant yeast strains Kluyveromyces marxianus IMB 1, IMB 2, IMB 3, IMB 4, and IMB 5 were tested for SSF processing of switchgrass at 45 C and compared with that of S. cerevisiae D5A at 37 C (Faga et al., 2010). It was shown that fermentation by K. marxianus strains
Chapter 6 Waste-to-bioethanol 113 ceased by up to 96 h, while S. cerevisiae continued for 7 d. At 96 and 120 h, IMB 3 achieved similar ethanol yields to S. cerevisiae D5A and greater yields than the other K. marxianus strains. The ethanol yields expressed as the percentage of the maximum theoretical yield ranged from 67.3% to 80.3% at 168 h for the K. marxianus strains as compared to 92.3% for S. cerevisiae. Finally, hybrid, genetically engineered or coculture of two yeast strains (e.g., fusing protoplast of Saccharomyces and xylosefermenting yeasts like P. tannophilus, Candida shehatae, and Pichia stipitis) can be applied to effectively utilize hexose and pentose sugars (Azhar et al., 2017). Table 6.2 summarizes the bioethanol production using different yeast strains. Table 6.2 Yeast strains used in bioethanol production (Azhar et al., 2017). Yeast strain Feedstock S. cerevisiae RL-11 Spent coffee grounds Sorghum stover Giant reed S. cerevisiae MTCC 173 S. stipitis CBS 6054 S. cerevisiae KL17 S. pombe CHFY0201 S. cerevisiae CHY1011 S. cerevisiae ZU-10 S. cerevisiae RPRT90 S. cerevisiae CHFY0321 (protoplast fusant) Galactose and glucose Cassava starch Cassava starch Corn stover Ipomoea carnea Cassava starch Sugar concentration Fermentation (g/L) condition 195.0 200.0 33.4 500.0 95.0 195.0 99.0 72.1 195.0 Ethanol Ethanol concentration productivity (g/L) (g/L/h) 30 C, 200 rpm, 48 h 30 C, 120 rpm, 96 h 30 C, 150 rpm, 96 h 30 C, 200 rpm, 28 h 11.7 0.49 68.0 0.94 8.2 0.17 96.9 3.46 32 C, 120 rpm, 66 h 32 C, 120 rpm, 66 h 30 C, 180 rpm, 72 h 30 C, 150 rpm, 28 h 32 C, 120 rpm, 65 h 72.1 1.16 89.1 1.35 41.2 0.57 29.0 1.03 89.8 1.38
114 Chapter 6 Waste-to-bioethanol 5. Further development The saccharification and fermentation-based production of second-generation ethanol is facing three major challenges (Bertrand et al., 2016). First, the feedstock acquisition and transportation, and enzymes used to hydrolyze the cellulose of feedstocks are generally expensive, rendering the whole development economically inviable. For example, for bioethanol production from sugarcane straw, it was shown that the costs of sugarcane straw and enzymes contributed most to the total production cost and accounted for 35.66% and 25.88% of the total cost of the ethanol plant, respectively (Mesa et al., 2017). The economic status could be worsened by the higher level of unpredictability in the process, feedstocks (e.g., composition and availability variations), and the market. Second, due to the recalcitrance of the second-generation feedstocks, harsh pretreatment is required and leads to the formation of inhibitory compounds against fermentation. Third, it is technically difficult to achieve high ethanol concentrations (4%e4.5% (w/v)) for reducing the cost of distillation and wastewater treatment, which requires high substrate loadings >15% and poses the risk of causing mixing and inhibition problems. The distillation is key to the economic profitability of bioethanol generation. A high ethanol concentration 4% (w/w) corresponding to a glucose yield of 8% (w/w) and a lignocellulose loading of 20% (w/w) for enzymatic hydrolysis was suggested considering the conversion of cellulose only (Modenbach & Nokes, 2013). The high loading hydrolysis implicitly implies the use of smaller equipment and/or fewer reactors in the process as well as less water usage (lower costs in the disposal of process wastewater), reducing the capital costs. However, due to the reduced enzymatic hydrolysis effectiveness at a high biomass solid loading condition, large amounts of enzymes are needed to obtain high ethanol concentrations. This increases the operating costs of the bioethanol production process. It is necessary to operate under optimal conditions (and reasonable enzymatic dosage) to achieve at least 40 g/L of ethanol production for reducing the cost of distillation (Guerrero et al., 2018). An additional method to improve the economic viability for producing secondgeneration bioethanol is based on the diversification of products (i.e. multigeneration), which can potentially offset the high steam energy consumption upon low ethanol concentrations.
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116 Chapter 6 Waste-to-bioethanol Choi, I. S., Lee, Y. G., Khanal, S. K., Park, B. J., & Bae, H.-J. (2015). A low-energy, cost-effective approach to fruit and citrus peel waste processing for bioethanol production. Applied Energy, 140, 65e74. https://doi.org/10.1016/ j.apenergy.2014.11.070 Choi, I. S., Wi, S. G., Kim, S.-B., & Bae, H.-J. (2012). Conversion of coffee residue waste into bioethanol with using popping pretreatment. Bioresource Technology, 125, 132e137. https://doi.org/10.1016/j.biortech.2012.08.080 Ebrahimi, F., Khanahmadi, M., Roodpeyma, S., & Taherzadeh, M. J. (2008). Ethanol production from bread residues. Biomass and Bioenergy, 32(4), 333e337. https://doi.org/10.1016/j.biombioe.2007.10.007 Endo, A., Nakamura, T., Ando, A., Tokuyasu, K., & Shima, J. (2008). Genomewide screening of the genes required for tolerance to vanillin, which is a potential inhibitor of bioethanol fermentation, In Saccharomyces cerevisiae. Biotechnology for Biofuels, 1(1), 3. https://doi.org/10.1186/1754-6834-1-3 Faga, B. A., Wilkins, M. R., & Banat, I. M. (2010). Ethanol production through simultaneous saccharification and fermentation of switchgrass using Saccharomyces cerevisiae D5A and thermotolerant Kluyveromyces marxianus IMB strains. Bioresource Technology, 101(7), 2273e2279. https://doi.org/ 10.1016/j.biortech.2009.11.001 Fernandes-Klajn, F., Romero-García, J. M., Díaz, M. J., & Castro, E. (2018). Comparison of fermentation strategies for ethanol production from olive tree pruning biomass. Industrial Crops and Products, 122, 98e106. https:// doi.org/10.1016/j.indcrop.2018.05.063 Guerrero, A. B., Ballesteros, I., & Ballesteros, M. (2018). The potential of agricultural banana waste for bioethanol production. Fuel, 213, 176e185. Gupta, R., Sharma, K. K., & Kuhad, R. C. (2009). Separate hydrolysis and fermentation (SHF) of Prosopis juliflora, a woody substrate, for the production of cellulosic ethanol by Saccharomyces cerevisiae and Pichia stipitis-NCIM 3498. Bioresource Technology, 100(3), 1214e1220. https:// doi.org/10.1016/j.biortech.2008.08.033 Hoekman, S. K., & Broch, A. (2018). Environmental implications of higher ethanol production and use in the U.S.: A literature review. Part II e biodiversity, land use change, GHG emissions, and sustainability. Renewable and Sustainable Energy Reviews, 81, 3159e3177. https://doi.org/10.1016/ j.rser.2017.05.052 Jørgensen, H., Vibe-Pedersen, J., Larsen, J., & Felby, C. (2007). Liquefaction of lignocellulose at high-solids concentrations. Biotechnology and Bioengineering, 96(5), 862e870. Jung, Y. H., Park, H. M., Kim, D. H., Yang, J., & Kim, K. H. (2017). Fed-batch enzymatic saccharification of high solids pretreated lignocellulose for obtaining high titers and high yields of glucose. Applied Biochemistry and Biotechnology, 182(3), 1108e1120. https://doi.org/10.1007/s12010-016-2385-0 Koppram, R., Nielsen, F., Albers, E., Lambert, A., Wännström, S., Welin, L., Zacchi, G., & Olsson, L. (2013). Simultaneous saccharification and cofermentation for bioethanol production using corncobs at lab, PDU and demo scales. Biotechnology for Biofuels, 6(1), 2. https://doi.org/10.1186/1754-6834-6-2 Laluce, C., Schenberg, A. C. G., Gallardo, J. C. M., Coradello, L. F. C., & Pombeiro-Sponchiado, S. R. (2012). Advances and developments in strategies to improve strains of Saccharomyces cerevisiae and processes to obtain the lignocellulosic ethanol A review. Applied Biochemistry and Biotechnology, 166(8), 1908e1926.
Chapter 6 Waste-to-bioethanol Lennartsson, P. R., Erlandsson, P., & Taherzadeh, M. J. (2014). Integration of the first and second generation bioethanol processes and the importance of byproducts. Bioresource Technology, 165, 3e8. Li, Z., Fei, B., & Jiang, Z. (2014). Study of sulfite pretreatment to prepare bamboo for enzymatic hydrolysis and ethanol fermentation. Chemistry and Technology of Fuels and Oils, 50(3), 189e196. Loaces, I., Schein, S., & Noya, F. (2017). Ethanol production by Escherichia coli from Arundo donax biomass under SSF, SHF or CBP process configurations and in situ production of a multifunctional glucanase and xylanase. Bioresource Technology, 224, 307e313. https://doi.org/10.1016/ j.biortech.2016.10.075 López-Abelairas, M., Lu-Chau, T. A., & Lema, J. M. (2013). Fermentation of biologically pretreated wheat straw for ethanol production: Comparison of fermentative microorganisms and process configurations. Applied Biochemistry and Biotechnology, 170(8), 1838e1852. https://doi.org/10.1007/ s12010-013-0318-8 Ma, F., Yang, N., Xu, C., Yu, H., Wu, J., & Zhang, X. (2010). Combination of biological pretreatment with mild acid pretreatment for enzymatic hydrolysis and ethanol production from water hyacinth. Bioresource Technology, 101(24), 9600e9604. https://doi.org/10.1016/j.biortech.2010.07.084 lez, E. (2017). Desirability function for Mesa, L., Martínez, Y., Barrio, E., & Gonza optimization of Dilute Acid pretreatment of sugarcane straw for ethanol production and preliminary economic analysis based in three fermentation configurations. Applied Energy, 198, 299e311. https://doi.org/10.1016/ j.apenergy.2017.03.018 Modenbach, A. A., & Nokes, S. E. (2013). Enzymatic hydrolysis of biomass at high-solids loadingsea review. Biomass and Bioenergy, 56, 526e544. Mosier, N., Wyman, C., Dale, B., Elander, R., Lee, Y. Y., Holtzapple, M., & Ladisch, M. (2005). Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology, 96(6), 673e686. https:// doi.org/10.1016/j.biortech.2004.06.025 Nishimura, H., Tan, L., Sun, Z.-Y., Tang, Y.-Q., Kida, K., & Morimura, S. (2016). Efficient production of ethanol from waste paper and the biochemical methane potential of stillage eluted from ethanol fermentation. Waste Management, 48, 644e651. https://doi.org/10.1016/j.wasman.2015.11.051 lez, R., Aguilar, C. N., MartínezPalacios, A. S., Ilyina, A., Ramos-Gonza ndez, J. L., Segura-Ceniceros, E. P., Gonza lez, M. L. C., Aguilar, M., Herna Ballesteros, M., & Oliva, J. M. (2019). Ethanol production from banana peels at high pretreated substrate loading: Comparison of two operational strategies. Biomass Conversion and Biorefinery, 1e10. Paschos, T., Louloudi, A., Papayannakos, N., Kekos, D., & Mamma, D. (2020). Potential of barley straw for high titer bioethanol production applying prehydrolysis and simultaneous saccharification and fermentation at high solid loading. Biofuels, 1e7. https://doi.org/10.1080/17597269.2020.1760688 Peng, L., & Chen, Y. (2011). Conversion of paper sludge to ethanol by separate hydrolysis and fermentation (SHF) using Saccharomyces cerevisiae. Biomass and Bioenergy, 35(4), 1600e1606. https://doi.org/10.1016/ j.biombioe.2011.01.059 Ray, M. J., Leak, D. J., Spanu, P. D., & Murphy, R. J. (2010). Brown rot fungal early stage decay mechanism as a biological pretreatment for softwood biomass in biofuel production. Biomass and Bioenergy, 34(8), 1257e1262. https://doi.org/10.1016/j.biombioe.2010.03.015 117
118 Chapter 6 Waste-to-bioethanol Rocha, N. R. de A. F., Barros, M. A., Fischer, J., Coutinho Filho, U., & Cardoso, V. L. (2013). Ethanol production from agroindustrial biomass using a crude enzyme complex produced by Aspergillus Niger. Renewable Energy, 57, 432e435. https://doi.org/10.1016/j.renene.2013.01.053 Sawin, J. L., Sverrisson, F., Seyboth, K., Adib, R., Murdock, H. E., Lins, C., Edwards, I., Hullin, M., Nguyen, L. H., Prillianto, S. S., Satzinger, K., Appavou, F., Brown, A., Chernyakhovskiy, I., Logan, J., Milligan, M., Zinaman, O., Epp, B., Huber, L., … Mastny, L. (2017). Renewables 2017 global status report. http://inis.iaea.org/search/search.aspx?orig_q¼RN:48058284. Srichuwong, S., Fujiwara, M., Wang, X., Seyama, T., Shiroma, R., Arakane, M., Mukojima, N., & Tokuyasu, K. (2009). Simultaneous saccharification and fermentation (SSF) of very high gravity (VHG) potato mash for the production of ethanol. Biomass and Bioenergy, 33(5), 890e898. https:// doi.org/10.1016/j.biombioe.2009.01.012 Stephen, J. D., Mabee, W. E., & Saddler, J. N. (2012). Will second-generation ethanol be able to compete with first-generation ethanol? Opportunities for cost reduction. Biofuels, Bioproducts and Biorefining, 6(2), 159e176. https:// doi.org/10.1002/bbb.331 Talekar, S., Patti, A. F., Vijayraghavan, R., & Arora, A. (2018). An integrated green biorefinery approach towards simultaneous recovery of pectin and polyphenols coupled with bioethanol production from waste pomegranate peels. Bioresource Technology, 266, 322e334. https://doi.org/10.1016/ j.biortech.2018.06.072 Vertès, A. A., Inui, M., & Yukawa, H. (2007). Alternative technologies for biotechnological fuel ethanol manufacturing. Journal of Chemical Technology and Biotechnology, 82(8), 693e697. https://doi.org/10.1002/jctb.1743 Wang, Y.-F., Tan, L., Wang, T., Sun, Z.-Y., Tang, Y.-Q., & Kida, K. (2017). Production of ethanol from kitchen waste by using flocculating Saccharomyces cerevisiae KF-7. Environmental Technology, 38(3), 316e325. https://doi.org/10.1080/09593330.2016.1192224
Waste-to-biodiesel 7 Abstract This chapter gives an overview of the potential and development of biodiesel as a transportation fuel. It reviews the main properties (viscosity, density, cetane number, calorific value, and combustion emissions) of biodiesel. It differentiates the definitions and features of four types of biodiesel (first, second, third and fourth-generation). The potential impacts of biodiesel on soil and water as compared to petro-diesel are also presented. For biodiesel production, this chapter focuses on the technology of transesterification and the influences of several process factors such as alcohol and waste feedstock selection, alcohol/oil molar ratio, and temperature are explained. A special attention is made to the use of catalysts to promote the transesterification-based biodiesel production and the advantages and disadvantages of different types of catalysts are compared. Keywords: Catalysts; Classification; Properties; Transesterification; Waste; Waste-to-biodiesel. 1. Introduction The transport sector accounts for roughly 20% of global energy consumption with a heavy reliance on gasoline and diesel (Prasad & Raturi, 2018). The associated emissions from the sector were around 13.5% of global GHG emissions in 2005 which grew to about 25% in 2016 (Dioha & Kumar, 2020). With an increasing number of road users, the emissions would be expected to keep growing in the coming decade when electric vehicles would have not been dominant. It was predicted that the emissions from the transport sector by 2050 would double that of the 2010 level (Marchal et al., 2011). The situation can be further complicated by the depletion of fossil fuel reserves. Biodiesel derived from biological sources such as animal fats, edible and nonedible oils, and waste cooking oils serves as a promising alternative to fossil fuels (in addition to biohydrogen as introduced in Chapter 4). It was predicted that the world’s biodiesel supply would reach 41.4 billion liters in 2025 that would be Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00010-4 Copyright © 2022 Elsevier Inc. All rights reserved. 119
120 Chapter 7 Waste-to-biodiesel 25% greater than the 2016 level (Rouhany & Montgomery, 2019). Biodiesel derived from waste biomass is generally featured by a lower carbon footprint, and the generation and application of biodiesel does not only help to tackle the challenge of SWM but also contribute to the development of a low-carbon transport fuel. Specifically, existing LCA showed that the carbon footprint of biodiesel could be 20%e80% lower than that of petro-diesel depending on the definition of system boundary (e.g., types of feedstocks and technologies) and application backgrounds (Rouhany & Montgomery, 2019). Biodiesel is biodegradable, safe to handle, and offers a cleaner combustion to conventional diesel with less emissions of particulates and carbon dioxide (Hasan & Rahman, 2017). Additionally, the use of biodiesel will also request minimal modifications to the existing diesel engines, making quick technology uptake possible and representing an easy and socially acceptable option. For the road transport in the European Union, the biodiesel consumption accounted for 78% of the energy content of all biofuels used, reaching 10.6 million tonnes of oil equivalent in 2011, followed by bioethanol (21%), vegetable oil (0.5%), and biogas (0.5%); the market share of biodiesel reached about 3.9% in 2012 (Malça et al., 2014). Biodiesel can be blended with diesel to adjust the quality of the fuel which is closely related to the content proportion (i.e., blending ratio) of biodiesel in a blend. The concentration of biodiesel within a diesel blend affects the consumption of petro-diesel. Specifically, the consumption of petro-diesel could be reduced by 95% and 19% upon the use of pure biodiesel (B100) and 20% concentration biodiesel (B20), corresponding to 78.45% and 15.66% of carbon saving, respectively (Sheehan et al., 1998). Biodiesel can be produced through various technologies including pyrolysis, microemulsion, and transesterification, among which transesterification serves as the most used method. For transesterification, biodiesel is produced from fats/oils through pretreatment and chemical reactions, usually in the presence of an alcohol and a catalyst to produce the required fatty acid methyl esters (FAMEs) and glycerol as a by-product. 2. Biodiesel properties Toward commercial application, the properties of biodiesel need to meet certain technical standards (e.g., ASTM D6751, EN 14214, and GB/T19147-2003). For example, in Europe, all commercial biodiesel must comply with the EN 14214 standards
Chapter 7 Waste-to-biodiesel which designate various property requirements such as a density between 0.86 and 0.90 g/cm3, a minimum flashpoint of 101 C, a kinematic viscosity 3.5e5 mm2/s at 40 C, and a maximum sulfur content of 10 mg/kg (Biofuelsystems.com, 2020). These properties determine the combustion characteristics, emissions, and performance of biodiesel engine applications. Biodiesel is generally more viscous than petro-diesel, meaning that adding biodiesel to petro-diesel increases the viscosity of the fuel and associated fuel pump power consumption, which adversely affects spray and atomization upon engine application. But normally, the viscosity of the blend would not change too much as the proportion of biodiesel increase up to 30% (Hasan & Rahman, 2017). The density of blends (e.g., ranging from 838 to 896 kg/m3) is higher than the original petro-diesel, suggesting a higher energy concentration. A high density will reduce the efficiency of atomization. The high density and viscosity properties of biodiesel cause technical problems upon their direct applications in engines. The cetane number of biodiesel is generally between 46.9 and 49.9, which is higher than that of petro-diesel, and is related to the long-chain hydrocarbon group composition of biodiesel (Hasan & Rahman, 2017). As a result, increasing the proportion of biodiesel in a blend will increase its cetane number, leading to a shortened ignition delay. The calorific value of biodiesel is generally lower than that of petro-diesel and is between 43 and 47 MJ/kg, therefore the calorific value of blend is lower than that of original petro-diesel ranging from 35.6 to 44.16 MJ/ kg. The flash point of fuels closely affects the safety of fuel handling, transport, and storage, and the flash point of biodiesel is roughly 50% higher than that of petro-diesel, meaning that biodiesel addition serves to enhance the safety of the fuel. The average flash point of biodieselediesel blends was reported to be 107.75 C (Hasan & Rahman, 2017). The composition of biodieselediesel blends affects their combustion emissions though the emissions are also related to the types of biodiesel and petro-diesel used. For example, the oxygen content of biodiesel is around 10 wt.%, which along with its lower carbon to hydrogen ratio leads to lower CO emission of combustion as compared to petro-diesel (Murillo et al., 2007). Increasing the biodiesel content in a blend led to an increase in NOx emission, and the NOx emission of pure biodiesel application could be 16% higher than petro-diesel. This is mainly caused by the higher oxygen content of biodiesel and a higher content of unsaturated compounds in biodiesel leads to lower NOx emissions (Xue et al., 2011). Additionally, the addition of biodiesel could significantly reduce the emissions of hydrocarbon, sulfur dioxides (low sulfur in biodiesel), 121
122 Chapter 7 Waste-to-biodiesel and PMs (Hasan & Rahman, 2017). Specifically, the PM and hydrocarbon emissions of engine application of pure biodiesel were found to decrease by 87.7% and 89.5% as compared to petrodiesel (Xue et al., 2011). The lower PM emissions for biodiesel are related to its higher oxygen content and cetane number, and lower contents of aromatic and sulfur compounds. It is worth noting that the PM emissions are also affected by the engine load and speed: the larger the engine load or lower engine speed, the higher the PM emissions. For biodiesel, the emission of sulfur dioxide is generally negligible due to its low sulfur content. 3. Biodiesel classification Depending on the sources of feedstocks, biodiesel can be classified into first, second, third, and fourth generation, respectively. The first-generation biodiesel is produced from food and edible feedstocks including rapeseed, soybean, oil palm, canola, etc., which are featured by good availability and easy conversion procedures. The first-generation biodiesel accounts for roughly 80% of global biodiesel production (Rouhany & Montgomery, 2019). In the European Union, nearly 80% of biodiesel is produced from rapeseed which is favored by the climate of the European Union and the biodiesel’s good cold flow properties and oxidation stability (Malça et al., 2014). But similar to the case of bioethanol production, the use of the edible feedstocks threatens human food consumption and thus affects food security, sparking the “food versus fuel” debate. Hence, the feedstocks suffer from the disadvantages of affecting food supply, high costs, and limited cultivation areas. It was reported that the cost of raw feedstock materials accounted for 70%e80% of the total production cost in the production of biodiesel from edible oils (Gui et al., 2008). Existing studies have also shown that the production process (the occupation and preparation of cultivation land with the use of fertilizers and pesticides) contributed most to the environmental impact for producing rapeseed, sunflower, and soybeans (Requena et al., 2011). Actually, the land use is the most affected impact category for sunflower requiring more land per kg of seed production as compared to rapeseed and soybeans. For rapeseed-based biodiesel production, cultivation accounted for 40% of the abiotic depletion impact in Germany and 98% of the eutrophication impact in Spain, with the use of fertilizers and relevant soil emissions being the major contributors (Malça et al., 2014). Agricultural practice-related soil carbon changes critically affect the global warming potential (GWP) of rapeseed-based biodiesel,
Chapter 7 Waste-to-biodiesel while fossil methanol usage has a major impact on abiotic depletion. All of these stimulated the search for nonedible feedstocks to achieve reduced impacts and disruption to the global food supply. The second-generation biodiesel is produced from various nonedible feedstocks such as crops, nonedible oils (e.g., neem oil, jatropha oil, nagchampa oil, calophyllum inophyllum oil, rubber seed oil, etc.), and other nonedible biomass (e.g., wood and husk). Jatropha could thrive on the land which is unsuitable for growing edible crops and under various harsh climates such as low precipitation, and thus has a low impact on the plantation of edible crops. The second-generation feedstocks have the advantages of being more environmentally friendly, reduced production costs, and mitigating food inequality, as compared to the first-generation feedstocks. However, the yields of main nonedible plants like jatropha, jojoba, and karanja are limited, requiring extensive plantation on the land that is potentially in competition with the plantation of the first-generation feedstocks (Mofijur et al., 2020; Singh et al., 2020). Hence, feedstocks that are more accessible are further demanded. The third-generation biodiesel is produced from microalgae and waste oils (e.g., waste fish oil, waste animal tallow oil, and waste cooking oil) which offer such benefits as less requirements on farmland and food supply, and great availability, effectively mitigating the major problems of the first- and secondgeneration biodiesel. The use of waste oil for biodiesel production serves to tackle the challenge of SWM towards the development of a circular economy. This is particularly applicable to countries or regions where the land for cultivation is limited such as Japan and Singapore. Upon the comparison between jatropha oil and waste cooking oil for biodiesel production, the former had greater environmental impacts (climate change, human health, and ecosystem quality) than the latter due to the contributions of cultivation of nonedible jatropha. However, the waste cooking oil-based biodiesel production requires various chemicals and significant energy for the pretreatment of raw oil and its environmental impacts are also subject to the collection mechanism of waste cooking oil (Sajid et al., 2016). Waste cooking oil gathers water, soluble compounds, and impurities during the dehydration cooking process, which accelerates the hydrolysis of triglycerides to form free fatty acids (FFAs) at high temperatures (Foteinis et al., 2020). Being a biodiesel feedstock, microalgae usually cultivated in photobioreactors have the potential to achieve a yield per plantation area that is 15e300 times of that for a traditional biodiesel crop (Mofijur et al., 2020). The major disadvantage of the 123
124 Chapter 7 Waste-to-biodiesel third-generation biodiesel production is associated with high initial production and setup costs against its economic viability. The fourth-generation biodiesel refers to photobiological solar fuels and electro-fuels. The production of the biodiesel aims to achieve carbon negativity (CO2 trapping and storage) using materials like biomass in a process similar to that of the secondgeneration biofuels. A typical feedstock can be the genetically modified algal species which are used in photobiological solar cells where solar energy is converted to usable biodiesel. The processes arrest CO2 using techniques like oxy-fuel combustion at each stage of the production followed by geo-sequestration of CO2 in, e.g., saline aquifers, gas fields, or old oils (Karmakar & Halder, 2019). The advantages of the fourth-generation biodiesel include high lipid content, CO2 absorbing ability and energy content, and rapid growth rate (Singh et al., 2020). However, the technologies are still at the infancy stage and are calling for additional research and development. 4. Biodiesel impacts on soil and water Petro-diesel exhibits toxicity at concentrations above 3% (w/ w) in nonadapted aerated soil; meanwhile, biodiesel exhibits no toxicity up to a concentration of 12% (w/w) and can be relatively easily biotransformed as determined by the measurement of the respiration of soil microorganisms as well as the activity of soil dehydrogenases (Lapinskiene_ et al., 2006). A series of CO2 evolution tests showed that the biodegradability of biodiesel was >98% after 28 days, as compared to 50% for petro-diesel and 56% for gasoline (Pasqualino et al., 2006). The addition of biodiesel to petro-diesel and gasoline increases the biodegradability of the fuels by means of co-metabolism. This suggests that biodiesel can be utilized as a natural solvent or an energizer to help microbes break up and decontaminate the soil polluted with petro-diesel. Biodiesel is 15e25 times more water soluble than petro-diesel, posing a potential risk of water pollution upon their diffusion though soil to underground water or streams (He et al., 2007). Experiments based on aerobic seawater microcosms showed that biodiesel were degraded at roughly the same rate as n-alkanes, and more rapidly than other hydrocarbon components. It was suggested that FAMEs in biodiesel mixtures did not affect the evaporation rates of spilled petroleum hydrocarbons but might stabilize oil droplets in the water column and facilitate associated transport mechanisms. This enhances the dissolution of
Chapter 7 Waste-to-biodiesel petroleum hydrocarbons and affects the transport, weathering rate, and ecological impact of spilled biodiesel. 5. Biodiesel production There are various biodiesel production technologies such as pyrolysis, microemulsification, and transesterification. The principles of pyrolysis have been introduced previously. For example, biodiesel can be derived from animal fatecontaining waste cooking oil (with high FFA contents) using the pyrolysis technology involving decarboxylation (Ito et al., 2012). Specifically, triacylglycerols could be decomposed between 360 and 390 C with the formation of fatty acids via the cleavage of the ester bond, short-chain hydrocarbons and fatty acids. Increasing the residence time to up to 120 min at 420 C decreased the yields of fatty acids to about 15 wt.% and increased the yields of hydrocarbons to about 60 wt.%. Adding activated carbonesupported palladium catalysts to the process further promoted decarboxylation and led to a high yield (85 wt.%) of hydrocarbons that were comparable to light oil. The derived biodiesel includes paraffins and olefins which have higher calorific values and better low-temperature properties than FAMEs as derived using transesterification. Microwave-assisted pyrolysis has been applied to recover biodiesel from sewage sludge and the recovered fuel was of a LHV in the range of 32e34 kJ/g which was about 30% lower than that of petro-diesel (Capodaglio et al., 2016). Transesterification is considered as the most effective technology for producing the biodiesel with properties close to that of petro-diesel and will be the focus of this chapter. Transesterification is a process which involves a series of reversible reactions where alcohol reacts with triglycerides, the main component of fats and oils to produce ester (biodiesel) and glycerol. Triglyceride ðTGÞ þ ROH 4 Diglyceride ðDGÞ þ RCOOR1 Diglyceride ðDGÞ þ ROH 4 Monoglyceride ðMGÞ þ RCOOR2 Monoglyceride ðMGÞ þ ROH 4 Glycerol þ RCOOR3 (7.1) For the transesterification process, a short-chain, low boiling point alcohol (e.g., methanol and ethanol) is needed to generate biodiesel. The reaction efficiency of the process is affected by various factors such as the alcohol/molar ratio, the use and type of catalyst (e.g., homogeneous or heterogeneous catalysts, or biocatalysts), temperature, water content, FFA content, and pressure (Caetano et al., 2019). 125
126 Chapter 7 Waste-to-biodiesel As the shortest chain alcohol, methanol has been widely used for the transesterification process to generate FAMEs. As compared to the other alcohols that are of longer chain lengths, methanol is more reactive and leads to a higher conversion for the transesterification process. For example, the transesterification of sunflower oil with methanol achieved a conversion of 91% compared to 81% and 75% for the processes with ethanol and n-propanol, respectively (Sankaranarayanan et al., 2011). An optimal Karanja biodiesel yield of 91.05% was achieved using methanol and 1.22% (w/w) KOH as a catalyst for 90.78 min at 66.8 C, which was higher than that (77.4%) using ethanol and 1.21% (w/w) KOH for 120 min at 61.3 C (Verma & Sharma, 2016). As compared to methanol, ethanol has the advantages of being less toxic and recoverable from sugar- or starch-rich biomass (i.e., bioethanol as introduced in Chapter 6), making it a relatively lowcarbon option for transesterification. For countries with limited petroleum resources and refineries, bioethanol offers an opportunity for biodiesel development based on the local bioresources that are relatively accessible. The technical feasibility of bioethanolbased transesterification has been widely verified with the achievement of an ethyl esters yield greater than 90% (Brunschwig et al., 2012). One of the major issues related to bioethanol-based biodiesel production is on the adverse effects of the relatively highwater content in bioethanol on the transesterification process, limiting the use of 95% bioethanol at industrial scales (Brunschwig et al., 2012). For alkaline-catalyzed transesterification with bioethanol, secondary hydrolysis and saponification reactions are caused by the presence of water, which reduces the process yield and leads to the consumption of the catalyst; hence, a maximum water content of 0.1% is recommended for biodiesel production. Heterogeneous catalysts used in the process can also be inhibited by water or lixiviation which is caused by the dissolution of metal cations or active acid sites in the reaction medium and contaminates the biodiesel product (Brunschwig et al., 2012). Finally, the cost of bioethanol is generally higher than commercial ethanol, adversely affecting the economic viability of bioethanol-based production at an industrial scale. Higher molecular weight alcohols such as butanol have the advantages of greater miscibility with lipid and higher boiling points as compared to lower molecular weight alcohols (Lotero et al., 2005). The former advantage facilitates the development of a less pronounced initial mass-transfer-controlled regime, while the latter makes higher reaction temperature possible, which is a favorable condition for acid-catalyzed transesterification to achieve higher reaction rates. The waste cooking oil from, e.g., restaurants, often contains animal fats which (1) promote the formation of long-chain
Chapter 7 Waste-to-biodiesel saturated FAMEs, and (2) comprises significant FFAs (Ito et al., 2012). The long-chain saturated FAMEs have a high freezing point and can deteriorate the low temperature properties of derived biodiesel. The FFAs also tend to form a soap upon alkalicatalyzed biodiesel production, adversely affecting the overall yield of the process. The alcohol/oil molar ratio is one of the most critical factors of the process. Stoichiometrically, a ratio of 3 (i.e., 3 mols of alcohol for 1 mol of triglyceride) is needed for the reaction, but higher ratios (i.e., excess of alcohol) are normally adopted to increase the yield of the process (Caetano et al., 2019). For higher molar ratios such as 6 and 10, it was found that the conversion of the triglyceride from jatropha oil could proceed to completion within 30 min at varied temperature conditions (45 and 60 C) while the conversion was 90% under the stochiometric ratio of 3 (Kumar et al., 2011). A series of transesterification experiments (65 C and 3 wt.% catalysts) with a base catalyst (K2SiO3/AlSBA-15) and jatropha oil showed that the yield of biodiesel increased from 29.5% to 65.6% as the molar ratio increased from 3:1 to 15:1 with a yield of 95% for the ratio of 9:1; however, further increasing the ratio from 9 to 15 did not significantly chang the yield (Wu et al., 2014). Increasing the process temperature will increase the rate of reaction since a higher temperature increases the kinetic energy of molecules and lowers the oil viscosity, improving the mixing between catalysts and alcohol molecules (Takase et al., 2014). However, an over high temperature (e.g., >45 C for alkaline-catalyzed transesterification of edible Canola oil and >60 C for used frying oil) would decrease the ester yield due to the acceleration of the saponification (fat, oil, or lipid converted into soap and alcohol by aqueous alkali) of triglycerides (Leung & Guo, 2006). The effect of temperature on the yield of biodiesel might also subject to the influence of other factors, depending on the types of feedstocks. For example, for acid-catalyzed primary sludge transesterification, there was a significant interactive effect between temperature, acid concentration, and methanol-to-sludge mass ratio on the yield of FAMEs, while for secondary sludge, the FAME yield was significantly affected by the independent effects of the factors (Mondala et al., 2009). A sufficiently long reaction time is needed to ensure the completion of the process; however, excess reaction time would promote the reverse reaction of transesterification (i.e., hydrolysis of esters), reducing the biodiesel yield (Leung & Guo, 2006). Various types of catalysts have been used for the transesterification process and their advantages and disadvantages are listed in Table 7.1. Basic homogenous catalysts (e.g., sodium hydroxide (NaOH) and sodium hydroxide (KOH)) are in the same phase as 127
128 Chapter 7 Waste-to-biodiesel Table 7.1 Advantages and disadvantages of different types of transesterification catalysts for waste cooking oil (Lam et al., 2010). Catalyst Advantages Disadvantages Homogeneous base catalyst (1) Very fast reaction rate (2) Requirements of mild reaction condition and less energy intensive (3) Low cost and wide availability (1) Faster reaction rate than acidcatalysts (2) Requirements of mild reaction condition and less energy intensive (3) Easy separation of catalyst from product (4) Highly reusable and regenerable (1) Less sensitive to FFA and water content in the oil (2) Preferred for low-grade oil (3) Simultaneous esterification and transesterification (4) Requirements of mild reaction condition and less energy intensive (1) Simultaneous esterification and transesterification (2) Easy separation of catalyst from product (3) Highly reusable and regenerable (1) Sensitive to FFA content in the oil (soap formation for >2 wt.% FFA content and biodiesel yield reduction with high purification requirements) Heterogeneous base catalyst Homogeneous acid catalyst Heterogeneous acid catalyst Enzyme (1) Less sensitive to FFA and water content in the oil (2) Preferred for low-grade oil (3) Low reaction temperature (4) Simple purification (1) Ambient air poisoning (2) Sensitive to FFA content in the oil (soap formation for >2 wt.% FFA content and biodiesel yield reduction with high purification requirements) (3) Product contamination by the leaching of catalyst active sites (1) Very slow reaction rate (2) Corrosion on reactor and pipelines (3) Separation of catalyst from product being challenging (1) Relatively high costs (2) Requirements of high temperature, high alcohol to oil molar ratio, and long reaction time (3) Energy intensive (4) Product contamination by the leaching of catalyst active sites (1) Very slow reaction rate (2) High costs (3) Deactivation by alcohol the reacting materials and are most used catalysts for transesterification. Upon the comparison of sodium methoxide, potassium methoxide, sodium hydroxide, and potassium hydroxide for methanolysis of sunflower oil in a batch stirred reactor (methanol/oil molar ratio ¼ 6:1, 65 C, and catalyst concentration ¼ 1 wt.%) followed by separation and purification, nearly 100 wt.% biodiesel purity was achieved for all catalysts but nearly
Chapter 7 Waste-to-biodiesel 100 wt.% biodiesel yields (the ratio between the weight of methyl ester generated and the weight of oil used) were only achieved with the methoxide catalyst (Vicente et al., 2004). Sodium hydroxide and sodium hydroxide achieved biodiesel yields of 86.7% and 91.7%, respectively, and the losses of biodiesel yields were associated with the dissolution of methyl ester in glycerol and triglyceride saponification. With the potassium hydroxide catalyst at 1.0 wt.% concentration, a mixing intensity of 600 rpm, a reaction temperature of 65 C, and a methanol/oil molar ratio of 6:1, an optimal biodiesel yield of 95%e96% was achieved from rapeseed oil in 2 h (Rashid & Anwar, 2008). Suboptimal conditions with higher or lower potassium hydroxide and methanol concentrations than the optimal values led to either incomplete reaction or the formation of soap. Existing studies recommended that the preferred concentration of basic homogeneous catalyst should range from 0.005 to 1 wt.% (Marchetti et al., 2007). The presence of high water or FFA (>1 wt.%) contents in feedstocks such as waste cooking oil and tallow can reduce the effect of catalyst and the process efficiency. Alkaline catalysts can react with FFAs to form soaps, adversely affecting the separation of biodiesel, glycerine and wash water, which is a less problem for homogeneous acid catalysts. Acid catalysts such as hydrochloric acid (HCl) and sulfuric acid (H2SO4) can handle cases of higher FFA or water contents in oils and can simultaneously promote esterification and transesterification to consume triglycerides and FFAs. The major disadvantages of the acid catalysts are on their slow reaction rates and corrosiveness, posing challenges to the operation of system. A combined process using base and acid catalysts was also developed (Canakci & Van Gerpen, 1998). The process consisted of an acid-catalyzed pretreatment stage to convert FFAs into alkyl-esters via esterification, followed by an alkaline-catalyzed transesterification to convert the rest of the mono-, di-, and triglycerides into alkyl-esters. A pilot plant based on the process was demonstrated to treat soybean oil, yellow grease (9 wt.% FFAs), and brown grease (40 wt.% FFAs). As shown in Table 7.1, for homogeneous catalysts, separation and purification stages are needed post-biodiesel production, incurring significant costs and the production of a significant amount of wastewater. Typical heterogeneous solid catalysts include alkali-metal carbonates, alkaline-earth carbonates, and alkaline-earth metal oxides for base ones and WO3/ZrO3, Zeolite Y (Y756), carbonbased catalyst from starch, H3PW12O40 $ 6H2O (PW12), Zr0.7H0.2PW12O40 (ZrHPW), and SO2 4 /SnO2eSiO2 for acid ones (Lam et al., 2010). The activity of the heterogeneous catalysts is 129
130 Chapter 7 Waste-to-biodiesel closely related to various factors such as specific surface area, pore size and volume, and the active site concentration on the surface of the catalysts, among which the active site concentration is considered to be most important (Zabeti et al., 2009). A higher specific surface area and active site concentration tend to promote the reactions of the process. One of the major problems about the use of heterogeneous catalysts is the formation of three phases with alcohol and oil, resulting in diffusion limitations and lowering the reaction rate; in this case, catalyst supports (e.g., alumina, silica, and zinc oxide) can be used to mitigate the mass transfer limitation. To prepare the active sites, various procedures and conditions including washing and drying are needed. For Ca/ Al composite oxideebased alkaline catalyst, the temperature of calcination might affect the activity of the catalyst (Meng et al., 2013). The catalyst calcined at 600 C showed the highest activity with >94% yield of FAMEs upon its application to the transesterification of rapeseed oil at 65 C and a methanol/oil molar ratio of 15:1. The performance of the catalyst was not only related to the specific surface area but also the crystalline structure that served to improve the catalytic activity due to its synergistic effect with CaO. The high temperature condition might cause burning and the compaction of active sites. The high calcination temperature requirement also suggested that the use of the catalyst might involve significant energy input considering a typical 8-h duration of the calcination preparation process. The catalysts can be impregnated and modified with metal ions to improve their performance, which can be influenced by the type of metal ion/salt used. For example, for CaO impregnated with various alkali metal ions (Liþ, Naþ, and Kþ), a maximum strength was found for lithium carbonate-impregnated CaO (Kumar & Ali, 2010). The nanocrystalline Liþ-impregnated CaO was used to catalyze the transesterification of used cottonseed oil and achieved a complete transesterification process in 45 min at the conditions of 65 C, and moisture and FFA contents of 0.26 and 0.31 wt.%. The catalyst was also applicable for the transesterification of cotton seed oil with a moisture content up to 15 wt.% water. Chemical catalystebased transesterification processes suffer from the problems of a huge amount of wastewater generation and difficulty in glycerol recovery which incur significant economic and environmental expenses to biodiesel production (Lam et al., 2010). Enzyme catalysis is receiving increasing attention due to its apparent advantages of less by-product generation, easy product recovery, mild reaction conditions, being insensitive to high FFA contents, and high reusability. The lipase from Mucor
Chapter 7 Waste-to-biodiesel miehei was found to be most effective at converting triglycerides to biodiesel with short-chain alcohols (i.e., methanol and ethanol), and achieved a 97% yield on various types of feedstocks such as tallow, rapeseed, and soybean oil (Nelson et al., 1996). Water did not degrade lipase catalysis but improved the efficiency of the enzyme. The lipase from Candida antarctica was most efficient for converting triglycerides with secondary alcohols to generate branched alkyl esters. However, enzyme catalysis suffers from the problems of a relatively high cost, slow reaction rate, and enzyme deactivation, limiting its large-scale industrial applications for the time being. Finally, regardless of the use of the catalysts, the efficiency of the transesterification reaction can be further enhanced using microwave, supercritical conditions, ultrasounds, and/or membranes. 6. Whole process A complete biodiesel production process generally includes raw material collection and transport, raw material pretreatment, preparation of catalyst, transesterification, separation and filtration, purification and drying, with biodiesel and glycerol being the final products. For feedstocks such as waste cooking oil with high FFA contents, a pretreatment stage, e.g., using sulfuric acid and methanol to lower the FFA contents needs to be included (Chung et al., 2019). A methanol/FFA molar ratio of 19.8:1 is generally adopted by biodiesel producers for FFA esterification in the United States, while generally, it was suggested that the 19.8:1 methanol/FFA molar ratio only applied to an FFA range of 15%e25% (Chai et al., 2014). The pretreatment could achieve an FFA conversion of w80% (Photaworn et al., 2017). The method of gravity settling combined with centrifugation has also been used to remove solid impurities, water, and watersoluble compounds in waste cooking oil (Foteinis et al., 2020). Prior to the pretreatment process, oil decantation and filtration might be needed to remove impurities, followed by thermal treatment to reduce the water content in the oil. The sources of the catalysts vary and is a critical factor upon the evaluation of the techno-economic feasibility and environmental impacts of biodiesel devellopment. For example, calcium carbonate (CaCO3) can be prepared by crushing readily available limestone, while calcium oxide (CaO) can be sourced from waste eggshell using dedicated calcination processes or high temperature thermal treatment involving the use of milling machine and fired furnace (Tan et al., 2015). 131
132 Chapter 7 Waste-to-biodiesel As an exaxmple, a typical process of acid-catalyzed transesterification of mutton tallow consists five substages: transesterification, methanol recovery, acid removal, water washing, and glycerol purification (Faleh et al., 2018). The first substage (80 C and 400 kPa) involves a transesterification reactor where methanol and sulfuric acid catalyst are fed into together with recycled methanol and mutton tallow that is preheated to 60 C in a heat exchanger. In the second substage, the transesterification products are fed to a distillation column to separate the methanol from other components for recycling. In the third substage, the other components are sent to an acid removal reactor after being cooled in a Cooler to 60 C. A neutralization reactor is used to neutralize sulfuric acid using calcium oxide. CaSO4 $ 2H2O is formed by reaction between the produced water and CaSO4 and is removed using a gravity separator. In the substage 4, water at 25 C is added to separate the FAMEs from the glycerol. In the last substage, a distillation column is used to purify glycerol. Other transesterification configurations are also available. For example, Foteinis et al. (2020) considered a two-step transesterification process made of acid-catalyzed esterification and alkaline catalyst transesterification, which is suitable for feedstocks of high FFA contents such as waste cooking oil (w2e7 wt.%). In the first step, the esterification of FFAs is achieved through the acidcatalyzed process with excess methanol in a continuous plug flow reactor. In the second step, the transesterification of the esterified low-acidity oil occurs with excess methanol and base catalysts (e.g., KOH) in two continuous stirred tank reactors in series. Typical products of the process include CH3OH, potassium sulfate (K2SO4), biodiesel distillation residue, soap, catalyst, and wastewater. The produced biodiesel needs to be purified to meet the standards for diesel engine applications. The purification process involves a series of washing procedures with acidulation and vacuum drying to reduce the amount of water, CH3OH, and catalyst and with vacuum distillation to remove heavy components such as polymerized FAMEs or traces of sulfur. To recover the methanol in the water, a rectification process for water evaporation followed by distillation could be used. Base-catalyzed transesterification of waste cooking oil tends to form soap from the reaction of FFAs and the base catalyst. This reduces the yield of biodiesel and poses a significant challenge to the separation of biodiesel from glycerol. Glycerol can be considered as a food source for animals or value-added chemical for, e.g., cosmetics and soap production. For dry purification that utilizes less water, a magnesol powder can be considered and the crude biodiesel is simply pumped through a wash column containing the powder, removing any unreacted glycerides and other impurities.
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Chapter 7 Waste-to-biodiesel Sajid, Z., Khan, F., & Zhang, Y. (2016). Process simulation and life cycle analysis of biodiesel production. Renewable Energy, 85, 945e952. Sankaranarayanan, T. M., Pandurangan, A., Banu, M., & Sivasanker, S. (2011). Transesterification of sunflower oil over MoO3 supported on alumina. Applied Catalysis A: General, 409, 239e247. Sheehan, J., Camobreco, V., Duffield, J., Graboski, M., Graboski, M., & Shapouri, H. (1998). Life cycle inventory of biodiesel and petroleum diesel for use in an urban bus. Golden, CO (United States): National Renewable Energy Lab.(NREL). Singh, D., Sharma, D., Soni, S. L., Sharma, S., Sharma, P. K., & Jhalani, A. (2020). A review on feedstocks, production processes, and yield for different generations of biodiesel. Fuel, 262, 116553. Takase, M., Zhang, M., Feng, W., Chen, Y., Zhao, T., Cobbina, S. J., Yang, L., & Wu, X. (2014). Application of zirconia modified with KOH as heterogeneous solid base catalyst to new non-edible oil for biodiesel. Energy Conversion and Management, 80, 117e125. Tan, Y. H., Abdullah, M. O., Nolasco-Hipolito, C., & Taufiq-Yap, Y. H. (2015). Waste ostrich-and chicken-eggshells as heterogeneous base catalyst for biodiesel production from used cooking oil: Catalyst characterization and biodiesel yield performance. Applied Energy, 160, 58e70. Verma, P., & Sharma, M. P. (2016). Comparative analysis of effect of methanol and ethanol on Karanja biodiesel production and its optimisation. Fuel, 180, 164e174. Vicente, G., Martınez, M., & Aracil, J. (2004). Integrated biodiesel production: A comparison of different homogeneous catalysts systems. Bioresource Technology, 92(3), 297e305. Wu, H., Zhang, J., Liu, Y., Zheng, J., & Wei, Q. (2014). Biodiesel production from Jatropha oil using mesoporous molecular sieves supporting K2SiO3 as catalysts for transesterification. Fuel Processing Technology, 119, 114e120. Xue, J., Grift, T. E., & Hansen, A. C. (2011). Effect of biodiesel on engine performances and emissions. Renewable and Sustainable Energy Reviews, 15(2), 1098e1116. Zabeti, M., Daud, W. M. A. W., & Aroua, M. K. (2009). Activity of solid catalysts for biodiesel production: A review. Fuel Processing Technology, 90(6), 770e777. 135
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Waste-to-biochar 8 Abstract This chapter highlights the significance of waste-to-biochar development by reviewing the recent environmental and energy applications of biochar. It explains the technical and process principles and influential factors of three major biochar production technologies, i.e. torrefaction, pyrolysis, and gasification. Some typical reactor designs (e.g., masonry and metal kilns, retorts, auger type, and fluidized bed type for pyrolysis, and fixed bed, fluidized bed, and entrain flow for gasification) are reviewed. The yields and physicochemical properties of biochar from the different technologies are summarized. Finally, the design of wasteto-biochar production is discussed based on energy, environmental, and economic criteria. Keywords: Biochar yield; Gasification; Pyrolysis; System design; Torrefaction; Waste-to-biochar. 1. Introduction Biochar, a carbon-rich solid material, is receiving increasing attention due to its significant potential for environmental and energy applications. Extensive research has been carried out to understand the role of biochar in mitigating climate change. The carbon sequestration potential of biochar upon soil application stems from its carbon-rich feature (Lehmann, 2007). The recalcitrant carbon in biochar is stable in soil and this makes carbon storage in soil feasible for an extended period of time, leading to carbon sequestration. It was estimated that w200 million tonnes of carbon sequestration can be achieved upon biochar soil application and 2.2e2.93 tonnes of CO2 could be sequestered for every tonne of biochar applied to soil (Galinato et al., 2011). It is evidenced that biochar addition can reduce the direct emission of various GHGs such as CH4 and N2O. For example, it was reported that biochar soil addition reduced CH4 and N2O by 26.18% and 62%e98% as compared to the control scenarios without biochar amendment (Li et al., 2013; Qin et al., 2016). Additionally, indirect carbon mitigation can be resulted from biochar’s role in promoting soil quality and biomass yields via improved Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00009-8 Copyright © 2022 Elsevier Inc. All rights reserved. 137
138 Chapter 8 Waste-to-biochar nutrients and water availability, the mineralization, fixation, and transformation of organic nitrogen, and enhanced aggregate formation. For example, a biochar addition of 20 tonnes/ha enhanced the wheat production by 17%e36% over 6 years (Wang et al., 2018), and wheat straw biochar application in a rice paddy improved the rice yield by about 25% and reduced the GHG balance by 53.9%e62.8% (Zhang et al., 2015). Up to 50% of N2O was reduced when biochar was applied to soil at the rate of 30 tonnes per hectare in New Zealand and similarly, a reduction of 40%e51% in N2O was observed in paddy soil in China. Biochar soil application has a negative emission potential of 0.7 GtCeq/Year with a lower impact on land, water use, nutrients, albedo, energy requirement, and costs as compared to other negative emission technologies such as direct air capture, enhanced weathering, bioenergy with carbon capture and storage, and afforestation/deforestation (Smith, 2016). Typical agrarian benefits from biochar soil application include increasing nutrient (e.g., potassium, calcium, zinc, etc.) retention and availability, contamination reduction, increasing water holding capacity and cation exchange capacity, and enhancing mycorrhizal activities, which serve to improve soil productivity ultimately (Sohi et al., 2010). Biochar has high inorganic matter contents (e.g., carbon, nitrogen, and phosphorus), indicating that it has the potential to increase soil nutrition status. It can increase the extractable phosphorus in the soil solution and influence the element’s solubility and cation interaction activity. The combined soil application of biochar and other soil amendments such as manure and composts can improve the efficiency of nutrient uptake and reduce nutrient leaching. For example, the combined application of orchid pruning biochar and inorganic fertilizers onto a vineyard in central Italy decreased the soil bulk density and increased water retention capabilities of soil, leading to a significant increase in grape productivity in terms of yield, weight, and size (Genesio et al., 2015). The porous structure of biochar can serve to increase the hydrophilicity and surface area, and thus the water retention and cation exchange capacities of soil particles. Biochar pores provide a benign environment for microorganisms and improve the fungi and bacterial growth and activities in soil. A second attractive feature of soil biochar application is closely associated with biochar’s ability to mitigate various soil quality problems such as soil degradation (e.g., runoff, nitrogen and phosphorus losses) and contamination (e.g., toxic metals and pesticides) as the result of the prominent physicochemical properties (e.g., high porosity, high specific surface area, aromatic
Chapter 8 Waste-to-biochar carbon structure, and alkalinity) of biochar. For example, it was found that adding rice straw biochar reduced the annual sediment yield by 11% and annual soil runoff by 19%e28% (Li et al., 2017). Bean stalk and rice straw biochar addition reduced the Cd and Zn concentrations in roots and shoots by 30%e75% and 43%e79%, and 25.0%e44.1% and 19.9%e44.2%, respectively (Zheng et al., 2015). Biochar particles can adsorb soil pollutants such as polycyclic aromatic hydrocarbon and heavy metal, mitigating soil contamination. Mitigating soil contamination reduces the risk of human exposure to harmful contaminants via food intake. It is worth noting that the actual effects of biochar on soil and plant productivity depend on the types of biochar, crop, and soil and local environmental conditions (Biederman & Harpole, 2013). Recently, the focus of biochar research and development has also been paid to exploring the possibility of applying biochar for nonsoil energy and environmental applications (You et al., 2017). Specifically, biochar has been used as a low-cost, environmentally friendly catalyst or catalyst support in various thermochemical processes. It can be applied to reduce the content of tar generated during the process of gasification and as mentioned in Chapter 3, tar formation poses a significant risk to reduce the stability and efficiency of gasification operation. Biochar can be used as a good catalyst support or a direct catalyst for tackling the problem of tar formation during the process of gasification. Upon its use as a catalyst support, biochar can improve the dispersion of reactant molecules into the internal structure of catalysts due to its high surface area and porosity. Upon its use as a catalyst directly, biochar exhibits tar removal performance comparable to some of the conventional catalysts such as olivine and nickel catalysts, while the performance can be further improved by applying active metals (e.g., nickel) to the surface of biochar (Qian & Kumar, 2015). Compared to the conventional catalysts used in the thermochemical processing of waste, the use of biochar for the thermochemical processes improves the efficiency of waste utilization and offers a possibility for completing the close-loop of waste processing (e.g., gasification biochar supplied back to the process to mitigate its tar formation). However, as the catalyzing process goes on, the surface area and porosity of biochar normally degrade, adversely affecting its catalyzing performance. Hence, similar to the conventional catalysts, it is key to improve the stability and regenerating potential of biochar toward practical, large-scale commercialization. Biochar can be applied as adsorbents for removing heavy metals and organic pollutants in environments because of its 139
140 Chapter 8 Waste-to-biochar high surface area, porous structure, and surface functional groups. The adsorption capacity of biochar is generally positively associated with its porosity, density of functional groups (serving as active sites), and specific surface area (You et al., 2017). Upon further activation using either physical (e.g., steam and ozone) or chemical methods (e.g., KOH, NH3, and ZnCl2), biochar can be upgraded to activated carbon with much enhanced porosity and surface area characteristics. The properties of activated biochar depend on the conditions of activation such as the types of activating agents used and temperature (Zhang et al., 2014). Additionally, it has been evidenced that biochar has the potential to serve a cost-effective green sorbent for CO2 capturing (Dissanayake et al., 2020). The CO2 adsorption capacity of biochar is positively associated with micropore areas and volumes which are controlled by the types of feedstocks and process conditions of biochar production. Recent studies also find the potential of biochar in electrochemical applications as exemplified by direct carbon fuel cells (DCFCs), electrocatalysts, and supercapacitors because of its good surface texture features and high conductivity and electrochemical activities. DCFCs have the great potential to achieve a high electrical efficiency and reliability. The feature of high contents in carbon and carboneoxygen groups makes biochar an appropriate material for DCFC applications to produce valuable gases. The power density of DCFCs is positively related to the carbon content, surface area, and porosity of biochar and these properties favor the reactivity of anode electrochemical reactions. One of the major technical challenges against the scaleup applications of DCFCs is associated with the ash residuals upon the consumption of biochar carbon which degrade the surface features and reduce the associated charge transfer (You et al., 2017). Pine wood biochar after sonication and heating treatment was used as a manganese oxide electrocatalytic support for microbial fuel cells and achieved power densities comparable to Vulcan carbon (Huggins et al., 2015). Generally, uniform biochar sizes will be beneficial for the electrochemical applications of biochar, suggesting the requirement of some pretreatment activities such as sieving and milling prior to the biochar production process. Biochar has been applied as an additive to promote the anaerobic digestion processing of organic waste for higher quality biogas production. Specific benefits of biochar addition into anaerobic digestion include mitigating the inhibition phenomena, promoting syntrophic metabolisms, and increasing the buffer capacity of the process (maintaining the alkalinity of
Chapter 8 Waste-to-biochar anaerobic digestion media), serving as support media for biomass immobilization, and enhancing nutrient retention and carbonto-nitrogen ratio. These serve to improve the quality of biogas (e.g., increasing methane content and lowering carbon dioxide content) and digestate (e.g., enriched macro- and micronutrients (e.g., K, Ca, Fe, etc.)) generated during anaerobic digestion and thus the economic viability of the process (Chiappero et al., 2020). This application provides opportunities for designing hybrid waste treatment processes by combining biochar production processes such as pyrolysis and gasification with anaerobic digestion and the hybrid processes are generally featured by a higher level of waste resource utilization. 2. Waste-to-biochar technologies Biochar can be generated from the thermochemical processing of waste, and there are three main biochar production technologies, i.e., torrefaction, pyrolysis, and gasification, depending on the conditions of temperature and the quantity of oxygen used. The latter two have been discussed in the previous chapters and will be introduced in this chapter with a specific focus on biochar production. 2.1 Torrefaction Torrefaction mainly occurs between 200 and 300 C in an inert or oxidative environment with a relatively long residence time (20e120 min) and it generates biochar (up to 97 wt.%) as the primary product and torrefied volatiles (liquid and gas) as a byproduct that can be combusted to supply energy for the torrefaction process (Wang et al., 2020). Some typical components of liquid products include water, organics (e.g., sugars, acids, alcohols, furans, ketones etc.), and lipids (e.g., terpenes, phenols, waxes, tannins, etc.). Water is produced via the thermal decomposition and evaporation of the moisture content in the feedstock, while lipids and organics, e.g., present in the original waste are released upon heating or devolatilization and carbonization (Peduzzi et al., 2014). Typical gas components include H2, CO, CO2, CH4, light aromatics (e.g., toluene and benzene), etc. Dehydration and decarboxylation are the main degradation reactions during the process. Torrefaction has been commonly applied to pretreat feedstocks for improving the heating value and hydrophobicity of feedstock for subsequent thermochemical processing. This pretreatment can help to reduce the cost and energy 141
142 Chapter 8 Waste-to-biochar consumption of feedstock transportation and extend the storage life of feedstock. Moreover, the formed biochar is of higher energy density as compared to the original waste and can be more easily pulverized for power generation. Depending on the heat transfer mechanism, the reactors of torrefaction can be classified into direct and indirect heating designs: in a direct heating reactor, the feedstock directly contacts with the heating media, while the feedstock is heated via the reactor walls in an indirect heating reactor (Chew & Doshi, 2011; Peduzzi et al., 2014). Depending on the movement of feedstock in the reactor, they can be classified into fluidized bed and moving bed. For fluidized bed reactors (similar to the ones for pyrolysis and gasification as described previously), an upward flowing gas stream is used to suspend feedstock particles, which significantly enhance the interaction between the feedstock particles and sand bed materials, promoting heat transfer and leading to a relatively uniform temperature distribution. It was reported that Topell’s commercial fluidized bed torrefaction reactor could enhance the volumetric caloric density (GJ/m3) of feedstock by 70% and generated biochar that could be cofired with coal up to a fraction of 80% (Chew & Doshi, 2011). In the fluidized bed system as illustrated in Fig. 8.1, an iron mesh basket is used to hold the feedstock, while aluminum oxide used as the sand bed material. Nitrogen gas is used to maintain an inert environment. An electrically heated thermal oxidizer is used to burn out the volatiles. A moving bed reactor is featured by using mechanical components such as multi-hearth, screw, and auger to transport the feedstock in the reactor. TORSPYD as developed by Thermya, France, was shown to achieve fast and continuous conversion of biomass into dry, nonhygroscopic, dense, and easy-to-grind biochar (Ratte et al., 2011). This reactor based on the principle of Scaled Pyrolytic Distillation involves a series of organic solid distillation. Feedstock particles are introduced from the top and move down the column gradually in the environment of increasing temperature and a countercurrent hot gas stream introduced at the base of the column. During the process, water is firstly released followed by organic matters and 4% of the biochar produced is supplied back into the system to fulfill the energy requirement of the process. It was reported that TORSPYD could produce the biochar that had 95% initial energy content and 90% mass of its original feedstock, and a net calorific value of around 20e21 MJ/kg (Chew & Doshi, 2011). TorrCoal as developed by Torr-Coal Group used a revolving drum oven as the reactor and the biochar produced achieved 70% mass
Chapter 8 Waste-to-biochar Figure 8.1 The illustration of a bubbling fluidized bed torrefaction system (Tumuluru et al., 2012). and 90% energy content of original feedstock and a LHV of 18e20 MJ/kg (Chew & Doshi, 2011). During the process, the produced biochar went through subsequent stages of size reduction and sieving followed by dechlorination and desulfation which served to remove 90% of chlorine and 30% of sulfur in biochar. The quality of biochar is generally evaluated in terms of various factors including the carbon content, pH value, specific surface area, porosity, and nutrients. The quality of torrefied biochar is mainly controlled by the condition of temperature with residence time being a less impactful factor. The condition of higher temperature increases the carbon content while reducing the hydrogen content of biochar. Accordingly, increasing the torrefaction temperature will increase the degree of conversion and reduce the hydrogen-to-carbon and oxygen-to-carbon ratios to the levels similar to coal. Higher temperatures also decrease the yield of biochar, which, for lignocellulosic biomass, is related to the decomposition of cellulosic fraction at higher temperature (up to 300 C) (Pala et al., 2014). In general, oxidative torrefaction leads to lower mass and energy yields of biochar as compared to nonoxidative 143
144 Chapter 8 Waste-to-biochar torrefaction because oxidative reactions promote the rate of carbon degradation (Brachi et al., 2019). Moreover, the production of torrefaction depends on the types of feedstocks. For example, oxidative torrefaction achieved better biochar physical properties (mass density, energy density, hardness, and durability) than nonoxidative torrefaction for nonwoody olive pomace pellets, while similar phenomena were not observed for woody fir pellets (Brachi et al., 2019). Table 8.1 summarized the biochar production of torrefaction. The mass and energy yields of biochar from torrefaction range from around 50%e97% and 50% e99%, respectively, and the carbon and hydrogen contents of torrefaction biochar range from around 35%e70% and 3.3% e7.3%, respectively, across a wide range of feedstocks. For efficient treatment of the feedstocks of high moisture contents such as microalgae, the technology of wet torrefaction has been proposed and is based on the use of hot compressed water and a pressure condition that is higher than the saturated vapor pressure for the torrefaction temperature. As compared to conventional torrefaction, this technology has the advantages of saving the energy loss of water vaporization, improving the heat value, energy yield, and hydrophobicity of biochar, and promoting the ash removal from feedstock (Gan et al., 2020). The products of wet torrefaction include biochar as the primary product and sugar and sugar-based derivatives as by-products. The technology has been further adapted by, e.g., applying microwave irradiation for heating and applying an acid solution (e.g., HCl and H2SO4) as the medium. The acid solutionebased process was found to improve the carbon content and heating value of torrefied biochar. 2.2 Pyrolysis The biochar production of pyrolysis is closely related to the heating rate and residence time of the process with a lower heating rate and longer residence time favoring biochar production. Extended residence time promotes secondary charring reactions of volatile vapor and increases the yield of biochar. Pyrolysis can be classified into slow pyrolysis, and fast or flash pyrolysis which are featured by a heating rate and residence time range of <50 C/ min and 5e30 min, and 10 to 1000 C/s and <2 s, respectively (Roy & Dias, 2017). Accordingly, higher biochar production could be achieved for slow pyrolysis and is in the range between 21 and 58 wt.% (average 32 wt.%) as compared to fast or flash pyrolysis for which the biochar production is in the range between 5.1 and 56 wt.% (average 25 wt.%). The carbon content of slow
Chapter 8 Waste-to-biochar 145 Table 8.1 The yield and physicochemical properties of biochar from torrefaction (Wang et al., 2020). Feedstock Biochar composition composition (%) (%) Temperature Feedstock ( C) Mass yield (%) Energy yield (%) C H C H Pine chips Stem wood Olive pomace pellets Raw pellets Sugarcane bagasse Corn stover Peat Rice straw Bamboo Empty fruit bunches Parts of the plant Spent coffee grounds Medicine residue Microalga residue Licorice residue Biomass chips Spruce stem Spruce stump Spruce bark 225e300 89e52 94e71 47.21 6.64 49.47e63.67 6.07e5.58 260e310 97.1e45.9 98.8e62.8 50.3 6.2 51.4e69.2 5.9e5 200e250 79.92e53.04 94.5e68.4 54.93 6.33 57.31e63.61 6.33e4.68 200e250 79.92e53.04 93.65e49.85 50.91 6.25 52.22e66.65 6.06e3.34 200e300 79e52 98e79 32.5 5.01 34.5e50.3 4.98e3.4 200e300 97.1e57.4 98.52e84.41 e e 45.8e58.7 5.5e4.7 230e270 200e300 210e300 200e300 82e70 94.35e70.49 95.34e59.98 87.5e67.4 91e87 98.52e84.41 97.36e75.11 90.3e70.7 52.09 42.57 46.12 43 5.79 5.84 6.11 6 59e65.3 45.06e50.94 48.54e61.23 46.2e59 5.49e5.26 5.46e4.9 6.08e4.8 5.5e5.1 250e300 77e63 88e80.5 46.5 5.1 56.4e65.6 6.0e5.9 200e300 97e62.82 98.07e78.84 52.99 7.29 53.94e68 7.28e6.85 200e300 92.7e63 97.93e79.87 52.86 7.22 54.42e68.22 7.09e6.62 200e275 89.35e62.64 91.98e79.45 36.49 6.12 41.27e61.63 5.95e5.38 210e280 92e51 99.3e72.9 42.5 6.41 44.5e58 6.41e5.74 230e290 86e43.1 90.5e60.5 43.7 6.05 45.1e54.1 5.8e4.35 225e300 92.4e68.6 93.05e79.88 48.78 6.27 50.06e62.17 6.09e5.72 225e300 92.7e55.6 94.26e64.76 47.38 6.49 49.21e60.21 6.21e5.89 225e300 90.4e63.0 96.93e74.8 49.09 6.06 55.4e67.34 5.53e3.89
146 Chapter 8 Waste-to-biochar pyrolysis biochar ranges from 45% to 93% (average 73%), while the carbon content of fast pyrolysis biochar ranges from 31% to 82% (average 65%). Table 8.2 and Table 8.3 list the biochar production from slow and fast pyrolysis processes, respectively. The carbon content of biochar is positively related to pyrolysis temperature, residence time, and heating rate (Wang et al., 2020). Higher pyrolysis temperature promotes the release of volatiles from the feedstock, increasing the carbon content of biochar, while lower heating rates allow sufficient heat conduction and promote the carbon deposition reaction, leading to higher biochar production. The biochar production of pyrolysis is also associated with the size of feedstock particles and the use of catalyst, with larger feedstock particles and a higher ratio of catalyst to feedstock favoring the production of biochar. Additionally, a higher lignin content in the feedstock leads to a higher yield of biochar as compared to the case of a lower content. Table 8.2 The yield and physicochemical properties of biochar from slow pyrolysis (Wang et al., 2020). Parameters Composition (%) Feedstock T ( C) RT (min) HR ( C/min) Yield (%) C H N S Cow manure Pine wood Coffee husk Neem press seed cake Wheat straw Palm shell Hinoki cypress Lignin Algae Walnut shell Rubber wood Redcedar sapwood Redcedar heartwood Corn straw 300 300 350 450 120 60 30 60 10 17 0.5 20 58.0 43.7 39.82 38.3 51.30 71.3 69.96 52.39 4.52 4.7 3.63 2.57 1.70 e 3.58 2.23 e e 0.24 0.12 475 500 500 500 500 500 500 500 180 60 60 480 60 60 20 30 8 10 10e15 5 10 15 10 6 e 35.5 23.3 45.69 w32 w30 24.25 30.9 69.9 60.12 85.79 85.9 45.26 77.97 87.17 85.8 2.5 9.21 3.89 3.56 1.24 3.22 1.23 2.4 e 0.42 0.23 1.23 2.57 1.13 0.40 0.35 e 0.92 e 0.121 e e e 0.35 500 30 6 21.0 88.88 2.6 0.35 0.4 550 Several 30 w24 92.83 1.49 0.84 0.06
Chapter 8 Waste-to-biochar 147 Table 8.3 The yield and physicochemical properties of biochar from fast pyrolysis (Wang et al., 2020). Composition (%) Feedstock Temperature ( C) Yield (%) C H N S O Wheat straw Sweet sorghum Corn stalks Yellow poplar Corn cobs Pine sawdust Rice husk Pine sawdust Douglas fir Lvory nut Bamboo Rice husk Brown macroalga 500 500 550 500 500 550 500 500 480 500 500 550 375 26 23.8 e 5.1 18.9 e 26 e 11.2 15.82 24.4 38.86 56.08 56 69.03 72.28 76.3 77.6 70.68 45.2 70.68 75.8 69.59 81.7 44.73 30.67 2.3 2.78 3.14 2.3 3.05 3.6 1.5 3.6 1.56 2.93 3.7 1.80 2.72 1.0 0.59 1.09 0.7 0.85 2.4 0.4 2.4 0.33 e e 0.73 2.09 e e 0.9 e 0.02 0.21 e 0.21 0.13 e e e e e 276 22.47 20.7 5.11 23.11 1.7 23.11 19.57 18.31 e 7.69 64.53 Toward practical implementation of pyrolysis-based biochar production, it is important to consider valorizing the two other products of the process, i.e., bio-oil (mainly oxygenated organic compounds such as esters, acids, ketones, phenols, etc.) and syngas (e.g., CO, H2, CH4, etc.). From an energy perspective, the products can be used to generate heat to sustain the endothermic pyrolysis process and improve its energy efficiency. For example, in the fast pyrolysis system proposed by Patel et al. (2019) (Fig. 8.2(a)), around 11% of the produced bio-oil was used to generate electricity to sustain the system operation while the combustion of the noncondensable gases from the fast pyrolysis process supported 75% of the heat demand of the system with the remaining 25% from the use of propane. From an economic perspective, the upgrading and applications of the other products provide an additional income source for biochar production development. For example, it was shown that the sale of the heat generated from bio-oil and noncondensable gases could largely offset the utility expenses for a slow pyrolysisebased biochar production system (Fig. 8.2(b)) (Cheng et al., 2020). From an environmental perspective, their applications can serve to improve the environmental footprint (e.g., carbon footprint) of biochar production systems
148 Chapter 8 Waste-to-biochar Figure 8.2 (a) A fast pyrolysis system proposed by Patel et al. (2019), and (b) a slow pyrolysis system proposed by Cheng et al. (2020). due to their renewability and low-carbon features. For example, LCA showed that the carbon credits for displacing coal-based electricity generation using the energy produced and the credits for biochar soil application for fast pyrolysis were 1.242 and 0.193 tonne CO2-eq., respectively, while the corresponding numbers
Chapter 8 Waste-to-biochar 149 were 1.174 and 0.427 tonne CO2-eq., respectively, for slow pyrolysis (Kung et al., 2013). Conventionally, kilns have been widely used to produce biochar and can be classified into masonry and metal kilns and retorts. Masonry kilns can be further classified into “hottail” kilns, slope type kilns, surface kilns, masonry rectangular kilns, Missouri kilns, and Argentine kilns. “Hot-tail” kilns (Fig. 8.3(a)) are made of bricks laid with clay and sometimes with noble additives such as sugar or sodium silicate to allow expansion and contraction (expansiveness) (Rodrigues & Junior, 2019). Instead of having a chimney, there are holes on the wall and base of the kiln for the adjustment of gas input and output. The pyrolysis process is monitored visually based on the coloration and quantity of smoke from the holes. The “hot-tail” kilns are of relatively low capital and operation and maintenance costs but suffer from the problems of limited temperature control and mechanization, being slow in cooling, and potential air pollutant emission through the holes. Figure 8.3 Schematic illustrations of different types of kilns: (a) “hot-tail” kilns, (b) slope kilns, (c) beehive kilns, (d) rectangular kilns (Rodrigues & Junior, 2019).
150 Chapter 8 Waste-to-biochar The slope kilns (Fig. 8.3(b)) made of masonry normally have one to three chimneys, one door, and multiple holes. Like the “hot-tail” kiln, the pyrolysis process inside the kiln is monitored by observing the color of the smoke from the chimneys (Rodrigues & Junior, 2019). The slope is built as part of the kiln which saves the construction material and cost, and there is good insulation and low thermal loss, leading to homogeneous, good quality biochar production. Major disadvantages of slope kilns include the necessity to find a firm and appropriate terrain and being slow in cooling. The beehive kilns (or surface kiln in Fig. 8.3(c)) made of masonry are similar to “hot-tail” kilns but have one to six chimneys to improve the thermal conditions and gas flows inside the kiln (Rodrigues & Junior, 2019). There are multiple air inlet holes on the wall and along the circumference of the kiln dome. The pyrolysis process can be monitored via observing the color of the smoke from the chimney and the kiln external temperature. Mechanization can be installed to facilitate biochar discharge. The design of beehive kilns can be improved by adjusting the position and number of holes, the dimension and position of chimney, etc. Beehive kilns with an external combustion chamber for the heat supply do not need air inlet holes and the air inlet control is done through the chamber, improving the space utilization and process productivity. The rectangular kilns (Fig. 8.3(d)) have been developed with the installation of mechanization for loading and unloading and of surveillance units for temperature monitoring (Rodrigues & Junior, 2019). However, the kilns are still experiencing difficulties in temperature control, limiting the yields and quality of biochar. Additional disadvantages of rectangular kilns include significant maintenance requirements and a long cooling cycle. Improvements have been made to better monitor and control the process temperature with the design of openings, use of sensors, and use of a gas burner to generate heat for the process (e.g., drying pretreatment). Metal kilns have been developed to improve the quality of biochar production and is able to treat feedstocks with a greater variation in properties. They also have a better process efficiency as compared to the earth kilns. Sangsuk et al. (2018), designed and studied a metal kiln with heat distribution pipes to improve the quality of biochar and biooil production. The kiln was made of sheet steel and was covered with fiberglass and galvanized steel as an insulator. Six exhaust chimneys were connected to a condenser unit for bio oil condensation. 1446 kg of bamboo (moisture content (MC) of 30%) were used to generate 315 kg
Chapter 8 Waste-to-biochar biochar, 12 kg ash (ASH), and 900 L bio oil with the heat supplied using 279 kg firewood. The composition and heating value of biochar were 8.5% (MC), 7.7% (VM), 81% (FC), and 11.3% (ASH), and 30,347 kJ/kg, respectively. Retorts are a type of kiln that can be designed in a semibatch or continuous mode. A typical retort design is the container retort (Rodrigues & Junior, 2019). The container retort consists of a metallic cylinder (container with holes at the bottom serving as hot gas inlets) that is placed in a thermal insulation system (masonry or a metal structure with ceramic-fiber) using a hoisting mechanism. There is a combustion chamber at the bottom of the system to supply the process heat by combusting, e.g., forest residues. One of the possible improvements made to the container retort is about creating and connecting lateral fissures to an exhaust system and applying valves to control the airflow and water for cooling. The gas product of the process can be utilized to generate electricity or to dry the feedstock. Major advantages of container retorts include efficient carbonization and cooling, reduced contamination of biochar by earth, and easy integration for heat and electricity recovery and process monitoring (Rodrigues & Junior, 2019). However, container kilns have such disadvantages as relatively high capital and operation and maintenance costs and limited scales. Biochar production is nowadays often produced using modern reactors of relatively high technology and control levels. Some of the typical pyrolysis reactor types include the auger type, fluidized bed type, ablative type, cyclonic type, rotating cone type, and entrained flow type. It was recommended that fluidized bed and auger reactors will be of greater potential for widespread commercial applications considering two major factors, i.e., technology strength and market attractiveness (Fig. 8.4). This chapter will focus on the two types of reactors. Auger reactor applies a screw rotating in an enclosed shell to convey particles into a reaction vessel and to evacuate the solid residues of the process. In the reactor, feedstock particles can be well mixed and a close interaction between the particles and the heating wall can be achieved. This enables good axial dispersion, and the feedstock particles experience relatively uniform heating. Fig. 8.5 shows a typical single-auger reactor where the wall is heated. The residence time can be controlled by adjusting the speed of the auger and the flow rate of inert gas. Some of the major advantages of auger reactors include (a) flexible control of mass flow rate and residence time to allow slow and fast pyrolysis, respectively, (b) complete and uniform heat transfer by using heat carriers and/or catalysts, (c) facilitating solid 151
152 Chapter 8 Waste-to-biochar Figure 8.4 Strength and attractiveness of several pyrolysis technologies (Campuzano et al., 2019). Figure 8.5 A schematic diagram of an auger type pyrolysis system (Campuzano et al., 2019).
Chapter 8 Waste-to-biochar fraction separation, (d) being suitable for processing waste mixture and feedstock of different typology and sizes, (e) nondilution of gas product, (f) being suitable for decentralized, mobile applications, (g) a low specific reactor size and high energy efficiency, (h) reduced inert gas usage, and (i) efficient feedstock particle treatment (Campuzano et al., 2019). On the other hand, the major disadvantages include (a) risks of plugging and mechanical wear and corrosion especially under a high temperature condition, (b) a risk of flowinduced segregation reducing the effectiveness of mixing, and (c) limited mixing at the radial direction. To improve the energy efficiency of the process, the gas product can be combusted to supply heat to sustain the pyrolysis process. For the pyrolysis based on single auger reactors, the enthalpy (indicating the heat requirement of the pyrolysis process) was in the range between 1.1 and 1.9 MJ/kg for a variety of feedstocks such as waste tires, cedar pine, willow, and bamboo (Martínez et al., 2013; Yang et al., 2013). This wide variation is attributed to the influences of the variations in temperature, system pressure, heating rates, residence time, carrier gas mass flow, reactor malfunctions (heat losses), etc. A twin-screw configuration with two intermeshing screws has been developed to improve the effectiveness of the mixing between feedstock particles and heat carriers (e.g., steel balls, silicon carbide, fine or coarse sand, and ceramic pellets) and to promote feedstock devolatilization. The twin-screw configuration also serves to mitigate the problems of clogging and the problems related to secondary reactions due to prolonged residence time. Fluidized bed reactors can be used to achieve relatively uniform heating via the formation of a particle suspension and enhanced gasesolid contacting. For the fluidized bed reactors, the enthalpy was reported to range from 0.8 0.2 to 1.6 0.3 MJ/kg-dry for such feedstocks as oak, oat hulls, pine, and corn stover (Daugaard & Brown, 2003). As a result, the biochar produced using fluidized bed reactors can be of high quality. Biochar recovered from sawdust of radiata pine using a bench-scale fluidized bed pyrolysis systems (Fig. 8.6) achieved an HHV of 26 MJ/kg (Kang et al., 2006). The sawdust was fed into the reactor using two screw feeders. The fluidized bed with a bed material of sand was heated using an electric heater. The biochar was collected using a separation system consisting of a cyclone for biochar particles >10 mm and three cylindrical ceramic filters for particles down to around 1 mm. A series of quenching columns (a minimum temperature of 30 C) were used for cooling the gas product. 153
154 Chapter 8 Waste-to-biochar Figure 8.6 A schematic diagram of a bench-scale fluidized bed pyrolysis system (Kang et al., 2006). Chlorella vulgaris (a type of microalgae) remnants after solvent extraction were used in a bench-scale fluidized bed fast pyrolysis system for biochar production (Wang et al., 2013). The system was heated using a clamshell heater and used silica particles with an average diameter of 0.55 mm as the sand bed material. Two gas cyclones were used to collect the biochar, while bio oil was collected using two condensers (20 C), followed by an electrostatic precipitator, and another condenser (1 C). A biochar yield of 31 wt.% was achieved, representing 36% of the energy content of the feedstock and the biochar was rich in inorganic content such as potassium, phosphorous, and nitrogen. 2.3 Gasification Conventionally, gasification has been mainly applied to produce syngas. Recent findings of biochar research and
Chapter 8 Waste-to-biochar 155 applications, particularly the significant carbon saving potential upon biochar soil applications, stimulate the development of the gasification systems featured by a balance of syngas and biochar production for optimal economic viability and carbon saving potential (You et al., 2017). The quality and relative yields of syngas and biochar can be adjusted by controlling the key gasification process parameters such as temperature, types and flow rate of gasifying agent (i.e., ER), and reactor design. Additionally, they can also be affected by the types and composition of feedstocks. Fig. 8.7 shows that the biochar yield of gasification generally is in the range of 0%e20%, less than that (w20%e50%) of pyrolysis. Moreover, increasing gasification temperature generally decreases the yield of biochar due to the enhanced oxidation reactions consuming more carbon. However, as compared to pyrolysis, gasification can potentially achieve a higher rate of energy recovery and a reasonably high yield of biochar. For example, experiments conducted using 150e350 kWe downdraft gasifiers at 900e1100 C and rice husk as the feedstock achieved a biochar yield of ca. 35% (Shackley et al., 2012). Small- to medium-scale gasification-based biochar production systems (a few kW to 3 MW) have been constructed with fixed bed reactors for systems <200 kW and bubbling fluidized bed or circulating fluidized bed reactors for 200e3000 kW (You et al., 2019). The economic viability of gasification-based biochar systems needs to be improved by enhancing the valorization of biochar. One decentralized gasification system in suburban Figure 8.7 Gasification biochar yields with respect to temperature, the types of feedstocks, and gasifying agents. The red (gray in print version) dash lines denote the biochar yield range of pyrolysis (You et al., 2017).
156 Chapter 8 Waste-to-biochar Beijing was built to supply cooking gas (4.50 102 million m3/ year (LHV ¼ 14.7 MJ/m3)) to 387 households with biochar (27 tonnes/year) being a by-product for sale. However, the cost of raw material (woody biomass) and the relatively low system efficiency made the development economically unsustainable. 3. Biochar system design The potential of biochar valorization is linked to the profitability of the biochar production systems which can be promoted by diversifying the income sources of waste treatment. It was shown that a biochar price of 14e112 USD/tonne could reduce the electricity cost by 2.5%e20% and increase the net present value by 0.336e3.752 million USD for a 5 MWe gasification system (for CNY to USD exchange rate of 0.14 in 2019) (Huang et al., 2019). The governmental subsidies and the actual “market” values of the products recovered from waste also define the income flow of a biochar production system. A high economic value in biochar is needed to cover the cost of biomass collection and to ultimately promote sustainable biomass use, especially when the opportunity cost of labor in biomass/waste collection is considered (You et al., 2020). A previous study showed that a biochar price as high as 238 USD/tonne was needed to ensure that a straw pyrolysisebased biocharelectricity production was as profitable as a gasification-based electricity production (Clare et al., 2015). The biochar price definition becomes even more tricky when the deployment of biochar production happens in rural areas because the pricing of biochar needs to balance among the agronomic impact of biochar, the economic benefit received by rural residents (who contribute waste biomass for biochar production) from the deployment, and the economic viability of biochar production systems (You et al., 2020). Insufficient economic stimulus might lead to limited participation of rural households, rendering the operation of the system unsustainable. Meanwhile, the agronomic effects of biochar are closely associated with the physicochemical properties of biochar and thus with the conditions of thermochemical processes. Hence, the optimization of the quality and yield of biochar through accurate process control will be needed for defining a biochar price that caters to the needs of different stakeholders. Finally, as illustrated by Fig. 8.8, the practical implementation of biochar production systems is affected by their economic, energy, and environmental aspects which are correlated with each other. For each aspect, various factors need to be considered for a practical design, rendering the analysis based on the three aspects complex.
Chapter 8 Waste-to-biochar 157 Figure 8.8 A summary of the economic, energy, and environmental aspects of biochar productions systems (You et al., 2020). References Biederman, L. A., & Harpole, W. S. (2013). Biochar and its effects on plant productivity and nutrient cycling: A meta-analysis. GCB Bioenergy, 5(2), 202e214. Brachi, P., Chirone, R., Miccio, M., & Ruoppolo, G. (2019). Fluidized bed torrefaction of biomass pellets: A comparison between oxidative and inert atmosphere. Powder Technology, 357, 97e107. Campuzano, F., Brown, R. C., & Martínez, J. D. (2019). Auger reactors for pyrolysis of biomass and wastes. Renewable and Sustainable Energy Reviews, 102, 372e409. Cheng, F., Luo, H., & Colosi, L. M. (2020). Slow pyrolysis as a platform for negative emissions technology: An integration of machine learning models, life cycle assessment, and economic analysis. Energy Conversion and Management, 223, 113258. Chew, J. J., & Doshi, V. (2011). Recent advances in biomass pretreatmenteTorrefaction fundamentals and technology. Renewable and Sustainable Energy Reviews, 15(8), 4212e4222. Chiappero, M., Norouzi, O., Hu, M., Demichelis, F., Berruti, F., Di Maria, F., Masek, O., & Fiore, S. (2020). Review of biochar role as additive in anaerobic digestion processes. Renewable and Sustainable Energy Reviews, 131, 110037. Clare, A., Shackley, S., Joseph, S., Hammond, J., Pan, G., & Bloom, A. (2015). Competing uses for China’s straw: The economic and carbon abatement potential of biochar. Gcb Bioenergy, 7(6), 1272e1282.
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160 Chapter 8 Waste-to-biochar Zhang, A., Bian, R., Li, L., Wang, X., Zhao, Y., Hussain, Q., & Pan, G. (2015). Enhanced rice production but greatly reduced carbon emission following biochar amendment in a metal-polluted rice paddy. Environmental Science and Pollution Research, 22(23), 18977e18986. Zhang, X., Zhang, S., Yang, H., Feng, Y., Chen, Y., Wang, X., & Chen, H. (2014). Nitrogen enriched biochar modified by high temperature CO2eammonia treatment: Characterization and adsorption of CO2. Chemical Engineering Journal, 257, 20e27. Zheng, R., Chen, Z., Cai, C., Tie, B., Liu, X., Reid, B. J., Huang, Q., Lei, M., _ _ E. (2015). Mitigating heavy metal accumulation into Sun, G., & Baltrenait e, rice (Oryza sativa L.) using biochar amendmentda field experiment in Hunan, China. Environmental Science and Pollution Research, 22(14), 11097e11108.
System design: costebenefit analysis 9 Abstract This chapter explains the mathematical principles and factors of costbenefit analysis and covers various main cost and benefit components associated with waste-to-resource development. It provides a systematic database about the major cost and benefit components of different types of waste-to-resource systems. This chapter emphasizes the importance of waste collection and transportation to the economics of waste-toresource development and explains two existing models of waste collection and transportation. It concludes on reviewing the existing studies about the economic feasibility of the different types of waste-toresource systems and highlighting the critical factors associated with the economic feasibility. Keywords: CAPEX; Cost-benefit analysis; Economic feasibility; External costs; OPEX; Waste collection and transportation. 1. Introduction Economics is one of the most important performance indicators determining the desirability and sustainability of a waste-to-resource project. It needs to be evaluated well before the implementation of a project so that the decision is informed with optimal process and system design to ensure maximum profitability and continuous profit along the operation of the project. Costebenefit analysis (CBA) has been commonly used to evaluate the economic feasibility of a project, technology, or system. It gathers all the monetized costs and benefits associated with the development, maintenance, and operation of a project over its lifetime and estimates one or a few economic parameters (e.g., net-present value (NPV), costebenefit ratio (CBR), and internal rate of return (IRR)) as a measurement of economic feasibility. This chapter introduces the mathematical principles of CBA and provides a systematic database about the major cost and benefit components of the waste-to-resource technologies introduced previously. Finally, the economics of existing waste-to-resource development is explained and discussed. Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00011-6 Copyright © 2022 Elsevier Inc. All rights reserved. 161
162 Chapter 9 System design: costebenefit analysis 2. Mathematical principles 2.1 Economic indicators NPV, CBR, and IRR are commonly calculated in CBA as direct indicators of the profitability of a process, technology, or system under study. To estimate the NPV, all cash flows of a project over a chosen period are resolved to their equivalent present date cash flows. Costs/expenses and revenues/incomes are taken as negative and positive cash flows, respectively. NPV is calculated using (You et al., 2016) NPV ¼ LT X t Ct C0 ð1 þ rÞt (9.1) where Ct is the net cash inflow during a year t; C0 is the total initial capital investment (or CAPEX); LT denotes the lifetime of the project; r is the discount rate in the range between 5% and 15%. A near-zero discount rate means future benefits and costs are worth about the same as today. A positive NPV suggests that a given project is profitable. CBR denotes the ratio between the overall cost and overall benefits of a project and is calculated by ! LT X Cet (9.2) CBR ¼ þ C0 =NPV ð1 þ rÞt t where Cet is the expenditure cost (e.g., operation and maintenance costs and external (emission) cost) during a year t. A CBR less than one suggests a project is profitable. IRR refers to as a discount rate that corresponds to a zero NPV as shown in Eq. (9.1). IRR can be calculated using an algorithm provided by relevant software platform (e.g., “irr” in MATLAB). A high IRR suggests that the project is more economically viable. Additionally, some other indicators specific to the waste-to-resource development can also be estimated, such as the minimum ethanol selling price (MESP) for waste-to-bioethanol development, hydrogen cost for waste-to-biohydrogen development, and levelized cost of energy (LCOE: the average net present cost of energy generation for a plant over its lifetime). 2.2 Cost and benefit components Data compilation is the most critical stage in CBA. In many cases, cost and benefit data specific to a project under analysis is not available and existing data reported in the literature need to be updated and/or used. Assumptions may need to be made
Chapter 9 System design: costebenefit analysis to identify approximate data for the analysis. For example, the construction costs reported in literature are often for past years and need to be updated to the current year, e.g., using Chemical Engineering Plant Cost Index (CEPCI) as Costi ¼ Costj CEPCIi =CEPCIj (9.3) where i and j denote the current year and base year, respectively. The value of CEPCI could be obtained via various online sources (e.g., https://www.chemengonline.com/). Economies of scale need to be considered especially when the scale of the system under analysis is significantly different from the existing ones whose capital costs are to be used in the analysis. There is limited understanding of the impact of system scale toward the unit cost of waste-to-resource development. Approximate relationships have been applied to consider the scale dependence of facility cost (You et al., 2016); for example, f Costk ¼ Costi ðSk =Si Þ (9.4) where k and i denote the designed system scale and base system scale (corresponding to the system reported in literature where relevant cost data will be used), respectively. f is the scaling factor typically ranging between 0.6 and 0.8. For the CBA of typical waste-to-resource development, the following cost and benefit components need to be considered: (1) the capital cost (or CAPEX) being the initial investment of constructing a treatment plant; (2) the operation and maintenance (O&M) cost (or OPEX) being the sum of all the costs required for running the plant; (3) the collection and transportation (C&T) cost being the cost resulting from collecting all waste and transporting it to the treatment facility; the revenues from the sale of (4) products (e.g., electricity, heat, chemicals, etc.); (5) gate fees denoting the revenue from the disposal of the waste; (6) the revenue due to a carbon tax. The specific data for some of the components such as CAPEX, O&M, and C&T are relatively difficult to gather. Assumptions and justifications need to be made to consider the reported data as approximates. The estimation of C&T is even more tricky as it is linked to actual logistics arrangement of waste collection. Meanwhile, C&T can be one of the major contributors (up to 80%) to the overall cost of a waste-to-resource development (Ascher et al., 2020). 2.3 Waste collection and transportation Models have been developed to quantify the energy and resource requirements during waste collection and transportation. ORWARE (ORganic WAste REsearch) is a model that can 163
164 Chapter 9 System design: costebenefit analysis be used to estimate the emissions to air and water, energy turnover, and the amount of residues returned to arable land during the handling of organic waste in urban areas (Dalemo et al., 1997). It was shown that the model predictions fell within 5% e14% of actual numbers for energy consumption and 10%e24% for time consumption. Major processes and elements of organic waste management are considered in this model including sewage plant, incineration, landfill, compost, anaerobic digestion, truck transportation, transportation by sewers, residue transportation, and spreading of residues on arable land. Within the main model, there is a dedicated transportation submodel for calculating the time and energy consumption during the collection of waste with compacting trucks. The submodel is based on (i) the statistics of the average distance from a residential area to the treatment facility/transfer station, fuel consumption per km for the truck and the average load and speed, and (ii) the time and energy consumption data related to the extra work that is performed on account of stopping and emptying bins (Sonesson, 2000). The model considers various factors affecting the fuel consumption and time requirements such as the distance driven, the number of stops that must be made for picking up the waste, the energy required for lifting the bins and compacting the waste, the number of traffic-related stops, speed limits, the type of truck used and fuel, etc. Specifically, it assumes that the distance driven and the number of stops are independent variables, and that the energy needed for lifting the bins and compacting the waste is proportional to the number of stops. The model also defines three different types of collecting areas, i.e., urban, suburban, and rural areas, to differentiate traffic situations. The total driving distance consists of the one from hauling to and from the collection area and to the treatment or transfer station and the one during collection. It is estimated based on the frequency of truck stops (km between stops during collecting), the distance between collection area and unloading point, and the amount of waste collected. The fuel consumption is estimated by summing the one related to the distance driven (the distance the average fuel consumption per km þ the number of stops fuel consumption per stop) and the one related to the extra stops due to emptying garbage bins. The time requirement is the sum of the time for hauling (the total hauling distance/the average speed during hauling), driving during collection (the driving distance during collection/the average speed during collection), and for emptying bins (the number of stops the time per stop). The more recent MSW-collect model appears to be of greater accuracy as compared to the ORWARE model and can be used to estimate the diesel and truck time requirement per tonne of
Chapter 9 System design: costebenefit analysis 165 Figure 9.1 Schematic of MSW-Collect. A truck’s run starts at the truck depot “start/end” (Edwards et al., 2016). waste collected (Edwards et al., 2016). As shown in Fig. 9.1, this model divides the waste collection process into three main stages, i.e., “stop/go” referring to the acceleration/deceleration a truck makes as it drives between stops, “haul to unload” referring to the travel made by the truck to unload the waste including both urban and freeway driving, and “unload to collection” referring to the travel reversing to “haul to unload.” MSW-Collect improves ORWARE by further differentiating lift and “stop/go” during waste collection and the urban and freeway driving during haul, which facilitates the use of contemporary time and fuel requirement data for charactering different waste collection regimes. Geographic information system software is also used to improve the estimation of the distance data. The modeled time requirement can be used to estimate the number of trucks required for waste collection and transportation. The CAPEX and O&M (including staff wage and the size of each truck) of truck operation can be further calculated by considering the unit CAPEX of truck and the annual O&M cost per truck operation. The annual truck fuel cost is estimated based on the modeled fuel requirement and the price of fuel. It is worth noting that for any trucks and fuel purchased in the future, their costs need to be discounted to current time for CBA. 2.4 CAPEX and O&M The CAPEX and O&M cost of waste-to-resource development account for two of the major cost components. Table 9.1 summarizes some of the reported CAPEX and O&M cost for various types of waste-to-resource systems. The numbers in Table 9.1 are indicative only as they were linked to the actual design of a system considered in original studies. Readers are encouraged to check the original sources of the data for evaluating their suitability for a given CBA study.
166 Chapter 9 System design: costebenefit analysis Table 9.1 System CAPEX and O&M cost for different waste-to-resource systems. Type Technology CAPEX O&M cost Reference(s) Waste-toenergy Anaerobic digestion 500 EUR/kW (100 kWe, year 2015) 7% (O&M/CAPEX) Gasification 250e850 USD/tonne of MSW per year (year 2017) 400e700 USD/tonne of MSW per year (year 2017) 9800 USD/tonne of hydrogen production per year (42,077.75 tonne hydrogen/year, year 2022) 533 USD/tonne of hydrogen production per year (3 tonne hydrogen/day, year 2016) 11 million EUR (90 kg/h hydrogen production, year 2017) 45e85 USD/tonne of MSW/year Ascher et al. (2020); Renda et al. (2016) Haraguchi et al. (2019) 50e80 USD/tonne of MSW per year Haraguchi et al. (2019) Pyrolysis Waste-toGasification biohydrogen Dark fermentation Steam gasification Waste-tobiomethane Anaerobic digestion Waste-tobioethanol Saccharification and fermentation 1819 USD/tonne of Lee (2016) hydrogen production per year (42,077.75 tonne hydrogen/year, year 2022) 88,298.1 USD/year Han et al. (2016) (3 tonnes hydrogen/day, year 2016) 4.36 million EUR/annum Yao et al. (2017) (90 kg/h hydrogen production, year 2017) 413 kEUR/year Budzianowski and (920,723 Nm3/year, Budzianowska year 2015) (2015) 0.1 kEUR/m3 (Near-atmospheric digester, year 2015); 0.12 kEUR/m3 (Pressurized digester, year 2015); 230 kEUR/MW (Biogas upgrading unit, year 2015); 0.03 kEUR/(Nm3/h biogas) (Biogas cleaning unite, year 2015) 1305.6 USD/kL of 163.32 million USD bioethanol (Bioethanol production: (Bioethanol 100,000 kL/year, production: year 2019) 100,000 kL/year, year 2019) Kang et al. (2019)
Chapter 9 System design: costebenefit analysis 167 Table 9.1 System CAPEX and O&M cost for different waste-to-resource systems.dcontinued Type Technology Waste-tobiodiesel 5.885 million USD 13,793 kUSD (without Esterification (without acetone as acetone as a and a cosolvent, 4400 cosolvent, 4400 biodiesel transesterification biodiesel tonne/year, tonne/year, year 2018); year 2018); 6.612 15,213 kUSD (with million USD (with acetone as a cosolvent, acetone as a 4400 biodiesel cosolvent, 4400 tonne/year, biodiesel tonne/year, year 2018) year 2018) 7.6 million USD Pyrolysis 76.7 million USD (16.8e21.3 kilotonne (16.8e21.3 kilotonne of of biochar, year biochar, year 2015) 2015) w240,000 Gasification 785,000e835,000 USD e252,000 USD/year (capacity ¼ 0.32 dry (capacity ¼ 0.32 dry tonne/h of woody tonne/h of woody biomass, year 2019) biomass, year 2019) 189 EUR/tonne Torrefaction 33.63 million EUR (capacity ¼ 79,200 (capacity ¼ 79,200 tonne/annum, year tonne/annum, 2018) year 2018) Waste-tobiochar CAPEX O&M cost In addition to the main reactors associated with the technologies, upstream pretreatment (e.g., drying and milling) and downstream upgrading units are often needed for practical deployment. The concrete composition depends on the types of technologies and the configuration design of a specific development to meet the needs of end users. For example, in the economic analysis of a fermentative biohydrogen production plant (1095 tonne food waste treated annually), the CAPEX included the costs of equipment (e.g., blender (16,918 USD), reactor (24,169 USD), fermenter (9668 USD), centrifuge (87,009 USD), sludge pretreatment equipment (19,335 USD), constant temperature incubator (835 USD), and purification unit (21,056 USD)), equipment installation (52,645 USD), piping (31,587 USD), electrical systems (21,058 USD), buildings (52,645 USD), service facilities (26,322), yard improvements (10,529 USD), instrumentation and controls (21,058 USD), engineering and supervision (31,587 USD), Reference(s) Tran et al. (2018) Cheng et al. (2020) Sahoo et al. (2021) Doddapaneni et al. (2018)
168 Chapter 9 System design: costebenefit analysis construction expenses (42,116 USD), legal expenses (10,529 USD), contractor’s fees (15,793 USD), and contingency (52,645 USD) (Han et al., 2016). Yao et al., 2017, studied the economics of steam gasificationebased hydrogen production which comprises a water-gas shift reactor, a rapeseed methyl ester scrubber, and a PSA unit in addition to the main dual fluidized bed steam gasification reactor. The associated annual OPEX included the costs of raw materials (1,195,040 EUR/year), operating labor (350,000 EUR/year), utilities (491,920 EUR/year), employee benefits (77,000 EUR/year), supervision (35,000 EUR/year), laboratory (35,000 EUR/year), maintenance (726,000 EUR/year), insurance and taxes (363,000 EUR/year), operating supplies (363,000 EUR/year), plant overhead (121,000 EUR/year), and depreciation (605,000 EUR/year). In the economic analysis of an AD-based biomethane production system (capacity ¼ 1.1445 MW), the accounting of CAPEX included such components as a digester (300 kEUR), biogas upgrading unit (263 kEUR), biomass storage and preparation unit (200 kEUR), buildings and logistic infrastructure (300 kEUR), installations (350 kEUR), transportation means (40 kEUR), and permission and management (250 kEUR) (Budzianowski & Budzianowska, 2015). The accounting of OPEX included such components as feedstock (maize silage 31 EUR/tonne), digestate handling (8 EUR/tonne for transporting, drying, packaging, and distribution), labor (45 kEUR/(personnelyear)), water and service cost of an upgrading unit (45 kEUR/year), services (14 kEUR/year for safety, legal, technical, and telecommunication), insurance (14 kEUR/year), taxes (11 kEUR/year), and others (10 kEUR/year for electricity, water, management, consumables, etc.). An integrated process consisting of a pretreatment, hydrolysis and fermentation, and purification stage was proposed to generate bioethanol from Miscanthus sacchariflorus (Kang et al., 2019). As shown in Fig. 9.2, the system consists of such equipment as hoppers, a twin-screw extruder, a buffer tank, an ethanol scant reagent feeder, a hydrolysis and fermentation tank, a solvent recovery system, an NaOH tank, a distillation unit, and a dehydration unit. The CAPEX components of the development (capacity ¼ 100,000 kL of bioethanol) included land (20 million USD), structure and housing (10 million USD), pretreatment (12.1 million USD), saccharification and fermentation (4.07 million USD), chemical recovery (12.1 million USD), distillery and solid recovery (14.3 million USD), wastewater treatment (33 million USD), storage (3.3 million), boiler and turbogenerator (49.5 million USD), and utilities (4.95 million USD). The OPEX components included raw material (561.2 USD/kL), enzymes (328 USD/kL), chemical (64 USD/kL), wastewater treatment (72 USD/kL), cooling water (7.3 USD/
Chapter 9 System design: costebenefit analysis 169 Figure 9.2 A schematic diagram of the bioethanol production process considered in CBA (Kang et al., 2019). kL), electricity (15.2 USD/kL), steam (200.9 USD/kL), labor (284.1 USD/kL), and depreciation (97 USD/kL). In the CBA of a biodiesel production system (capacity ¼ 4400 tonne/year, Fig. 9.3) based on grease trap waste (GTW) in Adelaide, Australia, the considered CAPEX components included storage tanks (GTW tanks (219.78 kUSD), an Hexane tank (219.78 kUSD), an ETOH tank (162.44 kUSD), a KOH tank (16.58 kUSD), an H2SO4 tank (21.82 kUSD), an Acetone tank (127.30 kUSD), a B100 tank (254.60 kUSD), a K2SO4 tank (31.02 kUSD)), reactors (esterification reactor (45.01 kUSD), transesterification reactor (45.01 kUSD), and an neutralization reactor (13.31 kUSD)), Figure 9.3 The system composition of the biodiesel plant using a cosolvent of acetoneeethanol considered in CBA (Tran et al., 2018).
170 Chapter 9 System design: costebenefit analysis separation facilities (evaporators (413.60 kUSD), distillation columns (137.87 kUSD), liquideliquid extractors (413.60 kUSD), and a fractional distillation column (1378.68 kUSD)), other equipment (mixing units (41.71 kUSD), heat exchangers (358.06 kUSD), and pumps (60.38 kUSD)), other direct costs (5280 kUSD), total indirect costs (4158 kUSD), and working capital (2475 kUSD) (Tran et al., 2018). The components of OPEX included raw materials (0.983 million USD/year), operating labor (0.346 million USD/year), operating supervision (0.052 million USD/year), utilities (0.728 million USD/year), maintenance and repairs (0.785 million USD/year), operating supplies (0.118 million USD/year), laboratory charges (0.052 million USD/year), royalties (0.066 million USD/year), variable cost (3.13 million USD/year), taxes (0.262 million USD/year), financing (0.763 million USD/year), insurance (0.131 million USD/year), depreciation (0.785 million USD/year), fixed charges (1.94 million USD/year), plant overhead (0.71 million USD/year), manufacturing cost (5.78 million USD/year), administration (0.237 million USD/year), distribution and selling (0.331 USD/ year), research and development (0.264 million USD/year), and general expense (0.832 million USD/year). 2.5 External costs Although waste-to-resource development is regarded to be relatively environmentally friendly (or external benefits) as compared to other conventional waste management technologies such as landfill, they can still bear negative environmental consequences due to the pollutant emissions into water, air, and or land. For example, the thermochemical processing of waste can emit PMs into the environment, leading to the problem of air pollution (Yao et al., 2018). Incineration-based wasteto-energy plants release various types of pollutants such as SO2 and dioxins which, if not controlled properly, pose a health risk to workers and surrounding communities. Additionally, the deployment of a waste-to-resource plant can worsen the traffic conditions (e.g., congestion) of an associated area and lead to disamenity to local residents because of unpleasant “odors” or unsightly perceptions (Jamasb & Nepal, 2010). The negative environmental impacts can be considered in CBA by estimating an associated external cost. The external costs refer to the expenses imposed on society by the environmental disadvantages (pollution and changes in air, water, and land environments) of waste-to-resource development that are not directly reflected in the price of recovered resources (Patrizio et al., 2017). To estimate the external costs, it is important to firstly
Chapter 9 System design: costebenefit analysis classify and quantify the socioenvironmental impacts of a project on ecosystems, human health, natural resources, and other essential categories and then monetize the impacts. The external costs can also cover the costs associated with the compliance with regulations and standards, and the ones related to compliance failure such as compensation fines and penalties, punitive damages, etc. (so-called environmental liabilities) (You & Wang, 2019). It becomes increasingly important for the waste treatment and renewable energy industry to account for the external costs in their business planning to maintain competitiveness and financial profitability, as there is a possibility that associated regulations on socioenvironmental impacts of waste-to-resource development will become increasingly tightened in the future. The external costs arising from the environmental impacts of energy production may be nontrivial. For example, between 2005 and 2010, the average external cost of electricity production in the European Union was w6 EURcent/kWh (Streimikiene & Alisauskaite-Seskiene, 2014). For incineration-based waste-toelectricity development (capacity ¼ 250,000 tonnes), the external cost related to the damage from the emissions to the air (mainly NOx and SO2) was estimated to be 50 EUR per tonne of waste treated and the cost related to the disamenity impact was 8 EUR per tonne of waste treated; for incineration-based wasteto-electricity and -heat development, the corresponding costs were 28.18 EUR per tonne of waste treated and 8 EUR per tonne of waste treated, respectively (Jamasb & Nepal, 2010). Although the estimation of external costs makes the socioenvironmental impacts more comprehensible in the marketplace and facilitates the formulation of business decisions that are environmental risks-informed, it can be challenging and even lead to misleading results due to data limitation, significant uncertainties in the causes and quantification of the impacts, and limited knowledge on the valorization of the impacts (Patrizio et al., 2017). However, it is still valuable to carry out the comparative examination of the externalities of different technologies or methods for decision-making where the impacts of data uncertainty can be effectively mitigated. 2.6 Project incomes Generally, product sale serves as the major income source and product valorization plays an important role to improve the economic feasibility of waste-to-resource development. This suggests that it is desirable to explore the market value of main product(s) and coproduct(s) (or by-product(s)). 171
172 Chapter 9 System design: costebenefit analysis For example, for gasification- and AD-based waste-to-energy development in the United Kingdom, the sale of electricity and/or heat is the focus for the system design and a high heat utilization rate (the percentage of generated heat being sold to residents) is important for the economic viability of the development. Additionally, income from the sale of by-products such as biochar and digestate is also key to promote its economic feasibility (Ascher et al., 2019). To estimate the product sale income, it is necessary to understand (i) the yield and quality of the products and (ii) the market potential of the products (i.e., the market price and the quantity of the products that will be potentially sold). Experimental data or modelling-based analysis are needed to estimate the yield and quality of products. The quality of products needs to meet certain standards (e.g., hydrogen purity for fuel cell applications, nutrient contents of digestate, etc.) and/or the demands of end-users. However, in many preliminary feasibility analyses, the quality of coproduct(s) or by-product(s) was not checked, leading to potential uncertainty in the results of CBA. Market surveys may be needed to understand the market value (sale price) of the products which may vary along the time and/or across regions. For the sale of energy products (electricity and heat), it may involve checking associated Feed-in-Tariffs (FiT) and its development trends (as relevant schemes may change over the lifetime of a project) in promoting renewable and low-carbon energy generation. For the sale of chemicals, it may involve gathering the price data of similar products in the market together with associated quality information. Table 9.2 summarized the prices of some common products recovered from waste. It is worth noting that the numbers shown in Table 9.2 are indicative only and the actual numbers can even fluctuate over time. Gate fees for the disposal of waste serve as another important income source. They may depend on the type of waste as well as the treatment technology adopted. For example, Table 9.3 shows the UK gate fees reported by local authorities. It shows that for organics treated by AD, the gate fees range from -£5/tonne to £68/ tonne with a median level of £26/tonne. It is expected that the gate fees will be variable across regions, and it is necessary to find the most relevant value for CBA. Governmental regulations may dictate additional economic incentives to the development of waste-to-resource. For example, in China, a national electricity subsidy of 0.12 USD/kWh and a local straw-burning avoiding subsidy of 28 USD/tonne were available to support pyrolysis-based biochar production from straw, which significantly improved the economics of biochar
Chapter 9 System design: costebenefit analysis 173 Table 9.2 Reported prices of products recovered from waste. Product Price Country Year Reference Biodiesel Biohydrogen Bioethanol Biochar Digestate Biomethane 0.7666 USD/kg (B20) 2.40 USD/kg (fast pyrolysis) 0.36 GBP/L (enzymatic hydrolysis) 3.08 USD/kg 14.4e19.6 USD/MT 0.16 USD/kWh Brazil Canada United Kingdom e China China 2017 2009e2020 2012 2014 2020 2020 Miranda et al. (2018) Sarkar and Kumar (2010) Wang et al. (2012) Alhashimi and Aktas (2017) Li et al. (2020) Li et al. (2020) Table 9.3 The UK gate fees reported by local authorities in 2017 (WRAP, 2018). Treatment Materials/Type of facility/Grade Median (£/tonnes) Mode Range No of gate fees (£/tonnes) (£/tonnes) reported In-vessel composting Anaerobic digestion Waste-to-energy Waste-to-energy Waste-to-energy Organics Organics All Pre-2000 facilities Post-2000 facilities 49 26 86 57 89 45e50 35e40 85e90 55e60 85e90 production system (Clare et al., 2015). In India, the central government provided subsidies for developing biogas plants depending on the initial capacity: for plants between 3 and 20 kW, the Central Financial Assistance subsidies for power and thermal applications were about 490 USD/kW and 245 USD/kW, for plants between 20 and 100 kW, they were 419 USD/kW and 210 USD/kW, and for plants between 100 and 250 kW, they were 350 USD/kW and 175 USD/kW (Singh et al., 2020). Under Italy’s 2018 decree for biomethane production, fuel retailers for the transportation sector are legally obliged to sell a minimum percentage of biofuel in their fuel blends, and a Certificate of Emission of Biofuel in Consumption (CIC) is issued for every 10 Gcal (1 Gcal ¼ 4.184 109 J) of biomethane produced (D’Adamo et al., 2019). Some specific incentives of the decree include payment of 375 EUR per CIC for a period of 10 years, doubling the unitary subsidy for advanced biomethane production 9e47 -5e68 33e117 44e94 33e117 34 62 62 20 42
174 Chapter 9 System design: costebenefit analysis (from e.g., OFMSW and by-products), and offering an additional premium for producers that also distribute the methane. In Denmark, the electricity produced based on biogas and biomassbased gasification gas and other fuels is receiving a price supplement: for example, for electricity based on pure biogas or gas from gasification, two price supplements (0.26 DKK/kWh (0.039 USD/kWh) and 0.10 DKK/kWh (0.015 USD/kWh)) are available depending on the rules of rate regulation with a settlement price of 0.793 DKK/kWh (0.12 USD/kWh) (Danish Energy Agency, 2017). In the United States, various laws and incentives have been in place to promote the production and application of biodiesel. For example, the Biomass Crop Assistance Program (BCAP) “provides financial assistance to landowners and operators that establish, produce, and deliver biomass feedstock crops for advanced biofuel production facilities: a reimbursement of 50% of the cost of establishing a biomass feedstock crop, as well as annual payments for up to five years for herbaceous feedstocks and up to 15 years for woody feedstocks. BCAP also provides qualified biomass feedstock crop producers matching payments for the collection, harvest, storage, and transportation of the crops to advanced biofuel production facilities for up to two years: 1 USD for each 1 USD per dry ton paid by a qualified advanced biofuel production facility, up to 20 USD per dry tonne.” (US Department of Energy, 2021). Carbon tax is considered as one of the most effective policies for promoting the effort of decarburization by subsidizing relatively cleaner technologies. The technologies of resource recovery from waste have the potential to achieve low-carbon resource production or even negative carbon emissions and thus can economically benefit from the existence of carbon tax. However, the carbon tax is still controversial because it imposes costs on consumers, and it was reported that over half of voters in Washington state opposed a carbon tax in 2018 (Hagmann et al., 2019) (Carattini et al., 2019). Three rates (40, 60, and 80 USD per tonne of CO2) were suggested for a hypothetical global carbon tax to be introduced after 2020, which were consistent with the recommendations by the World Bankesupported High-Level Commission on Carbon Prices (Carattini et al., 2019). 3. Economic feasibility of waste-to-resource development The economics of different waste-to-resource developments vary significantly depending on the design and socioeconomic
Chapter 9 System design: costebenefit analysis conditions of system implementation. In general, process efficiencies and product yields play a critical role in defining the economic feasibility of the developments. 3.1 Waste-to-energy The profitability of waste-to-energy development is closely related to the design of waste management strategies. Upon the comparison of the economic feasibility of an AD-based and an incineration-based strategy for the United Arab Emirates, the AD strategy (treating food waste) was economically infeasible and had an NPV of 127 million USD, while the incineration strategy (treating food waste and comingled waste) had an NPV of 181 million USD and a payback period of 19 years (Abdallah et al., 2018). The inferior economics of the AD strategy was related to its high digestate disposal cost that was caused by landfilling the digestate (accounting for 40% of the waste treated and the use of digestate was restricted by laws) and limited public participation in food waste separation at source. Meanwhile, the incineration strategy benefited from the greater amount of waste treated and a greater revenue from energy production. The economics of the AD and incineration strategies were mainly affected by the landfilling cost and electricity tariff. The NPV of the AD and incineration strategies would increase 57% and 5%, respectively, for a 10% decrease in the landfilling cost, while they would increase by 27% and 91%, respectively, for a 10% increase in the tariff. The cost of efficiency improvement against the economic gain of waste-to-energy was studied by the CBA of a base case and its seven modification cases (Eboh et al., 2019). The base case plant was a municipal heat and power grate boiler fired by municipal solid waste and industrial waste with an energy input of 100 MW. Modification 1 (M1) involved the addition of a flue gas condensation unit; M2 increased the temperature and pressure of the steam in the process and was added with an intermediate reheater; M3 was similar to M2 but had flue gas condensation; M4 was integrated with waste gasification (gasified at 900 C to produce syngas which was cooled down to 400 C and cleaned prior to combustion in a gas boiler for electricity and heat production). M5 was similar to M4 but had flue gas condensation; M6 was with two air heaters removed and added with a high-pressure feed-water heater and a new air heater; M7 was similar to M6 but had flue gas condensation. It was found that M6 was the best option with the lowest capital cost per unit increase in efficiency and the second-best alternative 175
176 Chapter 9 System design: costebenefit analysis for the lowest capital cost per total revenue earned. M1 and M7 were the two best options considering the capital cost per total unit of revenue generated. M4 and M5 had the highest exergy efficiencies, i.e., 30.1% and 30.5%, respectively, but their capital investment costs per increase in efficiency were higher than the other cases. M1 and M7 increased the NPV of the base case by 30% and 12% due to the significantly higher heat production via the use of flue gas condensation. The study also showed that the price of district heat and its maintenance costs significantly affected the economic viability of the waste-to-energy plant: 49% of the annual income was from the district heat. 3.2 Waste-to-biohydrogen Okolie et al. studied the economics of a biohydrogen system (capacity ¼ 170 tonnes of soybean straw per day, biomass-to-water ratio ¼ 0.1, temperature ¼ 500 C, particle size ¼ 0.13 mm, and reaction time ¼ 45 min) based on the catalytic supercritical water gasification of soybean straw (Okolie et al., 2021). The system consisted of four process units, i.e., pretreatment, gasification, separation, and purification and combustion. The minimum selling price of biohydrogen was estimated to be 1.94 USD/kg, which was relatively low than other biohydrogen technologies. The minimum selling price was mainly affected by the feedstock price, utility cost, tax rate, and labor cost with the feedstock price and labor cost being most significant. The undiscounted NPV would decrease from 80.18 million USD to 68.02 million USD and 75.77 million USD for a 30% increase in the tax rate and soybean straw price, respectively. A 30% decrease in the labor cost reduced the NPV by about 4.2%. Gasafi et al. (2008) studied the economics of a wasteto-biohydrogen system based on the supercritical water gasification of sewage sludge. It was found that a 50% increase in the equipment cost would increase the hydrogen production cost by 72%. Meanwhile, a 50% decrease in the interest rate of debt would reduce the hydrogen cost by 46%. The costs of fuel consumption and labor had relatively small impacts on the hydrogen production cost: a 50% increase in fuel or labor costs would increase the hydrogen production cost by about 17%. A combined bioprocess based on solid state fermentation was designed to recover hydrogen from food waste (Han et al., 2016). A. awamori and A. oryzae were used to produce glucoamylase and protease in the process of solid state fermentation at 30 C for 4 days. The produced enzymes were transferred into a bioreactor with pretreated food waste (grounded and blended with
Chapter 9 System design: costebenefit analysis water) for hydrolysis at 55 C and an agitation speed of 500 rpm. The food waste hydrolysate was used as the substrate for hydrogen production using pretreated anaerobic sludge (screened and heat pretreatment at 100 C for 6 h) as inoculum in a fermenter (pH was maintained between 4.0 and 4.6 by adding 5 M NaHCO3 and 0.005 M H2SO4). The system separated hydrogen and carbon dioxide using a purification system that was made of a low-pressure gas tank, a carbon dioxide compressor, an activated carbon filter, an absorbing-type desiccator, a compression refrigerator, and a storage tank. This process achieved a hydrogen yield of 39.14 mL/g food waste and had a capacity of treating 3 tonnes of food waste per day. Its total capital cost and annual production cost were 583,092 USD and 88,298 USD/year with a payback period of 5 years. It was also shown that the price of hydrogen and the labor cost had the highest impact on the NPV of the development. 3.3 Waste-to-biomethane System scale (from 100 m3/h to 1000 m3/h), types of feedstocks (OFMSW and a mixture of 30 wt.% maize and 70 wt.% manure residues), and biomethane end use (fed into the grid, cogeneration (electricity and heat), and sold as a vehicle fuel) were found to affect the economics of waste-to-biomethane development (Cucchiella & D’Adamo, 2016). Additionally, the impacts of the variations of the major parameters such as the incentive of CIC, natural gas price, incentive of Feed-in Tariff, investment cost of biogas production, substrate transportation cost, and maintenance and overhead costs on the economics have also been studied. It was found that 34, 33, 29, and 11 out of 60 cases studied were economically feasible for the scale of 1000, 500, 250, and 100 m3/h, respectively (more cases suggest a higher chance of being profitable). 43, 35, and 29 out of 80 cases were economically feasible for the end use of selling as a vehicle fuel, cogeneration, and feeding into the grid, respectively. 103 and 4 out of 120 cases were economically feasible for OFMSW and the mixture substrates, respectively. For a 100 m3/h plant treating OFMSW and selling biomethane as a vehicle fuel, it would be profitable when the incentive of CIC was 500 EUR and the percentage of maintenance and overhead costs in the overall production cost was 15%. For a 100 m3/h plant treating OFMSW and feeding biomethane into the grid to displace natural gas, it would be profitable when the natural gas was 30.50 EUR/MWh, there was no unitary transportation cost of substrate, and the percentage of maintenance and overhead costs in the overall production 177
178 Chapter 9 System design: costebenefit analysis cost was 15%. For a plant treating a mixture of 30 wt.% maize and 70 wt.% manure residues, it would not be profitable when biomethane was fed into the grid or for cogeneration, or when the scale was 100 m3/h regardless of the end use methods of biomethane. A co-digestion system was designed to co-ferment fish waste and cow dung to produce biomethane and concentrated liquid mineral fertilizer with carbon dioxide, solid fertilizer, and purified water being by-products (Kratky & Zamazal, 2020). In the process, fish waste was hydrothermally pretreated and then mixed with cow dung and water in a homogenization unit. Biogas and digestate were produced in an anaerobic fermenter followed by the upgrading of the biogas to separate biomethane and carbon dioxide. A solideliquid separation process was used to separate the liquid effluent from solid fermentation residue (solid fertilizer). The liquid effluent further went through a reverse osmosis section to produce concentrated liquid mineral fertilizer and purified water. The capital cost of the design was estimated to be 5,274,000 EUR (659 EUR/tonne of fish waste) with biogas upgrading, digestate upgrading, raw material storage and pretreatment, and anaerobic digestion contributing 44%, 23%, 22%, and 11%, respectively (i.e. pretreatment and posttreatment accounted for 89% of the capital cost). The O&M cost was 2,225,000 EUR per year and was mainly contributed by the energy cost, personnel cost, maintenance cost, and corporate directions. The economics of the biomethane development was mainly affected by the specific capital cost, methane yield, methane purchase price, and electricity price. The specific capital cost (659 EUR/tonne of fish waste) was the ratio between the total capital cost and the amount of fish waste treated and it was around four times of the ratios of conventional biogas plants (110e150 EUR/tonne of waste). When the specific capital cost was reduced from 659 EUR/tonne to 200 EUR/tonne, the payback period could be shortened by around 70%. If the biomethane yield could be higher than 0.34 L/kg VS of fish waste, the payback period would be less than 8 years, which was considered to be attractive to investors. If the price of biomethane is lower than 0.17 EUR/kWh, the payback period would be longer than 8 years. 3.4 Waste-to-bioethanol Fig. 9.4 shows the flow diagram of a typical bioethanol production process (maximum capacity ¼ 10.43 dry tonne per batch and energy consumption ¼ 30 kW/dry tonne) using an enzyme complex (Cellic Ctec 1) to treat waste papers (newspaper, office paper, cardboard, and magazine) (Wang et al., 2013). In the process,
Chapter 9 System design: costebenefit analysis Figure 9.4 Schematic process flow diagram of base cases (Wang et al., 2013). waste papers firstly went through a metal removal process (A100) and then were pulped at 15% (w/w) solids loading in pulpers (A200). The pulped paper slurry was saccharified enzymatically at 50 C for 72 h (A300). A recombinant bacterium Zymomonas mobilis was used in the fermentation (A400) and the hydrolysate from saccharification and nutrients of corn steep liquor and diammonium phosphate were used to seed incubation and fermentation tanks at 40 C for 36 h. The bioethanol yield of fermentation was 95% and the generated bioethanol was purified to 99.5% (w/w) using distillation, rectification, and molecular sieve adsorption processes (A500). The solid fuel (moisture content <50%) was transferred to the combustion area (A800) and the liquid fraction was transferred to the wastewater treatment area (A600) where biogas was produced via anaerobic digestion and sent to a circulating fluidized bed combustor (together with the solid fuel and the cell mass of anaerobic digestion) to generate heat and electricity. Fig. 9.5 shows the flow diagrams of two advanced bioethanol production processes which involved the use of (a) dilute acid to pretreat office paper and (b) oxidative lime to pretreat newspaper (Wang et al., 2013). In the first advanced process (Fig. 9.5(a)), shredded office paper was preheated to 100 C by 179
180 Chapter 9 System design: costebenefit analysis Figure 9.5 Schematic process flow diagrams of advanced cases ((a) office paper-to-bioethanol with dilute acid pretreatment and (b) newspaper-to-bioethanol with oxidative lime pretreatment; Unit process indicated using dashed boxes) (Wang et al., 2013). low-pressure steam at an initial solid loading of 30% and was then treated using dilute acid (0.5% (w/w) H2SO4 at 220 C) for 2 min. The liquid fraction of the treated slurry is detoxified and reacidified with H2SO4, followed by mixing with the solid fraction and being sent to the saccharification area (A200). In the product recovery area (A500), the liquid fraction from the distillation bottoms was sent to a series of evaporators, got concentrated, and transferred into the combustor for heat and electricity generation. In the second advanced process (Fig. 9.5(b)), shredded newspaper was preheated to 100 C at a 30% solid loading and was then treated using oxidative lime at 140 C with 1.875% (w/w) lime (Ca(OH)2) solution under compressed air for 3 h. The liquid fraction separated from the treated slurry was supplied with carbon dioxide to precipitate CaCO3 for calcium. Similarly, evaporators were used to produce a concentrated syrup which was then transferred into a combustor for heat and electricity generation.
Chapter 9 System design: costebenefit analysis The CBA of the processes showed that, for the typical process (Fig. 9.4), office paper was the most favorable feedstock with a high bioethanol yield of 343 L/dry tonne feedstock and the lowest MESP of 0.32 GBP/L, followed by cardboard with a yield of 247 L/ dry tonne feedstock and a MESP of 0.33 GBP/L. Although dilute acid pretreatment increased the yield of bioethanol, it increased the capital cost by 15% due to the addition of extra equipment costs for the pretreatment and heat exchanger requirement and the extra demands in electricity and high-pressure steam. The pretreatment arrangement also increased the amount of gypsum produced and sent to landfill. For the advanced process with oxidative lime pretreatment, the increased total equipment cost was offset by the benefits from the increased bioethanol yield and reduced enzyme consumption, reducing the MESP based on newspaper by 0.04 GBP/L. The MESPs of bioethanol derived from waste papers ranged from 0.31 GBP/L (0.45 USD/L) to 0.66 GBP/L (0.95 USD/L), which seemed to be better than that of the ones derived from other feedstocks (e.g., lignocellulosic biomass (0.57 USD/L to 1.17 USD/L) and corn stover (0.9 USD/L to 1.17 USD/L)). The biomass feedstock cost was the biggest contributor (63.5%) for the value of MESP, and the capital recovery charge for the combustor and electricity generator accounted for 16.7% of the total cost. The surplus electricity recovered from the biomass residue and supplied to the UK National Grid offset 42.7% of the cost of bioethanol production. The capital costs associated with saccharification and fermentation accounted for 16.9% and 10.9% of MESP, respectively. Moreover, the study showed that the MESP of bioethanol could be significantly affected by the variation in the solid loading of saccharification: increasing the loading from 15% to 20% would reduce the MESP by up to 20%. This was attributed to the fact that the energy consumption for distillation was reduced as more concentrated bioethanol stream was fed to the distillation column and the capacity and thus capital cost for the wastewater treatment area was significantly reduced. The economic performance of bioethanol production is also reasonably sensitive to the fermentation efficiency. Improving the efficiency has the potential to result in a reduction in the MESP by up to 26%. 3.5 Waste-to-biodiesel A CBA study in 2019 showed that microalgae biodiesel production in China was not economically viable with a unit production cost of 2.29 USD/kg that was significantly higher than that of commercial petro-diesel (Sun et al., 2019). Major 181
182 Chapter 9 System design: costebenefit analysis economic barriers included limited microalgae productivity, annual operating days, and product benefits, which appeared to be difficult to resolve in the short term. It was also highlighted that a relevant policy system needed to be in place to facilitate the development of microalgae-derived biodiesel. A 2012 CBA study of palm biodiesel production in Malaysia, the second largest producer of crude palm oil feedstock, showed that the life cycle cost of a 50 kilotonne palm biodiesel plant with a lifetime of 20 years was 665 million USD and had a payback period of 3.52 years (Ong et al., 2012). The cost of feedstock accounted for 79% of the total production cost (or 0.5 USD/L) followed by the O&M cost (0.13 USD/L). The variation of feedstock price would significantly affect the life cycle cost of palm biodiesel: a 0.1 USD/kg increase in the crude palm oil price would increase the biodiesel production cost by 0.05 USD/L. In this case, governmental subsidies became essential to support palm biodiesel development: for example, with a biodiesel subsidy of 0.1 USD/L, the biodiesel price would be comparable to the price of fossil diesel at a crude palm oil price of 1.05 USD/kg. Meanwhile, it would be desirable to keep improving the biodiesel conversion processes and their efficiencies. The economics of four different schemes of biodiesel production (annual cane processing capacity of 1,600,000 tonne) from engineered cane lipids were compared. The schemes included direct glycerolysis, solvent-based poplar lipid separation, solvent-based poplar lipid and free fatty acid separation, and membrane-based poplar lipid and free fatty acid separation under thermal glycerolysis and enzymatic glycerolysis approaches, respectively (Arora & Singh, 2020). It was shown that the scheme with polar lipid separation under thermal glycerolysis was most profitable with an NPV of 96.5 million USD and a minimum selling price of 1,107USD/tonne biodiesel. The economics of the processes was mainly affected by the cane lipid percentage, cane lipid procurement cost, polar lipid content, and free fatty acid content. When the cane lipid content decreased from 20% to 5%, the minimum selling price would increase by 45 5%. A 20% reduction in the lipid price from the base case (0.67 USD) led to a 60%e100% increase in the NPV and a 11%e20% decrease in the minimum selling price. A positive NPV could be achieved for the case of 15% cane lipids and a low lipid procurement price of 0.536 USD/kg. For the case of a higher lipid price, e.g., >0.80USD/kg, the lipid content needed to be 20% (even higher lipid contents in plant tissues would be less possible) for a positive NPV.
Chapter 9 System design: costebenefit analysis 3.6 Waste-to-biochar The CBA of a pyrolysis-based biochar system that was used to convert postharvest forestry residues into biochar for on-site Eucalypt plantations showed that the system could achieve an annual income of over 179 kUSD (Wrobel-Tobiszewska et al., 2015). The price of biochar and product distribution were two of the most significant factors affecting the economics of the development. The sale of biochar was the largest contributor to the annual revenue, while the cost of system operation accounted for the largest cost component. Doubling the price of biochar would increase the net profit by 166%, and for a 1 USD increase in the biochar price, there would be 288 USD of additional net profit. Doubling the carbon price would increase the net profit by 10%, and the system remained profitable when there was no carbon benefit. In a separate study, the economics of gasification-based biochar production from forest residues was studied. The minimum selling price of biochar was reported to be 1044 USD/per dry tonne (Sahoo et al., 2021). The minimum selling price was mainly controlled by the capital cost, labor cost, and feedstock cost, and it could be reduced to a level of 470 USD/dry tonne via technological improvement. Further reduction in the selling price was possible by considering the credits of renewable energy production and carbon saving. It was shown that the waste heat generated in the gasification system could be used to dry wet feedstocks, making the moisture content a less significant factor affecting the minimum selling price. Biochar production from straw using slow pyrolysis in China was shown to be economic viable (Ji et al., 2018). A biochar production system with a feedstock consumption capacity of 30,000 tonne/year and a biochar yield of 10,500 tonne/year was shown to have a capital cost of 5.19 million USD and achieve an NPV of 20.98 USD per tonne straw. The cost of feedstock was estimated to be 1.56 million USD/year, contributing to most of the annual operation cost. The annual biochar sale revenue was 3.67 million USD. The economics of the development was mainly affected by the feedstock cost and biochar price, followed by the initial capital cost (a 20% increase in the capital cost reduced the NPV by 8.26%). 4. Uncertainties The accuracy and validity of CBA can be significantly affected by the uncertainties and variabilities in the input data and assumptions. For a CBA study examining the economic feasibility 183
184 Chapter 9 System design: costebenefit analysis of a new technology, system, or process, there can be limited knowledge about the process parameters (e.g., energy flows and yields and quality of products). Existing knowledge regarding the new technology may be based on small-scale experiments and there are potential uncertainties limiting the accurate estimation of the associated data for large-scale development. Generally, there is a lack of cost and benefit data specific to new developments, and assumptions need to be made and existing data on other different technologies need to be “borrowed” for a CBA study. The assumptions need to be justified so that the “borrowed” data are relevant to and could represent the case under analysis. Hence, it is important to check the existing data for their applicability and suitability, and make it clear in the analysis any potential adverse effects from the use of the data. To mitigate the problems related to analysis uncertainties, a few methods are recommended: (i) a learning curve approach can be used to estimate the progress rate and trends of CAPEX and OPEX; (ii) artificial intelligenceebased models can be used to predict the variation of product cost and cash flows in the future if there is sufficient data; (iii) the opinions of industrial or experienced experts can be combined with fuzzy approaches to improve the reliability of the analysis (Bagdatlı et al., 2017); (iv) a completeness check can be carried out to ensure all relevant cost and benefit components are considered and the sources of data are well documented; (v) sensitivity analysis (e.g., based on the design-of-experiments method) can be carried out to evaluate the impacts of process parameters, economic data, and system design on the results of CBA; and (vi) stochastic approaches (e.g., Monte Carlo simulation) can be applied to account for the variability of data in a CBA study (You et al., 2016), which will be subject to the accurate understanding of the probabilistic distributions of relevant parameters. References Abdallah, M., Shanableh, A., Shabib, A., & Adghim, M. (2018). Financial feasibility of waste to energy strategies in the United Arab Emirates. Waste Management, 82, 207e219. Alhashimi, H. A., & Aktas, C. B. (2017). Life cycle environmental and economic performance of biochar compared with activated carbon: A meta-analysis. Resources, Conservation and Recycling, 118, 13e26. Arora, A., & Singh, V. (2020). Biodiesel production from engineered sugarcane lipids under uncertain feedstock compositions: Process design and technoeconomic analysis. Applied Energy, 280, 115933. Ascher, Simon, Li, W., & You, S. (2020). Life cycle assessment and net present worth analysis of a community-based food waste treatment system. Bioresource Technology, 305, 123076.
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Chapter 9 System design: costebenefit analysis Tran, N. N., Tisma, M., Budzaki, S., McMurchie, E. J., Gonzalez, O. M. M., Hessel, V., & Ngothai, Y. (2018). Scale-up and economic analysis of biodiesel production from recycled grease trap waste. Applied Energy, 229, 142e150. US Department of Energy. (2021). Biodiesel laws and incentives in federal. https://afdc.energy.gov/fuels/laws/BIOD?state¼US. Wang, L., Sharifzadeh, M., Templer, R., & Murphy, R. J. (2012). Technology performance and economic feasibility of bioethanol production from various waste papers. Energy & Environmental Science, 5(2), 5717e5730. Wang, L., Sharifzadeh, M., Templer, R., & Murphy, R. J. (2013). Bioethanol production from various waste papers: Economic feasibility and sensitivity analysis. Applied Energy, 111, 1172e1182. WRAP. (2018). Comparing the costs of alternative waste treatment options. https://wrap.org.uk/resources/guide/gate-fees-report-2018. Wrobel-Tobiszewska, A., Boersma, M., Sargison, J., Adams, P., & Jarick, S. (2015). An economic analysis of biochar production using residues from Eucalypt plantations. Biomass and Bioenergy, 81, 177e182. Yao, J., Kraussler, M., Benedikt, F., & Hofbauer, H. (2017). Techno-economic assessment of hydrogen production based on dual fluidized bed biomass steam gasification, biogas steam reforming, and alkaline water electrolysis processes. Energy Conversion and Management, 145, 278e292. Yao, Z., You, S., Dai, Y., & Wang, C.-H. (2018). Particulate emission from the gasification and pyrolysis of biomass: Concentration, size distributions, respiratory deposition-based control measure evaluation. Environmental Pollution, 242, 1108e1118. You, S., & Wang, X. (2019). On the carbon abatement potential and economic viability of biochar production systems: Cost-benefit and life cycle assessment. In Biochar from biomass and waste (pp. 385e408). Elsevier. You, S., Wang, W., Dai, Y., Tong, Y. W., & Wang, C.-H. (2016). Comparison of the co-gasification of sewage sludge and food wastes and cost-benefit analysis of gasification- and incineration-based waste treatment schemes. Bioresource Technology, 218. https://doi.org/10.1016/j.biortech.2016.07.017 187
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System design: life cycle assessment 10 Abstract This chapter explains the principles of life cycle assessment (LCA) and its applications for evaluating the environmental impacts of wasteto-resource developments. It introduces the procedures of LCA with respect to four stages, i.e., goal and scope definition, life cycle inventory, life cycle impact assessment, and data interpretation. It discusses the different implementation methods of LCA including process-based LCA vs. input-output (IO) LCA, attributional LCA vs. consequential LCA, and allocation strategies. This chapter reviews the existing studies on the environmental impacts of the different types of waste-to-resource developments and critical factors are highlighted. This chapter concludes with the discussion about the major types of uncertainty involved with LCA. Keywords: Attributional LCA; Allocation; Consequential LCA; Environmental impacts; Input-output LCA; Life cycle assessment; Processbased LCA. 1. Introduction Waste-to-resource development has the potential to achieve higher environmental sustainability as compared to the conventional waste management methods such as landfill. The actual environmental gains from the use of waste-to-resource technologies depend on various factors such as types of waste, types of waste-to-resource technologies, technological configurations, environmental conditions and backgrounds, etc. Moreover, upstream and downstream operations are often essential part of waste-to-resource development and include the collection, segregation, transportation, and pretreatment of waste as well as the upgrading of products and their storage and distribution for subsequent applications. As a result, there would be Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00001-3 Copyright © 2022 Elsevier Inc. All rights reserved. 189
190 Chapter 10 System design: life cycle assessment potential variabilities in the data input and system boundary definitions in evaluating the environmental impacts of wasteto-resource development. It becomes increasingly important to accurately quantify the impacts of environmental and energy systems for optimal decision-making. LCA is a standardized protocol that can be used to track and report the environmental impacts of a process, technology, or system throughout its entire life cycle. The method of LCA is defined by a set of standards, e.g., ISO 14040 and 14044 by the International Organization for Standardization (ISO) (ISO, 1997, 2006). Originally developed from the principles of industrial ecology, LCA has been widely adopted by decision-makers to understand the environmental performance of specific products, processes, or activities. Based on the range of process components considered, LCA can be classified into cradle-to-grave, cradle-to-gate, and cradle-to-cradle types, respectively. The cradle-to-grave type refers to an LCA study that considers the energy, material, and emissions of the processes ranging from raw material extraction, construction, manufacturing, to use, demolition, and end-of-life disposal. For example, for food wasteebased biogas production, a cradle-to-grave LCA covers such processes as food waste collection and transportation, biogas system construction and operation, biogas upgrading and utilization, and digestate processing and handling (e.g., treatment, transportation, and utilization). A cradle-to-gate LCA study considers the impacts related to manufacturing of a product up to the “gate” of the plant. For food wasteebased biogas production, it includes such processes as food waste collection and transportation, biogas system construction and operation, and biogas upgrading and storage. A cradle-to-cradle LCA study considers additional processes where the generated product(s) become the raw materials of other products. It is worth noting that LCA is not the same as life cycle costing analysis that tracks and reports the economics (covering such information as capital costs, operation and maintenance costs, etc., as discussed in Chapter 9) of a project throughout its full life cycle. Instead, LCA focuses on the energy and material flows of a process, technology, or system, and associated emissions to nature (e.g., GHG and particulate matters (PMs)) and material extraction from nature (e.g., water, limestone, fossil fuels, and iron ore) throughout its entire life cycle. Additionally, it is often impossible and impractical to accurately figure out all relevant environmental impacts due to resource, data, and knowledge
Chapter 10 System design: life cycle assessment limitations. In many cases, only a few relevant and significant impact categories are analyzed using LCA considering data availability. 2. LCA procedures The general structure of LCA is made of four stages: (i) goal and scope definition, (ii) life cycle inventory (LCI) analysis, (iii) impact assessment, and (iv) data interpretation. The four stages are carried out sequentially with the information defined and/ or calculated in a previous stage serves as the input for or guides a subsequent stage. The actual analysis is often conducted in an iterative mode within and between the different stages to ensure the whole analysis is complete and self-consistent. 2.1 Goal and scope definition The first stage of LCA involves the definition of the goal and scope. To define a clear and comprehensive goal, it is important to address the following questions (Simonen, 2014): What is the intended application of the process, technology or system under analysis? Why is LCA carried out? Who is the intended audience of the analysis? How the results of the analysis will be disseminated to, e.g., the public? What are the benefits of the process, technology or system and the analysis? What are the limitations regarding the use of the results? For example, a comprehensive goal statement for biomethane production from anaerobic digestion of food waste can be: “This LCA is a study of biomethane production from anaerobic digestion of food waste (what). It is carried out to improve our understanding of the environmental impacts of the biomethane production process (why). The analysis and its results will be used by the food waste management and biomethane production industry and investors, waste management organizations, and associated policymakers (who). The LCA will benefit the associated stakeholders in their design of optimal food waste management and low-carbon methane production plans with reduced environmental impacts. The results will be made open access to the public for enhancing the awareness of sustainable food waste management and low-carbon methane production practices.” The goal of an LCA study, once developed, will be by no means unchangeable. It is common to revisit and reevaluate the goal along the development of the analysis and make corresponding adjustment so that the whole analysis is self-consistent. 191
192 Chapter 10 System design: life cycle assessment The scope definition involves the clarification of what is included in and excluded from the analysis and what are the parameters of the analysis. Specifically, it is critical to define the process, technology or system under study with clear statements about its function, unit, and performance, i.e., the definition of a functional unit. The system boundary of the analysis needs to be set up to indicate what is included and excluded and the specific processes/system units of the system to be included. It is also necessary to define the methodological details including the methods of impact assessment (e.g., midpoint or endpoint) and data interpretation (e.g., sensitivity analysis). Moreover, if multiple products (coproduction) are involved, the method of allocation needs to be defined (details will be introduced below). Finally, the sources and quality requirement of data are also needed to be defined. The setup of the scope needs to be consistent with and support the LCA goal. For example, if the main goal is to reduce the carbon footprint of waste management, the scope of the analysis can be limited to tracking GHG emissions and associated processes with other kinds of emissions such as PMs being ignored. Like the definition of LCA goal, it is common to revisit and reevaluate the LCA scope to ensure a self-consistent and accurate analysis. The functional unit includes such information as the quantity, quality, and duration of a product or service considered. It facilitates the comparison across different cases by ensuring the comparison on a functionally equivalent basis. In many existing LCA studies of waste-to-resource development, a unit that includes quantity only, or a declared unit, is used (e.g., the treatment of 1 tonne of food waste, the generation of 1 kWh of electricity, the production of 1 m3 of biohydrogen). This often applies to cradle-to-gate LCA studies where the application of products is not the focus. On the other hand, for cradle-to-grave LCA studies where the application of product(s) is considered, a relatively comprehensive definition needs to be used. For example, the production of 1 m3 of biohydrogen with a purity of 99.99% for fuel cell application can be defined as the functional unit for the LCA of biohydrogen production for fuel cell application. It is important to define a clear system boundary to facilitate reevaluation and checking while ensuring the fulfilment of the LCA goal. However, the definition of system boundary is often limited by the availability and accuracy of data. The system boundary should include the processes where energy, material, and emission data are available and of high accuracy. The following processes are commonly excluded in a system
Chapter 10 System design: life cycle assessment boundary: manufacture of fixed equipment (e.g., factory), manufacture of mobile equipment (e.g., trucks), hygiene-related water use (e.g., toilets and sinks for workers), and employ labor (e.g., commuting). A diagram can always be used to delineate the system boundary to indicate the processes, inputs, and outputs to be analyzed (Fig. 10.1). 2.2 Life cycle inventory LCI involves the collection of energy and material requirements and emissions throughout the entire life cycle of the process, technology or system under analysis. The data can be from existing databases or from measurements and calculation (e.g., process modeling, estimation based on reported data). For the LCA of a new waste-to-resource technology, it is key to understand the energy and material flows and yields (and quality) of products of the process based on experiments and modeling (e.g., theoretical models, process models, machine learninge based models, etc.). Toward the end of an LCI analysis, a list of data covering the energy, water, and material inputs from nature and emissions to air, water, and land is developed. According to ISO 14044 (2006b:12), the inventory data can be classified into four types: (i) inputs (energy, materials, etc.), (ii) products (including coproducts and waste), (iii) emissions, and (iv) other environmental aspects (ISO, 2006). The LCI data can be gathered with respect to a unit process or an aggregated process including a few unit processes. The former will be beneficial for granulating the data but are not always available. Figure 10.1 A diagrammatic illustration of the system boundary for food waste management based on the technology of anaerobic digestion. Ascher, S., Li, W., & You, S. (2020). Life cycle assessment and net present worth analysis of a community-based food waste treatment system. Bioresource technology, 305, 123076. 193
194 Chapter 10 System design: life cycle assessment 2.3 Life cycle impact assessment Life cycle impact assessment (LCIA) involves the amalgamation of the emission data and estimate the indicators of a few overall environmental impact categories of relevance and interest. The LCIA stage is made of two steps: (i) a classification step where the data from the LCI analysis stage are classified in terms of their relevance to and are matched with the specific impact categories (e.g., the data related to GHG emissions are collected and matched with the global warming potential (GWP)), and (ii) a characterization step where the emission and resource extraction data of each category are translated into a limited number of impact indicators (e.g., GHG emissionserelated data converted into GWP) by weighting the quantity of inventory using a characterization factor. For example, the impact of methane on global warming is 28 times of carbon dioxide, so it has a characterization factor of 28 if the GWP is calculated on the basis of carbon dioxide equivalence (Table 10.1). The total GWP (GHG emissions or carbon footprint) is calculated based on the sum of the multiplication of the amount of each type of GHG emission and the corresponding characterization factor. It is worth noting that the same type of environmental impact can be affected by multiple different emissions (e.g., there are different types of GHG species), while the same emission may be linked to multiple environmental impacts (e.g., methane emission is related to GWP and smog formation). There are two major ways in which characterization factors can be derived, i.e., midpoint one and endpoint one corresponding to the consideration of the different range of the stages within the causeeeffect chain of emissions. The endpoint method estimates the indicators that account for the ultimate effects of emissions in terms of human health (e.g., skin cancer and immune system suppression), biodiversity (e.g., freshwater Table 10.1 GWP characterization factors relative to CO2. Emissions GWP characterization factors References CO2 CO2 (biogenic) CH4 N 2O 1 0 28 265 IPCC (2016), Myhre et al. (2013) Christensen et al. (2009), Møller et al. (2009) IPCC (2016), Myhre et al. (2013) IPCC (2016), Myhre et al. (2013)
Chapter 10 System design: life cycle assessment and marine life damage and crop damage), and resource scarcity (e.g., oil, gas, coal, and energy costs). The midpoint method estimates the indicators that account for the single environmental problems such as global warming, PMs, ozone depletion, etc. Midpoint indicators have been applied widely in the LCA of waste-to-resource development. The endpoint indicators are easier to understand and can thus be used to facilitate the illustration of the impacts and simplify result interpretation. However, the endpoint indicators are generally of higher uncertainties as the knowledge and data required for estimating the ultimate effects of emissions are often not available and/or accurate. This makes the comparative assertion difficult especially when the differences of the LCA results among comparing cases are not significant. On the other hand, the calculation of midpoint indicators is of less uncertainties because it involves summing the emission and resource extraction data directly. By considering the single environmental problems, midpoint indicators allow the illustration of a wider range of environmental problems incurred by the object. However, midpoint indicators are less straightforward to understand as compared to the endpoint ones. Finally, a single environmental performance “score” can be developed by weighting the relative importance of the different environmental impacts, which serves to describe the comprehensive environmental performance of a process, technology, or system. This is normally preferred by policymakers for making quick decisions. However, there is no global consensus on an appropriate method to combine different impacts, and current practice tends to report the impact results of LCA separately. 2.4 Interpretation The final stage involves the examination of the results and analysis to identify potential issues and ensure a high-quality analysis in compliance with the standard. A minimum of three evaluation methods are required (ISO, 2006b: 26). First, the completeness of the analysis for the processes included in the system boundary and analysis needs to be checked. For complex systems for which hundreds or even thousands of processes are tracked, this check is particularly desired. Second, assumptions and uncertainties are often inevitable in LCA, and the sources of uncertainties include limited site-specific or technologyspecific data in the inventory, the aggregation of data over different temporal and spatial scales, and the cumulative effects of input uncertainties and data variability. A sensitivity check is 195
196 Chapter 10 System design: life cycle assessment required to assess the reliability of the final LCA results and conclusions by studying how they are affected by the data uncertainties, allocation methods, or calculation of category indicator results, etc. (Ross et al., 2002). Third, a consistency check is required to ensure the LCA study is internally consistent across the stages and the goal and scope are properly met. 2.5 LCA implementation 2.5.1 Process-based LCA versus inputeoutput LCA In process-based LCA (a bottom-up approach), data about the energy and material flows, emissions, and resource extractions related to every single stage (or subprocess) of the whole life cycle are used (Simonen, 2014). As a result, it has the strength of detailing the emission contributions of different subprocesses, which is valuable for technology comparison and guiding the development of process improvement plans. Process-based LCA has a high requirement on the completeness of database. Due to data and knowledge limitation, process-based LCA often do not include all processes (e.g., upstream and downstream of waste-to-resource development) along the whole supply chain and suffers from system boundary truncation, leading to truncation errors and a potential underestimation of environmental impacts (Yang, 2017). Inputeoutput LCA (IO-LCA) (a top-down approach) covers an entire economy in terms of financial transactions, and inputs and outputs between sectors, and aggregated industry-level environmental data are used (Zhao & You, 2019). Accordingly, the system boundary of IO-LCA can cover a relatively comprehensive supply chain. Hence, IO-LCA has the advantages of promoting the use of existing economic data and facilitating quick screening and simplified LCA to gain approximate ideas and to identify emission hotspots of the process, technology, or system under analysis. However, due to the use of aggregated data, IO-LCA involves with aggregation errors and reduced resolution in the analysis results. It is also not possible to identify the emission differences and contributions of different subprocesses using IO-LCA, adversely affecting the design of process improvement. Alternatively, hybrid LCA based on the combination of process and IO models helps to achieve a compromise between process specificity and completeness, with the potential to reduce LCA uncertainties. Generally, the data associated with the upstream and/or downstream processes to a core technology under development are obtained based on IO-based LCA, while the method
Chapter 10 System design: life cycle assessment of process-based LCA is applied to consider the core technology to detail the impacts of the different subprocesses within the technology design. The main disadvantages of hybrid LCA include the lack of standardized protocol, and varied ways of data and method organization and combination, as well as potential problems related to double counting (Martínez-Corona et al., 2017). 2.5.2 Attributional LCA versus consequential LCA Multiple definitions have been proposed for differentiating attributional and consequential LCA, and one of them is introduced in this chapter. For the attributional approach, inputs and outputs are attributed to the functional unit by linking and/or partitioning the unit processes of the system following a normative rule (Ekvall et al., 2016). Attributional LCA is carried out under the assumption that the environmentally relevant energy and material flows, emissions and resource extractions will remain the same upon the development of the system. It does not consider indirect effects resulted from the changes in the output of the system. Attributional LCA is more appropriate for analyzing a small, confined system that will not tend to change the overall economic and environmental system. The consequential approach considers that activities within the process, technology, or system under analysis are linked and are expected to change in response to a change in demand for the functional unit. Consequential LCA aims to consider that the conditions may change or adapt in response to possible decisions and to model the consequences of the changes or actions. It is more suitable for supporting major decisions on policies or large-scale manufacturing shifts that tend to impact the overall economic and environmental systems to a great extent. For studies trying to compare different technological options characterized by different functional units, consequential LCA can also be a better option. Generally, attributional LCA and consequential LCA have different choices of input data. The attributional approach uses actual average physical flows representing average burdens for producing a unit of product in the process, technology, or system and is useful for consumption-based carbon accounting. The consequential approach uses marginal or incremental data that represent small changes in the output of the development and are small enough to not cause a change affecting the operation of the system (Ekvall et al., 2016). The marginal data require dynamic modeling of supply and demand. For example, in an 197
198 Chapter 10 System design: life cycle assessment attributional LCA of two waste-to-energy systems (one based on a moving grate combustor and another based on a gasifier), the data from several plants in operation were processed by means of mass and energy balances and substance flow analysis (Arena et al., 2015). Consequential LCA was applied to compare two ways of food waste utilization: food waste only used in the energy chains and food waste partly used in the energy chains and partly in the reuse and reduction chains (Bartocci et al., 2020). In this study, attributional LCA was used to detail the environmental impact of energy crops substitution using food waste in different proportions in a biogas plant based on three different scenarios (i) the biogas plant fed with only energy crops and olive husks, (ii) the use of food waste to substitute 1 wt.% corn silage, and (iii) the use of food waste to substitute 42 wt.% corn silage. In another study, consequential LCA was used to compare different management strategies (different pretreatment methods and different biogas applications) for biologically treating organic household waste (Khoshnevisan et al., 2018). It was considered that the analyzed scenarios would have structural consequences on the background system (energy network) and the analysis fell into the situation of meso/macrolevel decision support proposed by the International Reference Life Cycle Data System. Long-term marginal data were adapted to account for the recovered energy and nutrient. 2.5.3 Allocation Many waste-to-resource processes produce more than one product (i.e., with coproduct(s) or by-product(s)). For example, pyrolysis can achieve trigeneration with biochar, biooil, and syngas being the products. AD produces biogas as the main product and digestate as a by-product. Accordingly, in an LCA of a process, technology, or system of multiple products, it is important to decide the division of the impacts and emissions among the products, i.e., what is the level of impact for each of the products. According to the LCA standard, coproduct allocation is about “partitioning the input or output flows of a process or a product system between the product system under study and one or more other product systems” (ISO, 2006). A stepwise procedure is recommended by ISO14044 for allocating the environmental impacts for the LCA of multigeneration processes. 2.5.3.1 Avoiding allocation, wherever possible Allocation should be avoided by (i) dividing the unit process or (ii) expanding the product system (if (i) is not possible).
Chapter 10 System design: life cycle assessment For (i), the unit process can be divided by collecting the input and output data for the multiple (e.g., two) products separately, leading to the development of multiple (e.g., two) separate processes for LCA. For system expansion (e.g., a two product system), the scope of the LCA study for the main product is expanded to include emissions and functions related to the second product, meaning the expansion of the functional unit. It is also understood as a procedure for eliminating by-products or coproducts as outputs by including them as negative inputs: the multiproduct system is reduced into a single-product system via subtraction of avoided burdens associated with the second product which is not part of the original functional unit (Heijungs et al., 2021). In the LCA of organic waste incineration in Spain WtE plants (functional unit ¼ 1 tonne of organic waste), the total emissions and consumption associated with incineration were allocated to the organic fraction based on the waste composition, mass, and heating. Considering that the process involved waste treatment and energy production, the system expansion approach was applied, and the function of the “alternative” system (energy production) was subtracted from the system under study. The data of the electric power mix of Spain were used for the technology replaced in the system expansion (Margallo et al., 2014). 2.5.3.2 Allocating based on physical relationships If allocation can not be avoided, LCA data should be allocated in a way that reflects the underlying physical relationships between them. Physical allocation means that the input and output of LCA are partitioned based on the physical properties of the different flows. Examples of physical relationships useable include by mass or volume of products. However, mass or volume is not always an appropriate figure to meaningfully describe the relationships between the products. For example, for the gasification process where syngas as a gas product and biochar as a solid product are generated, either mass or volume is not suitable for allocation. In this case, different relationships (e.g., economic values) can be used. 2.5.3.3 Allocating based on other relationships Economic values are commonly applied when the previously methods cannot be applied. In this case, the environmental impacts are partitioned in proportion to the economic values (e.g., prices or price relations) of the different products. In a study evaluating the environmental impact of hydrogen production 199
200 Chapter 10 System design: life cycle assessment from various types of lignocellulosic wastes (e.g., vine and almond pruning), the allocation of cultivation environmental impacts between fruit and pruning waste was considered. Two strategies were examined: (i) the total impacts were associated with both of the products; (ii) the impact was distributed based on the price of products (1% of the impact for almond pruning waste and 10% for vine pruning waste). It was found that the method of allocation significantly affected the calculated environmental impacts (Moreno & Dufour, 2013). 3. LCA of waste-to-resource developments 3.1 Waste-to-energy The environmental impacts of two waste-to-energy systems based on a moving grate combustor and a gasifier to treat unsorted residual municipal waste (200 ktonne/year) were compared using an attributional LCA approach (Arena et al., 2015). The functional unit was defined as the treatment of 1 tonne of solid waste for electricity recovery. The system boundary included solid waste treatment in the waste-to-energy units, electricity production, material (metals and inerts) recovery from bottom ashes, and the disposal of air pollution control residues. System expansion was applied to avoid allocation: the replaced products on the markets were identified and included in the model. The European energy mix and the data from several operating systems processed based on mass and energy balances and substance flow analysis were used. It was found that the GWPs of the combustion and gasification systems were 178 kgCO2-eq./ tonne waste and 390 kgCO2-eq./tonne waste. The higher carbon footprint of the gasification system was attributed to its significantly higher process emissions (612 kgCO2-eq./tonne waste vs. 359 kgCO2-eq./tonne waste) due to the use of metallurgical coke to achieve high temperatures in the molten section of the reactor. These process emissions exceeded the saved emissions resulted from metal recovery (76 kgCO2-eq./tonne waste). An attributional LCA approach was applied to compare the environmental performance of a real, large-scale combustionbased waste-to-energy unit (700,000 tonne/year) with some “virtual” units (Ardolino et al., 2020). The “virtual” units were defined according to the Best Available Techniques REFerence document. The real system had a thermal capacity of 340 MWt and an electricity export rate of 880 kWh/tonnewaste, with a net electric recovery efficiency of 26%. The functional unit was the treatment of 1 tonne of unsorted residual municipal waste. It was shown that
Chapter 10 System design: life cycle assessment the real unit had better environmental performance than the virtual ones in terms of GWP, Non-Renewable Energy Potential (NREP) and Respiratory INorganic Potential (RINP). The electricity efficiency and direct air emissions were critical factors affecting the performance. The better GWP performance of the real unit (399 kgCO2-eq./tonne waste vs. 435 & 469 kgCO2-eq./tonne waste) was attributed to a lower level of material consumption and more electricity exported to the grid. Sensitivity analysis showed that the real unit would perform better environmentally even in the case of a future electricity mix characterized by 45% of renewable sources. 3.2 Waste-to-biohydrogen The LCA of waste-to-biohydrogen development often adopted cradle/gate-to-gate boundaries (mass- or energy-based functional units), with cradle/gate-to-grave boundaries mainly considered for hydrogen use in mobility (traveled distancee based functional units) (Valente et al., 2017). For example, the environmental impacts of a biohydrogen/biomethane blend production process based on dark fermentation and mesophilic anaerobic digestion were studied. The considered feedstocks include food waste and wheat feed and the raw biogas produced was upgraded using PSA, followed by compression to 200 bar and the subsequent distribution at a passenger vehicle refuelling facility (Patterson et al., 2013). The functional unit of the analysis was the fuel corresponding to 1 km of passenger vehicle transportation. The system boundary included waste transportation and pretreatment, pasteurization, dark fermentation processing, anaerobic digestion processing, waste disposal, gas upgrading, compression, and storage, biogas-based heat and electricity generation, and operation of passenger vehicle. The environmental impacts were allocated among the multiproducts (biogas, digestate, and waste) on an economic basis. The endpoint method was applied to estimate the impacts of the process on human health, resources, and ecosystem quality by the estimation and normalization of damage factors for emissions. It was found that the impacts of biomethane/hydrogen blend production from food waste on carcinogens and ecotoxicity ranged from 5.94 105 point to 7.70 105 point and from 2.76 105 point to 3.33 105 point, respectively, which were significantly lower than the ones of fossil fuel. This was partly attributed to the diversion of the waste from landfill. However, for the utilization of biomethane/hydrogen derived from wheat feed, its impact on climate change was higher than that of diesel fuel 201
202 Chapter 10 System design: life cycle assessment utilization. The environmental impacts of different renewable hydrogen production methods (i.e., softwood gasification, short rotation coppice wood gasification, steam reforming of biomethane derived from organic substrate fermentation, glycerol pyroreforming, alkaline water electrolysis supported by electricity from biomass cogeneration) were compared (Wulf & Kaltschmitt, 2013). Mass or volume-based allocation was used to consider the production of wood residues and wood logs for nonenergetic purposes, while exergy-based allocation was used for heat generation as a by-product. 3.3 Waste-to-biomethane The GHG emissions of biomethane production and application as a transportation fuel have been evaluated for different types of feedstocks including organic waste and energy crop feedstocks (clover and timothy) (Uusitalo et al., 2014). The functional unit was 1 MJ of biomethane produced and supplied to the transportation sector. The impacts of four types of allocation methods were studied: (i) GHG emissions from digestate utilization were included in the ones from biomethane production; (ii) GHG emissions from digestate utilization were not included in the ones from biomethane production; (iii) allocation was based on economic or energy values; (iv) system expansion was conducted and GHG emissions savings by replacing mineral fertilizers using the digestate were subtracted from the total emissions. It was shown that the carbon footprints of biomethane production from organic waste and energy crops with the use of digestate were around 22 and 61 gCO2-eq./MJ (allocation (i)). The system expansion method (allocation (iv)) reduced the estimated carbon footprints of biomethane production from organic waste and energy crop by 10 and 22 gCO2-eq./MJ, respectively. The economic valueebased allocation method considered the actual utilization potential of digestate and served as a better option than the energy-based method which was better to be used upon the use of digestate for energy production. The emissions associated with land use change, cultivation processes, digestate use, and technology selections in digestion and upgrading were of the highest uncertainties. The use of energy intensive technologies would significantly increase the carbon footprint while the use of renewable energy in the processes could help to effectively reduce the carbon footprint according to the study. The environmental impacts of biomethane production by anaerobic digestion of OFMSW were compared across different biomethane application scenarios (e.g., energy production,
Chapter 10 System design: life cycle assessment biomethane for the transportation sector, and no energy from grid) using an attributional, process-based LCA approach (Ardolino et al., 2018). The analyzed anaerobic digestion plant comprised a wet anaerobic digester operating at 37e39 C generating 583 m3N/h of biogas from 100 tonne/day of OFMSW, a combined heat and power generation unit combusting part of the produced biogas, and a membrane separation unit upgrading the remaining biogas (400 m3/h) to biomethane (207 m3/h) that was supplied into the natural gas grid. To avoid the allocation problem, the system expansion method was used based on the identification of which products (diesel) on the markets were replaced by the coproducts and including the replacement in the model. It was assumed that the produced biomethane was used by a vehicle fleet consisting of 50% of passenger cars and 50% of small rigid trucks on urban roads. It was shown that the GWP and nonrenewable energy potential of the biomethane production scenario were 79% and 36% lower than that of the energy production scenario. The environmental impacts were affected by various factors such as the vehicle fleet composition and biomethane consumption, methane leakage in the biogas upgrading unit, the destination of solid digestate, gas engine efficiency, and national electric energy mix. 3.4 Waste-to-bioethanol The environmental impacts of bioethanol production from waste have been widely studied using LCA. Wang et al. (2012) studied (i) the environmental benefits of bioethanol production from waste papers and its application as a transport fuel for flexible fuel vehicles, and (ii) compared the impacts of the bioethanol production method with that of conventional wastepaper management methods including recycling and incineration for energy recovery. The functional units of (i) and (ii) were 1 kg of bioethanol used in a flexible fuel vehicle and the treatment of 1 kg of wastepaper, respectively. The system boundary for the case of bioethanol production and application included three unit processes, i.e., collection and transportation of wastepapers, bioethanol and electricity (coproduct) production, and distribution and use of the bioethanol in a flexible fuel vehicle. To avoid allocation, system expansion was applied by considering the avoided emissions of electricity (bioethanol and incineration systems) substitution of the average UK National Grid electricity production, of heat (incineration system) substitution of the average UK heat production, of ethanol substitution of conventional petrol, and of recycled paper for substituting virgin paper 203
204 Chapter 10 System design: life cycle assessment production. The study showed that the direct GHG emissions from combustion/turbogenerator in the bioethanol production process was the major positive GHG contributor, accounting for 32%e58% of positive GHG emissions. Other major GHG contributors include the direct CO2 emissions from the distillation process of saccharification and fermentation and from the end use of bioethanol and the input of energy-intensive enzyme. Pretreatment methods also affect the GHG emissions. Oxidative lime pretreatment reduced the GHG emissions of a newspaper-to-bioethanol process, while dilute acid pretreatment increased the GHG emissions of an office paper-to-bioethanol process. Overall, bioethanol production from wastepaper served as an effective approach to achieve environmentally favorable or similar benefits as compared to the conventional methods based on recycling and incineration. The environmental sustainability of bioethanol production from banana waste (banana rachis) and application to a passenger car upon mixing with gasoline was studied (Guerrero & Muñoz, 2018). The functional unit was 1 MJ of energy released by the combustion of bioethanol in a passenger car. The system boundary included raw material (e.g., banana rachis, H2SO4, water, enzymes, and petrol) collection and transportation, bioethanol production (e.g., milling, soaking and mixing, steam explosion pretreatment, sedimentation, saccharification and fermentation, distillation, etc.), and bioethanol distribution and use. The climate change, terrestrial acidification, freshwater eutrophication, photochemical oxidant formation, PM formation, and fossil depletion for the generation of 1 MJ bioethanol were reported to be 0.0315 kg CO2-eq., 0.0001 kg SO2-eq., 1 105 kg Peq., 0.0001 kg NMVOC, 5 105 kg PM10-eq., and 0.0066 kg oileq., respectively. In particular, the climate change impact of the bioethanol production was less than half of gasoline. The processes of bioethanol use, wastewater treatment, and saccharification and formation accounted for most of the analyzed impacts. Increasing the ratio of bioethanol in the blend served to reduce the GHG emissions but to increase freshwater eutrophication and water depletion. 3.5 Waste-to-biodiesel The collection and transportation of biodiesel feedstocks such as waste cooking oil significantly affect the environmental impacts of biodiesel production. An LCA study on waste cooking oilebased biodiesel production showed that the collection efficiency and the features of collection including types of collection (door-to-door, street containers or restaurants), population
Chapter 10 System design: life cycle assessment density and sector were the two most significant influential factors (Caldeira et al., 2016). The system boundary considered three stages of biodiesel production including waste cooking oil collection, feedstock pretreatment, and transesterification-based biodiesel production. The functional unit was 1 MJ of biodiesel produced. Waste cooking oil collection for the domestic and the food service industry was considered, respectively. System expansion was applied to consider the avoided burdens of glycerine (coproduct) substitution and wastewater treatment (electricity consumption) of waste cook oil. It was shown that the waste collection stage could account for 6%e71%, 2%e50%, and 8%e55% of the overall climate change, ozone depletion, and photochemical oxidant formation impacts. The collection efficiency (liter of waste collected per km) and type of fuel used by the waste collection vehicles significantly affected the environmental impacts and the impacts could be effectively lowered by using biodiesel as the vehicle fuel. Improving the waste collection efficiency would reduce the costs and impacts of collection, improving the overall sustainability of waste cooking oilebased biodiesel production. The biodiesel derived from waste cooking oil had lower environmental impacts than petrol-diesel and the biodiesel derived from virgin biomass. An attributional LCA approach has been carried out to evaluate the GWP, fossil cumulative energy demand, and air pollutant emissions (CO, NOx, SOx, and PM) for biodiesel production from grease trap waste (Hums et al., 2016). Grease trap waste is featured by its heterogeneous composition, high acidity, and high sulfur content, incurring additional processing requirements. The functional unit of the analysis was the production and combustion of 1 MJ of biodiesel from grease trap waste. The attributional LCA study considered the transportation of grease trap waste, separation of grease trap waste lipids, disposal of wastewater and solid waste, fuel recovery from lipids, and fuel use in a vehicle. The disposal of solid waste and wastewater from grease trap waste lipid separation accounted for a significant portion of the overall environmental impacts. When the lipid concentration was over 10%, the environmental impacts of the grease trap waste-based biodiesel production was comparable to that of soybean-based biodiesel. When the lipid concentration was less than 10%, the emissions from waste transportation and waste pretreatment dominated the GWP. A consequential LCA approach was also applied to compare the GWP of the biodiesel production with the current means (landfill) of grease trap waste disposal. The total GWP of biodiesel production from grease trap waste was higher than the current waste disposal method due to the 205
206 Chapter 10 System design: life cycle assessment emissions related to the processes of biodiesel conversion and purification. However, the GWP of the biodiesel process was negative when the avoided emissions for substituting low-sulfur diesel and current waste disposal were considered. 3.6 Waste-to-biochar The environmental impacts of waste-to-biochar development have been extensively analyzed using LCA. The endpoint environmental impacts of biochar production in rural areas were evaluated with respect to five thermochemical technologies, i.e., two low-temperature ones (nonretort earth mound kilns and retort kilns) and three high-temperature ones (flame curtain kilns, micropyrolytic cook-stoves with gas flame used for cooking, and gasification with gas combustion used for electricity production) (Smebye et al., 2017). The functional unit was the production of 1 kg biochar. The system boundary included thermochemical production of biochar, carbon sequestration, avoided electricity production (displacing electricity generation from diesel-fuelled generators), and avoided waste (woody shrub or agricultural residue) combustion for cooking. Several impact categories including climate change, PM emissions, land use effects, and minerals and fossil fuels were combined to estimate the overall impact. It was shown that flame curtain kilns had lower impacts than retort kilns and earth-mound kilns because of the avoided use of startup wood and low material use and gas emissions. Biochar production from flame curtain kilns was environmentally neutral, as the production emissions were offset by carbon sequestration from the stable carbon in biochar. Gas and aerosol emissions and impacts related to material production accounted for the most significant negative factors for pyrolysis and kilns. The production emissions of gasification and retort kilns were higher than pyrolytic cook-stoves and flame curtain kilns because of the use of significant metal and concrete in system production. The use of start-up wood also increased the production emissions for retort kilns. Pyrolytic cook-stoves had the lowest PM emission due to the lower PM10 emissions as compared to flame curtain and retort kilns. The climate change impacts of slow pyrolysisebased biochar production from corn stover, yard waste, and switchgrass feedstocks in the United States have been evaluated using a process-based LCA approach (Roberts et al., 2010). The functional unit was the management of 1 tonne of dry biomass. The considered processes in the system boundary included biomass collection and transportation, biomass shredding and drying, slow pyrolysis, syngas and oil combustion for heat
Chapter 10 System design: life cycle assessment production, and biochar transportation and soil application. System expansion was used to incorporate the avoided processes of natural gas production and combustion, biomass composting, and fertilizer production. The biochar production from stover and yard waste had a net GHG emission of 864 and 885 kg CO2-eq. per tonne dry biomass, respectively, 62%e66% of which was contributed by biochar-based carbon sequestration. The lower GHG emission for the yard waste case was because there were no waste production and collection-related emissions (only transportation emissions). The switchgrass-based biochar production had a much higher GHG emission at 36 kg CO2-eq. per tonne dry biomass. The avoided fossil fuel production and combustion contributed to 26%e40% of GHG emissions. Two percent to four percent of the total emission saving was contributed by reduced N2O emissions from biochar soil application. 4. Uncertainty analysis There are three major types of uncertainty involved with LCA, i.e., parameter uncertainty (uncertainty in input data), scenario uncertainty (uncertainty in normative choices), and model uncertainty (uncertainty in mathematical model choices) (Huijbregts et al., 2003). With the increasing popularity of LCA for supporting decision-making, the problem of uncertainty should not be neglected; otherwise, there is a risk of applying LCA to generate inaccurate data and mislead the decision-making process. Hence, it is imperative to include uncertainty analysis and associated explanations to facilitate the interpretation of the results and conclusions of LCA for reliable decision-making. Parameter uncertainty is often linked to the lack of precise data (e.g., existence of measurement errors) and limited knowledge for a specific process, and energy and material use, so that existing data and model predictions that are general or not representative of the process, technology, or system under analysis are used. To account for this type of uncertainty, random samplingebased techniques such as Monte Carlo simulation have been applied in LCA (Caldeira et al., 2016). It is key to assign appropriate probability distributions (e.g., lognormal, triangular, etc.) to uncertain and/or variable parameters during the LCI analysis stage for the Monte Carlo simulationebased method. However, for most of the cases, the applied probability distributions were assumed without explicit evidence or validation. In this case, it is worth examining the effects of the types of probability distributions on the results of LCA. Chen et al. developed a modified range 207
208 Chapter 10 System design: life cycle assessment method by utilizing publicly available data to estimate the uncertainty ranges in the actual values in IO models. It was shown that the average uncertainty ranges of energy consumption values were generally within 40% (Chen et al., 2018). Scenario uncertainty is related to the variability in the normative choices such as geographical scale of analysis and time horizon, while model uncertainty can be caused by the inappropriate selection of LCA models or methods such as allocation methods. Both scenario and model uncertainty significantly affect the results of LCA. Huijbregts et al. (2003) proposed a method to account for all three types of uncertainty together: quantifying the parameter uncertainty using Monte Carlo simulation, and the scenario and model uncertainty by resampling different scenarios and model formations, respectively. In recent, various methodologies have been proposed to propagate and analyze LCA uncertainty in a multistep way: (i) characterization using probability distributions, multiple scenarios, predefined changes, and ranges around default values, (ii) propagation using Monte Carlo simulation ((a) local sensitivity analysis using scenario analysis, (b) screening method, or (c) global sensitivity analysis based on the calculation of rank correlation coefficients or regression coefficients), and (iii) visualization using, e.g., summary statistics, coefficients of variation, ranges, contribution to variance percentages, uncertainty and variability ratios, etc. (Michiels & Geeraerd, 2020). For characterization, the use of probability distributions was most preferable by existing studies. Local sensitivity analyses, screening methods, and global sensitivity analysis serve as effective ways to identify the most influential parameters. Global sensitivity analysis requires that the uncertainty and variability in the inventory stage are clearly differentiated. It is recommended that uncertainty/variability and sensitivity measures should be combined to allow the identification of the parameters for more targeted data compilation and analysis and for uncertainty reduction and system improvement. References Ardolino, F., Boccia, C., & Arena, U. (2020). Environmental performances of a modern waste-to-energy unit in the light of the 2019 BREF document. Waste Management, 104, 94e103. https://doi.org/10.1016/j.wasman.2020.01.010 Ardolino, F., Parrillo, F., & Arena, U. (2018). Biowaste-to-biomethane or biowaste-to-energy? An LCA study on anaerobic digestion of organic waste. Journal of Cleaner Production, 174, 462e476. https://doi.org/10.1016/ j.jclepro.2017.10.320
Chapter 10 System design: life cycle assessment Arena, U., Ardolino, F., & Di Gregorio, F. (2015). A life cycle assessment of environmental performances of two combustion- and gasification-based waste-to-energy technologies. Waste Management, 41, 60e74. https:// doi.org/10.1016/j.wasman.2015.03.041 Bartocci, P., Zampilli, M., Liberti, F., Pistolesi, V., Massoli, S., Bidini, G., & Fantozzi, F. (2020). LCA analysis of food waste co-digestion. The Science of the Total Environment, 709, 136187. https://doi.org/10.1016/ j.scitotenv.2019.136187 Caldeira, C., Queirós, J., Noshadravan, A., & Freire, F. (2016). Incorporating uncertainty in the life cycle assessment of biodiesel from waste cooking oil addressing different collection systems. Resources, Conservation and Recycling, 112, 83e92. https://doi.org/10.1016/j.resconrec.2016.05.005 Chen, X., Griffin, W. M., & Matthews, H. S. (2018). Representing and visualizing data uncertainty in input-output life cycle assessment models. Resources, Conservation and Recycling, 137, 316e325. https://doi.org/10.1016/ j.resconrec.2018.06.011 Christensen, T. H., Gentil, E., Boldrin, A., Larsen, A. W., Weidema, B. P., & Hauschild, M. (2009). C balance, carbon dioxide emissions and global warming potentials in LCA-modelling of waste management systems. Waste Management and Research, 27(8), 707e715. https://doi.org/10.1177/ 0734242X08096304 Ekvall, T., Azapagic, A., Finnveden, G., Rydberg, T., Weidema, B. P., & Zamagni, A. (2016). Attributional and consequential LCA in the ILCD handbook. The International Journal of Life Cycle Assessment, 21(3), 293e296. https://doi.org/10.1007/s11367-015-1026-0 Guerrero, A. B., & Muñoz, E. (2018). Life cycle assessment of second generation ethanol derived from banana agricultural waste: Environmental impacts and energy balance. Journal of Cleaner Production, 174, 710e717. https://doi.org/ 10.1016/j.jclepro.2017.10.298 e, J., Schaubroeck, S., Heijungs, R., Allacker, K., Benetto, E., Brandão, M., Guine Schaubroeck, T., & Zamagni, A. (2021). System expansion and substitution in LCA: A lost opportunity of ISO 14044 amendment 2. Frontiers in Sustainability, 2, 40. https://www.frontiersin.org/article/10.3389/frsus.2021. 692055. Huijbregts, M. A. J., Gilijamse, W., Ragas, A. M. J., & Reijnders, L. (2003). Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling. Environmental Science and Technology, 37(11), 2600e2608. https://doi.org/ 10.1021/es020971þ Hums, M. E., Cairncross, R. A., & Spatari, S. (2016). Life-cycle assessment of biodiesel produced from grease trap waste. Environmental Science and Technology, 50(5), 2718e2726. https://doi.org/10.1021/acs.est.5b02667 IPCC. (2016). Global warming potential values. Greenhouse Gas Protocol. ISO. (1997). ISO 14040:1997 Environmental management d life cycle assessment d principles and framework. https://www.iso.org/standard/23151.html. ISO. (2006). ISO 14044:2006 Environmental management d life cycle assessment d requirements and guidelines. https://www.iso.org/standard/37456.html. Khoshnevisan, B., Tsapekos, P., Alvarado-Morales, M., Rafiee, S., Tabatabaei, M., & Angelidaki, I. (2018). Life cycle assessment of different strategies for energy and nutrient recovery from source sorted organic fraction of household waste. Journal of Cleaner Production, 180, 360e374. https://doi.org/10.1016/ j.jclepro.2018.01.198 209
210 Chapter 10 System design: life cycle assessment Margallo, M., Dominguez-Ramos, A., Aldaco, R., Bala, A., Fullana, P., & Irabien, A. (2014). Environmental sustainability assessment in the process industry: A case study of waste-to-energy plants in Spain. Resources, Conservation and Recycling, 93, 144e155. https://doi.org/10.1016/ j.resconrec.2014.09.014 Martínez-Corona, J. I., Gibon, T., Hertwich, E. G., & Parra-Saldívar, R. (2017). Hybrid life cycle assessment of a geothermal plant: From physical to monetary inventory accounting. Journal of Cleaner Production, 142, 2509e2523. Michiels, F., & Geeraerd, A. (2020). How to decide and visualize whether uncertainty or variability is dominating in life cycle assessment results: A systematic review. Environmental Modelling and Software, 133, 104841. https://doi.org/10.1016/j.envsoft.2020.104841 Møller, J., Boldrin, A., & Christensen, T. H. (2009). Anaerobic digestion and digestate use: Accounting of greenhouse gases and global warming contribution. Waste Management and Research, 27(8), 813e824. https:// doi.org/10.1177/0734242X09344876 Moreno, J., & Dufour, J. (2013). Life cycle assessment of hydrogen production from biomass gasification. Evaluation of different Spanish feedstocks. International Journal of Hydrogen Energy, 38(18), 7616e7622. https://doi.org/ 10.1016/j.ijhydene.2012.11.076 on, F.-M., Collins, W., Fuglestvedt, J., Huang, J., … Myhre, G., Shindell, D., Bre Zhang, H. (2013). Anthropogenic and natural radiative forcing. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, … Y. Xia (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Patterson, T., Esteves, S., Dinsdale, R., Guwy, A., & Maddy, J. (2013). Life cycle assessment of biohydrogen and biomethane production and utilisation as a vehicle fuel. Bioresource Technology, 131, 235e245. https://doi.org/10.1016/ j.biortech.2012.12.109 Roberts, K. G., Gloy, B. A., Joseph, S., Scott, N. R., & Lehmann, J. (2010). Life cycle assessment of biochar systems: Estimating the energetic, economic, and climate change potential. Environmental Science and Technology, 44(2), 827e833. https://doi.org/10.1021/es902266r Ross, S., Evans, D., & Webber, M. (2002). How LCA studies deal with uncertainty. The International Journal of Life Cycle Assessment, 7(1), 47e52. Simonen, K. (2014). Life cycle assessment. Routledge. Smebye, A. B., Sparrevik, M., Schmidt, H. P., & Cornelissen, G. (2017). Life-cycle assessment of biochar production systems in tropical rural areas: Comparing flame curtain kilns to other production methods. Biomass and Bioenergy, 101, 35e43. Uusitalo, V., Havukainen, J., Kapustina, V., Soukka, R., & Horttanainen, M. (2014). Greenhouse gas emissions of biomethane for transport: Uncertainties and allocation methods. Energy and Fuels, 28(3), 1901e1910. https://doi.org/ 10.1021/ef4021685 Valente, A., Iribarren, D., & Dufour, J. (2017). Life cycle assessment of hydrogen energy systems: A review of methodological choices. The International Journal of Life Cycle Assessment, 22(3), 346e363. https://doi.org/10.1007/ s11367-016-1156-z
Chapter 10 System design: life cycle assessment Wang, L., Templer, R., & Murphy, R. J. (2012). A Life Cycle Assessment (LCA) comparison of three management options for waste papers: Bioethanol production, recycling and incineration with energy recovery. Bioresource Technology, 120, 89e98. https://doi.org/10.1016/j.biortech.2012.05.130 Wulf, C., & Kaltschmitt, M. (2013). Life cycle assessment of biohydrogen production as a transportation fuel in Germany. Bioresource Technology, 150, 466e475. https://doi.org/10.1016/j.biortech.2013.08.127 Yang, Y. (2017). Does hybrid LCA with a complete system boundary yield adequate results for product promotion? The International Journal of Life Cycle Assessment, 22(3), 456e460. Zhao, S., & You, F. (2019). Comparative life-cycle assessment of Li-ion batteries through process-based and integrated hybrid approaches. ACS Sustainable Chemistry and Engineering, 7(5), 5082e5094. 211
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System optimization 11 Abstract This chapter introduces the background of multiobjective optimization of waste-to-resource development. It focuses on the procedures of multiobjective optimization with respect to six main stages including framework definition, superstructure generation, optimization problem formulation, model definition, solution strategy, and optimal solution identification. It also reviews two typical methods for optimal solution identification, i.e., the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) with their procedures and advantages and disadvantages. Keywords: Analytic hierarchy process (AHP); Multicriteria decision analysis; Multiobjective optimization; Superstructure; Technique for order preference by similarity to ideal solution (TOPSIS); Waste-to-resource. 1. Introduction Designing waste-to-resource systems is a complex process where the variabilities of feedstock choices and system configurations as well as the socioeconomic and environmental requirements of end-users need to be coherently considered. This means that for the same type of feedstock, there are various end-product options, and for each type of end-product, multiple technologies and system configurations may be technically feasible. Meanwhile, for the same type of technology and system configuration, multiple feedstocks are potential candidates, and their preferences are subject to environmental and geographical conditions (e.g., distance to the facility). Hence, designing waste-to-resource systems becomes an optimization problem in terms of various criteria including economics, environmental impacts, and social impacts. The criteria are usually conflicting with each other, i.e., a design with better profitability may be of greater environmental footprints (less environmentally friendly). For example, a largescale waste-to-energy system based on incineration may have better economics but worse a GWP than a large-scale waste-tobiochar system based on pyrolysis. The optimization process Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00006-2 Copyright © 2022 Elsevier Inc. All rights reserved. 213
214 Chapter 11 System optimization involves a compromise among the criteria against the demands of relevant stakeholders. In this case, a multiobjective optimization approach is required to generate a set of optimal trade-off solutions at which one objective can be improved only with the impairment of one or more the others (i.e., the Pareto front) for decision-making. There are various types of multiple-objective optimization algorithms such as evolutionary algorithms (e.g., Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and tDomination algorithm (tDOM)), and mixed integer linear/nonlinear programming (MILP/MINLP)), which can be categorized into deterministic and stochastic ones, respectively. Deterministic algorithms convert the multiobjective optimization problem into a set of single-objective optimization problems and are prone to converge to local optima, adversely affecting the resolution of the obtained Pareto front (Buck et al., 2020). Stochastic algorithms (e.g., genetic algorithms) can be used to search for global optima and are suitable for optimizing black box problems. The mixed integer programming approach generally achieves convergence to the optimal solution in a few steps with high flexibility and accuracy, and commercial solvers with large-scale capabilities are available, facilitating the application of the approach for optimizing renewable generation ndez-Blanco et al., 2014). This chapter will focus systems (Ferna on the introduction of the mixed integer programming approach. 2. Multiobjective optimization methods 2.1 Framework definition A comprehensive design of waste-to-resource systems needs to consider the whole life cycle of waste management and resource production ranging from the generation, collection and transportation of feedstock, feedstock storage and pretreatment, feedstock treatment, product upgrading and storage, to production distribution and application. The design parameters include types of feedstocks, modes of feedstock collection and transportation, types of products, types of technologies, technological configurations, operation and processing conditions, etc. The optimization problem becomes the generation, evaluation, and selection of the design parameters using a superstructure to achieve the optimal performance of system operation in terms of sustainability metrics. 2.1.1 Data preparation and criteria definition This step aims to define the goal of the optimization problem and collect and study all the required data about the associated
Chapter 11 System optimization waste-to-resource development. This includes the identification of the possible options and configurations for each stage of the development and the compilation of associated data, e.g., sources and types of waste, collection and transportation logistics, types of technologies, technology performance, constraints, energy, material and emission data, product application(s), endof-life arrangement. Heuristic methods can be used beforehand to rule out unqualified technologies based on the comparison of various factors such as process applicability, product yields and quality, operating and maintenance requirements, chemical requirements, and even the availability and quality of data. 2.1.2 Sustainability metrics definition Sustainability criteria against which the performance of a specific design will be evaluated need to be determined. Economic feasibility, environmental impacts, and social impacts are three of the most considered criteria for multiobjective optimization. This means that relevant economic analysis (e.g., costebenefit analysis), environmental impact assessment (e.g., life cycle assessment), and social impact assessment need to be constructed and linked to the associated system design parameters. The social impacts are receiving increasing attention due to the close connection of waste-to-resource systems to the social aspects of local communities and beyond. As compared to the economic and environmental criteria, the social criterion is more abstract and difficult to be quantified as well as with much limited data availability. It was defined that the social aspect should be to ensure people’s sociocultural and spiritual needs in an equitable way for designing sustainable treatment systems (Balkema et al., 2002). Accordingly, some possible quantifiable indicators for the social impacts can be the percentage of waste utilized or treated, number of jobs created, and community size ez et al., 2020). served (Padrón-Pa 2.2 Superstructure generation The possible configurations and their variations regarding equipment, logistics, and process designs that serve as the candidates of waste-to-resource designs are generated (illustrated in Fig. 11.1 where a schematic of different types of configurations and designs for optimizing waste-to-biohydrogen development is presented). This includes considering the different stages of the waste-to-resource process ranging from waste collection and transportation, waste storage and pretreatment, waste treatment, product generation, upgrading and storage, and product distribution and application. Depending on the needs of end-users, the 215
216 Chapter 11 System optimization Figure 11.1 A schematic of different types of configurations and designs for optimizing waste-to-biohydrogen development considering the economic and environment criteria. focus of the optimization problem can be further narrowed down, leading to a reduced system boundary. For each stage, the possible plant locations and capacities, configurations (e.g., conversion pathways), process parameters (e.g., incorporated in chemical process modelling), and performance also need to be mathematically modeled. 2.3 Optimization problem formulation The objective functions in terms of the sustainability criteria and the constraints need to be defined. Accordingly, the multiobjective optimization problem can be formulated mathematically as Min f 1 ðx; yÞ; f 2 ðx; yÞ; :::; f k ðx; yÞ Hðx; yÞ 0 (11.1) Gðx; yÞ ¼ 0 x ˛X y ˛Y where x is a vector of n continuous variables that define process variables (e.g., energy and material flow, emissions, product yields, etc.), y is a vector of m nonnegative integer variables that define the existence of different technologies, feedstocks, logistics plans, configurations, and sequence of events, H(x, y) are inequality constraints, G(x, y) are equality constraints, and fi(i ¼ 1, .,k) is an objective function corresponding to, e.g., economic feasibility, environmental impacts, or social impacts (Collette & Siarry, 2004). Accordingly, the objectives of the optimization problem can be to minimize the costs and socioenvironmental impacts by adjusting various decision variables such as number, capacities, locations, and technology selection and
Chapter 11 System optimization configurations of each processing facility, feedstock selection and logistics (e.g., transportation modes), product yields and energy and resource consumption at each facility, etc. The objective functions need to be mathematically modeled using, e.g., cost-benefit analysis and life cycle assessment, and incorporated into the optimization framework. Various constraints need to be considered in the optimization of waste-to-resource development. For example, the total amount of waste should not exceed its available amount, for which the factors of seasonality and geographical availability may need to be taken into consideration. It should also not exceed an upper bound defined by the productivity and the operation duration of the system. The amount of the waste transported is constrained by the overall transportation capacity. The mass balance of waste needs to be maintained in relevant treatment facilities considering the transported amount, inventory amount, consumed amount, and degraded amount. The total number of treatment facilities for a specific technology can be constrained by a maximum number. The overall system capacity is normally constrained by a maximum capacity. The capital cost, operation and maintenance cost, gate fees, carbon tax, and governmental subsidies may also be subject to some maximum levels. The annual production of products should be smaller than the productivity of the products multiplied by the duration of operation but greater than a minimum amount designed (e.g., defined by a minimum capacity utilization percentage) (You & Wang, 2011). Safety stock levels may be present to constrain the inventory levels of feedstock and/or intermediate products with the consideration of their consumption rates. The reutilization of some of the products (e.g., waste heat recovery to support the drying process) needs to be mathematically reflected via the definition of constraints. The quantity of products distributed to a given area may be constrained by the lower and upper bounds of the demand in the area. The energy- and massrelated constraints are normally mathematically represented using process models and data. The constraints associated with the selection of the different scenarios such as processes, technologies, configurations, feedstock types, etc., can be expressed using logical relations. Additional constraints may include the regulations limiting the types of waste to be treated or product quality requirements (e.g., the purity of biohydrogen for a waste-to-biohydrogen design). 217
218 Chapter 11 System optimization 2.4 Model definition In this step, a set of equations corresponding to the problem defined are proposed to represent the physicochemical and/or biological process and operation of waste-to-resource development and the associated criteria under consideration are evaluated. It may involve, for the thermochemical or biochemical treatment of waste, the analysis of the conservation equations of mass, energy, and momentum or empirical models that can predict the inputeoutput relationships of the treatment technologies. It may also involve the definition of the lower or upper bounds for the associated design parameters. 2.5 Solution strategy The mathematical models developed are solved based on a suitable multiobjective optimization strategy including the analysis of linearity (linear or nonlinear programming) and convexity of the optimization problem. The lexicographic method, ε-constraint method, hybrid method (lexicographic þ hybrid), and Pareto method are some common optimization methods. The lexicographic method supposes that an order has been given among the various objective functions according to their importance or significance instead of by assigning weights. The optimization process starts by minimizing (or maximizing according to the sense of optimization) the most important objective based on the assigned order of the importance of the criteria. An optimal solution is the one that is lexicographically minimum or maximum. However, for this method, the relative importance of each objective must be known beforehand. The ε-constraint method tries to optimize one of the objective functions while converting the others into constraints (within user-specific values). It is suitable for either convex or nonconvex problems (a convex problem with all of the constraints being convex functions, and the objective being a concave (convex) function upon maximization (minimization)), but the value of ε needs to be properly selected so that all the objective function constraints are binding in the ε-constraint problem. The solution strategy can be implemented using computational software such as MATLAB optimization toolbox and GAMS where relevant solvers are available. The software selection is dependent on the availability and user’s knowledge ez et al., 2020). GAMS has been widely used to solve (Padrón-Pa large-scale, complex multiobjective optimization problems. It supplies different solvers for mixed integer programming such
Chapter 11 System optimization as CPLEX, BONMIN (Basic Open-source Nonlinear Mixed Integer Programming), BARON (Branch And Reduce Optimization Navigator), and COUENNE (Convex Over and Under Envelopes for Nonlinear Estimation). CPLEX is one of the leading linear programming solvers and can solve large linear programming, convex mixed integer quadratically constrained problems (MIQCP). It implements a branch-and-bound algorithm (i.e., stepwise enumeration of possible candidate solutions, where subsets of the solution set are checked against the upper and lower estimated bounds of the quantity being optimized) that utilizes linear programming or quadratically constrained programs for bounding. BONMIN is a local solver and suitable for solving nonconvex problems. It ensures global optimal solutions only for convex MINLPs. BARON can solve convex and nonconvex MINLPs based on the implementation of a spatial branchand-bound algorithm utilizing linear programming for bounding. The linear outer-approximation is based on a factorable reformulation of the optimization problem, allowing the application of known convex underestimators for all nonconvex terms (Bussieck & Vigerske, 2010). COUENNE can be used to find global optima for convex and nonconvex MINLPs. It also implements a spatial branch-and-bound algorithm that utilizes linear programming for bounding. When no sound solutions can be obtained, it is necessary to consider redefining the objective functions and constraints in the previous steps. 2.6 Optimal solution identificationdthe pareto method As mentioned above, Pareto optimality is achieved for the multiobjective optimization problem, which means nondominated solutions are generated and they cannot improve one objective without deteriorating the performance of at least one of the others. Since only one of the solutions is needed for practical development, an important decision needs to be made about selecting the best solution among the set of optimal trade-off solutions. Multiple criteria are considered during the optimization process, so a straightforward method for selecting the best solution is based on the multicriteria decision analysis (MCDA) that involves choosing, sorting, and arranging datasets for ranking the trade-off solutions based on a certain preference. The MCDA method generally consists of two stages: a stage where considered criteria are “translated” into grades based on a given grade scale, and a second decision stage where the relative 219
220 Chapter 11 System optimization importance of each criterion is determined based on a weighting procedure (Ahmed et al., 2020). The score of each solution is calculated by the sum of the products of the grades of criteria and their weights. The method also offers the decision-makers the flexibility to assign the weights of the criteria in accordance with their practical demands (e.g., if economics is more important than carbon saving potential, a higher weight can be assigned to the economic criterion). One common approach for MCDA is Analytic Hierarchy Process (AHP) which allows decision-makers to decompose a complex problem into a hierarchy of criteria, subcriteria, and alternatives based on experts’ preference (Lee & Kozar, 2006). The criteria are paired for the comparison of their importance in relation to the goal, while the alternatives are paired to determine which is more preferred for each criterion (Aung et al., 2019). The AHP approach consists of seven steps: (1) Structuring the hierarchy for the decision-making problem (goals at the uppermost level followed by criteria, subcriteria, and alternatives at the lowest level); (2) Constructing the pairwise comparison matrix, Ann where aij indicating the relative importance of criteria i versus j (e.g., aij ¼ 1 if i and j are equally important; aij ¼ 3 if i is slightly more important than j; aij ¼ 7 if i is strongly more important than j; aij ¼ 9 if i is absolutely more important than j), aij ¼ 1 when i ¼ j, and aij ¼ 1 aji ; (3) Constructing the n normalized comparison matrix, Cnn P where cij ¼ aij aij ; i¼1 (4) Constructing the n P cij n; wi ¼ criteria weight vector Wn where j¼1 (5) Calculating the matrix of option scores by (i) constructing a ðkÞ pairwise comparison matrix for each criterion k, Bmm where ðkÞ m is the total number of alternatives and blh denotes the relative performance of the lth alternative compared to the hth ðkÞ alternative regarding the kth criterion (if blh <1, the lth alternative is worse than the hth alternative), and (ii) constructing the score matrix S ¼ sð1Þ .sðnÞ following the same method of step (3) and (4) where sðjÞ is a vector denoting the scores of the alternatives with respect to the jth criterion; (6) Ranking the alternatives by the vector R ¼ S$w with vi denoting the global score of the ith alternative;
Chapter 11 System optimization 221 (7) Consistency checking by calculating the consistency index CI ¼ ðlmax nÞ=ðn 1Þ and consistency ratio CR ¼ CI=RI. lmax is the average of the elements of the vector whose jth element is defined as the ratio between the jth element of A$w and the jth element of w. RI is the randomly generated consistency index (as shown in Table 11.1). It is considered to be tolerable if CR < 0.1. Another popular MCDA method is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (Tzeng & Huang, 2011). TOPSIS assumes that the criteria are monotonically increasing or decreasing. It is based on the comparison of the relative geometric distances between each alternative and the ideal alternative that has the best performance for each criterion. The best solution is chosen as the one that is nearest to the positive ideal solution (utopia point) and furthest away from the negative ideal solution (nadir point). TOPSIS can be divided into seven steps: (1) Creating an evaluation matrix consisting of alternatives and criteria X where xij denote the score of the ith alternative (m alternatives) with respect to the jth criterion (n criteria); for the n criteria, the first k (i.e., from one to k) ones are in the order of monotonically increasing preference while the remaining nek (i.e., from kþ1 to n) criteria are in the order of monotonically decreasing preference; xij (2) Normalizing the matrix to get R where rij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffi Pm ; x2 i¼1 ij (3) Calculating the weighted normalized decision matrix V where vij ¼ wj rij where wj is the weight assigned to the jth criterion; (4) Determining the worst alternative V ¼ v1 ; v2 ; .; vn minimum values where vi ¼ min vij for j ¼ 1; .; k or max vij for j ¼ k þ 1; .; n and the best alternative V ¼ v1 ; v2 ; .; vn maximum values where vi ¼ max vij for j ¼ 1; .; k or min vij for j ¼ k þ 1; .; n; Table 11.1 Values of RI. n 2 3 4 5 6 7 8 9 10 RI 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.51 Pun, K.F., Chin, K.S., & Yiu, M.Y.R. (2010). An AHP approach to assess new product development performance: an exploratory study. International Journal of Management Science and Engineering Management, 5(3), 210e218.
222 Chapter 11 System optimization (5) Calculating the distances between each alternative and the sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n 2 2 P P worst (di ¼ vij vj ) and best (di ¼ vij vj ) j¼1 j¼1 conditions, respectively; (6) Calculating the relative of each alternative to the idea distance solution C ¼ di di þdi ; (7) Ranking the alternatives based on the closeness coefficient C (the higher the coefficient, the better the alternative). This approach offers several advantages including being rational and understandable, simplicity for calculation, the incorporation of importance weights into the comparison procedures, etc. However, TOPSIS potentially leads to the problem of rank reversal: the alternative order of preference changes when an alternative is added to or removed from the decision problem (de Farias Aires & Ferreira, 2019). To deal with this problem, modified TOPSIS methods (e.g., for details please refer to M-TOPSIS and R-TOPSIS) are available (de Farias Aires & Ferreira, 2019; Ren et al., 2007). References Ahmed, A., Sutrisno, S. W., & You, S. (2020). A two-stage multi-criteria analysis method for planning renewable energy use and carbon saving. Energy, 199, 117475. https://doi.org/10.1016/j.energy.2020.117475 Aung, T. S., Luan, S., & Xu, Q. (2019). Application of multi-criteria-decision approach for the analysis of medical waste management systems in Myanmar. Journal of Cleaner Production, 222, 733e745. https://doi.org/ 10.1016/j.jclepro.2019.03.049 Balkema, A. J., Preisig, H. A., Otterpohl, R., & Lambert, F. J. D. (2002). Indicators for the sustainability assessment of wastewater treatment systems. Urban Water, 4(2), 153e161. https://doi.org/10.1016/S1462-0758(02)00014-6 Buck, V. De, Sbarciog, M., & Van Impe, J. (2020). Trade-off-based multi-objective optimisation of a simultaneous saccharification and fermentation process**This work was supported by the ERA-NET FACCE-SurPlus FLEXIBI Project, co-funded by VLAIO project HBC.2017.0176. V. De Buckis supported by FWO-SB Gran. IFAC-PapersOnLine, 53(2), 16884e16889. https://doi.org/ 10.1016/j.ifacol.2020.12.1229 Bussieck, M., & Vigerske, S. (2010). MINLP solver software. file:///C:/Users/ ysm18/Downloads/7195_BuVi2010_MINLPSoftware (1).pdf. Collette, Y., & Siarry, P. (2004). Multiobjective optimization: Principles and case studies. Springer Science & Business Media. Springer Science & Business Media. de Farias Aires, R. F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84e97.
Chapter 11 System optimization ndez-Blanco, R., Arroyo, J. M., & Alguacil, N. (2014). Consumer payment Ferna minimization under uniform pricing: A mixed-integer linear programming approach. Applied Energy, 114, 676e686. https://doi.org/10.1016/ j.apenergy.2013.10.015 Lee, Y., & Kozar, K. A. (2006). Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach. Decision Support Systems, 42(3), 1383e1401. https://doi.org/10.1016/j.dss.2005.11.005 ez, J. I., Almaraz, S. D.-L., & Roma n-Martínez, A. (2020). Sustainable Padrón-Pa wastewater treatment plants design through multiobjective optimization. Computers & Chemical Engineering, 140, 106850. https://doi.org/10.1016/ j.compchemeng.2020.106850 Ren, L., Zhang, Y., Wang, Y., & Sun, Z. (2007). Comparative analysis of a novel M-TOPSIS method and TOPSIS. Applied Mathematics Research EXpress, 2007. Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: Methods and applications. CRC press. You, F., & Wang, B. (2011). Life cycle optimization of biomass-to-liquid supply chains with distributedecentralized processing networks. Industrial & Engineering Chemistry Research, 50(17), 10102e10127. https://doi.org/ 10.1021/ie200850t 223
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Perspectives of future development 12 Abstract This chapter revisits the challenge of sustainable waste management and summarizes the content of this book. The importance of applying wasteto-resource development to supplement the “reduce, reuse, and recycle” (3R) strategy, and the barriers against sustainable waste management in rural areas are highlighted. It emphasizes that the demands of local residents need to be met for promoting the engagement of rural residents as one of the key factors affecting the sustainability of waste-toresource development in rural areas. This chapter promotes the concept of decentralized systems which can better cater to the demands of end users. Finally, this chapter suggests four future directions to pursue for enhancing waste-to-resource development. Keywords: Decentralized development; Policies and incentives; Public engagement; Rural waste management; Sustainable development. As the continuous economic development and population expansion, the problem of waste management will become increasingly prominent. Sustainability in waste management will play a greater role in achieving sustainable development. It is undoubtful that the existing hierarchical waste management guidelines serve as the basis for designing the overall waste management roadmap. Particularly, 3R (“Reduce, Reuse, and Recycle”) has been promoted by the Division for Sustainable Development Goals, the United Nations for sustainable waste management. However, considering the varied compositions and value of wastes, the 3R strategy alone is insufficient to curb the rapid generation and increasing threat of waste to the environment and ecosystems. Moreover, the effectiveness of the 3R strategy can be adversely affected by the limited waste management infrastructure and lack of plans in developing countries. Efficient and environmentally friendly technologies are required to handle the waste that cannot be managed via the 3R strategy. Waste-to-Resource System Design for Low-Carbon Circular Economy. https://doi.org/10.1016/B978-0-12-822681-0.00002-5 Copyright © 2022 Elsevier Inc. All rights reserved. 225
226 Chapter 12 Perspectives of future development As mentioned previously, the conventional practices for handling nonrecyclable waste (e.g., landfill) are losing appeal due to their adverse environmental impacts. Advanced resource recovery from waste via the different types of waste-to-resource technologies offers alternatives for supplementing the waste management technology portfolio. However, the successful addition of resource recovery as a tier in the waste management hierarchy is dependent on the understanding of local context for waste-to-resource development. The success of waste-toresource development is also contingent upon the participation and cooperation of the public as well as effective policy supports. Lack of awareness appears to be one of the key barriers against the effective management of waste (Garnett & Cooper, 2014). Hence, the waste-to-resource approach needs to work complementarily with the 3R approach. Both of the approaches need to be supported by educational initiatives to enhance public awareness for tackling the challenge of sustainable waste management (SWM). On the other hand, reduced, reused, recycled, and recovered resources that precisely match the socioeconomic, energy, and environmental demands of communities will accelerate the uptake of such initiatives and lead to higher public engagement. This calls for the development of a sustainable, systematic, and hierarchical waste management roadmap that clearly defines the relative roles and effects of the measures and includes the steps or milestones needed to achieve SWM. Additionally, rural areas account for large sources of waste pollution (Han et al., 2018); however, waste management in rural areas has received much less attention as compared to their urban counterparts. The problem is likely to be even worse for low- and middle-income countries (LMICs) considering their rudimentary waste management infrastructure and prevalence of improper waste management practices, such as uncontrolled waste disposal, open field burning, or dumping in rivers (Horodytska et al., 2019). Around 1.9 billion people lack waste collection services in rural areas and the service coverage rate for the rural population is under 50% for countries like India and Malaysia (Mihai, 2017). Hence, the waste management pollution in the rural areas is predisposed to be made in an uncontrolled manner, with wide ecological, health, and socioeconomic impacts. Unfortunately, knowledge of effective waste management in rural areas of LMICs is limited for all related stakeholders (e.g., rural households, industry, governmental agencies, etc.), which renders existing measures ineffective and discourages sustainable effort. Waste management endeavors are further complicated by socioeconomic, environmental, and geographical factors. For example, populated remote rural areas are usually the most neglected by waste management services. Waste operators may
Chapter 12 Perspectives of future development avoid such areas, and local authorities may provide no or insufficient financial resources to provide appropriate public services. Improper waste management practices have been prevalent owing especially to lack of awareness of consequences, and prevalence of poor waste collection infrastructure (Janmaimool & Denpaiboon, 2016). The geographical constraints (mountains, hills, high plateaus, and wetlands) create additional barriers to implementing proper waste management facilities (Mihai & Taherzadeh, 2017). All of these factors suggest that waste management in rural areas will need to be tailored to local circumstances, and based on systematic datasets that help identify appropriate solutions. Indeed, if people do not see their lives and livelihoods as being intertwined with the crisis and identify their own behaviors as contributing to the problem, they will not be incentivized to change behaviors and adopt more appropriate approaches or modes to support sustainable waste management (Marks et al., 2020). Social media and the derived interactive dialogue represent a promising channel for engaging with the public in real-time, two-way communication (Lee et al., 2018). Its application toward waste reduction and management is still at a premature stage and calls for systematic scientific evidence and effective policy incentivization. Small-scale, decentralized deployment of waste-to-resource systems offers some advantages including the reduction of transportation expenses, minimization of pollutant or pathogen transmission, and higher flexibility in catering to the demands of local residents which is key for promoting the engagement of rural residents. What makes distributed waste-to-resource systems outstanding is their potential to foster a culture of energy and environmental conservation by bringing residents and communities closer to the notion of sustainability. As compared with large-scale, centralized development, decentralization falls short of economic profitability, preventing its quick and widespread deployment. But for remote areas or islands for which it is costly to transmit the resources (e.g., energy) from centralized systems, decentralized systems can be still preferred in terms of technoeconomic feasibility (You et al., 2017). For a set of decentralized systems that serve a series of smaller areas within a large one, the design of the system networks with the consideration of the sourcing of the feedstocks and the distribution of end-products become essential to maximize the performance of the systems. It is necessary to consider the supply chain and demand management of waste and energy in the network modeling. The design of the supply chain critically determines the impacts of waste-toresource systems due to the potential geographical widespread of waste feedstocks and consumer zones and the seasonality of 227
228 Chapter 12 Perspectives of future development biomass feedstocks (Chaplin-Kramer et al., 2017; Field et al., 2018). This adds spatial and temporal dimensions to the assessment of the potential of waste-to-resource development, and in this case, transportation network and modes, distance, and intermodal-transportation becomes important designing parameters. The waste-to-resource development will impact local communities in the aspects of economy, environment, and social development (Woolf et al., 2016). The actual implementation is subject to its social acceptability and benefits which may be directly reflected by its ability to create job and affect income. The environmental impact stems from its ability to tackle the crisis of fossil fuel depletion, global climate change and waste pile-up, as well as its complication with the development of associated ecosystems. The economic feasibility of the implementation critically determines its sustainability and the formulation of governmental subsidies. Moreover, although different stakeholders, i.e., policymakers, investors, and consumers, have different preferences on the impacts, it is important to incorporate the analysis of all the three impacts into the decision-making process. Hence, it is important to develop consistent and comprehensive databases and protocols which will facilitate cross-study comparison and the formulation of circular economy concept across boundaries. As an example, biohydrogen production with locally available organic waste is attractive due to potential savings in costs and emissions (Singh et al., 2015). As mentioned in Chapter 4, waste-to-biohydrogen technologies serve an alternative solution for low-carbon hydrogen production with locally available waste biomass, leading to the “Waste-to-Wheel” strategy upon the use of hydrogen for fuel cell electric vehicles (FCEVs). The “Wasteto-Wheel” strategy will contribute to developing both lowcarbon transportation infrastructure and waste management practices. The geographical distribution of waste-to-biohydrogen systems coincides with that of the hydrogen demand of FCEVs. Communities are a key stakeholder of waste management and hydrogen-fuelled transportation by being the ‘supplier’ of waste biomass, the ‘carrier’ of WTH systems, and the ‘user’ of FCEV services (Woolf et al., 2016). Decentralized waste-to-hydrogen systems have to be well coordinated so that the biohydrogen production in each area and all areas is able to cater to the varying demands of the community and all as a whole. As another example, the wide range of application possibilities and associated positive environmental benefits are important factors affecting the economic viability of biochar production. Large-scale biochar production is currently largely based on the soil application of biochar, while other applications showing great economic potential are still in the stage of feasibility or
Chapter 12 Perspectives of future development lab-scale research. Anyway, the deployment of waste-to-biochar systems needs to be subject to a comprehensive evaluation of their interests with investors, policymakers, and end-users. Such evaluation needs to consider the economic, energy, and environmental impacts of biochar production and application which are contingent upon the types of feedstocks and technologies as well as process conditions (You et al., 2020). Biochar is often produced as by-product or coproduct of a multigeneration process, and it is key to plan biochar production from a whole system perspective that takes the economic and environmental values of all products into consideration. In this case, it is critical to understand the relationships between process conditions and the relative variations of the multigeneration. The economic, environmental, and social aspects are correlated with each other, affecting the practical implementation of waste-to-resource systems. Low system efficiencies and high collection, transportation, and pretreatment costs often make a system economically infeasible. Decentralized systems serving to reduce the costs related to the collection and transportation of waste but may suffer from the problems of low efficiencies and productivities. Product sale, carbon tax, and financial subsidies are potential income sources, while the prices of bioproducts are subject to their practical usefulness, system profitability, and affordability of associated end-users (You et al., 2020). The multigeneration potential of waste-to-resource development offers the possibility to meet the varying demands of end-users and promote the development of integrated concepts toward improved process and resource efficiencies. Accurate process control toward effective and reliable productivity and quality management will play a critical role in promoting the widespread deployment of waste-to-resource systems and ensuring robust decision-making. Overall, four aspects of improvement need to be pursued to support the development of decentralized and centralized waste-to-resource development: (i) understanding the conditions and demands (i.e., feedstock and technology selection) of local communities where a waste-to-resource system will be deployed to facilitate the production well matches with the demands of the communities, (ii) improving the energy efficiency and productivity of the technology selected, (iii) optimizing the overall performance of the decentralized development considering economic and socioenvironmental impacts, and (iv) formulating targeted incentives and policies by considering the socioenvironmental impacts of waste-to-resource development. This justifies one of the major purposes of this book where major technologies and analysis methods and datasets are presented. 229
230 Chapter 12 Perspectives of future development References Chaplin-Kramer, R., Sim, S., Hamel, P., Bryant, B., Noe, R., Mueller, C., Rigarlsford, G., Kulak, M., Kowal, V., & Sharp, R. (2017). Life cycle assessment needs predictive spatial modelling for biodiversity and ecosystem services. Nature Communications, 8(1), 1e8. Field, J. L., Evans, S. G., Marx, E., Easter, M., Adler, P. R., Dinh, T., Willson, B., & Paustian, K. (2018). High-resolution technoeecological modelling of a bioenergy landscape to identify climate mitigation opportunities in cellulosic ethanol production. Nature Energy, 3(3), 211e219. Garnett, K., & Cooper, T. (2014). Effective dialogue: Enhanced public engagement as a legitimising tool for municipal waste management decision-making. Waste Management, 34(12), 2709e2726. Han, Z., Liu, Y., Zhong, M., Shi, G., Li, Q., Zeng, D., Zhang, Y., Fei, Y., & Xie, Y. (2018). Influencing factors of domestic waste characteristics in rural areas of developing countries. Waste Management, 72, 45e54. Horodytska, O., Cabanes, A., & Fullana, A. (2019). Plastic waste management: Current status and weaknesses. Janmaimool, P., & Denpaiboon, C. (2016). Evaluating determinants of rural Villagers’ engagement in conservation and waste management behaviors based on integrated conceptual framework of Pro-environmental behavior. Life Sciences, Society and Policy, 12(1), 1e20. Lee, N. M., VanDyke, M. S., & Cummins, R. G. (2018). A missed opportunity?: NOAA’s use of social media to communicate climate science. Environmental Communication, 12(2), 274e283. Marks, D., Miller, M. A., & Vassanadumrongdee, S. (2020). The geopolitical economy of Thailand’s marine plastic pollution crisis. Asia Pacific Viewpoint, 61(2), 266e282. Mihai, F.-C. (2017). One global map but different worlds: Worldwide survey of human access to basic utilities. Human Ecology, 45(3), 425e429. Mihai, F., & Taherzadeh, M. (2017). Rural waste management issues at global level (Introductory chapter). Mihai FC and Taherzadeh M J, 1e10. Singh, S., Jain, S., Venkateswaran, P. S., Tiwari, A. K., Nouni, M. R., Pandey, J. K., & Goel, S. (2015). Hydrogen: A sustainable fuel for future of the transport sector. Renewable and Sustainable Energy Reviews, 51, 623e633. Woolf, D., Lehmann, J., & Lee, D. R. (2016). Optimal bioenergy power generation for climate change mitigation with or without carbon sequestration. Nature Communications, 7(1), 1e11. You, S., Li, W., Zhang, W., Lim, H., Kua, H. W., Park, Y.-K., Igalavithana, A. D., & Ok, Y. S. (2020). Energy, economic, and environmental impacts of sustainable biochar systems in rural China. Critical Reviews in Environmental Science and Technology, 1e29. You, S., Tong, H., Armin-Hoiland, J., Tong, Y. W., & Wang, C.-H. (2017). Technoeconomic and greenhouse gas savings assessment of decentralized biomass gasification for electrifying the rural areas of Indonesia. Applied Energy, 208, 495e510. https://doi.org/10.1016/j.apenergy.2017.10.001
Index Note: ‘Page numbers followed by “f” indicate figures and “t” indicate tables.’ Acetogenesis, 38e39 Acetyl valves, 109e110 Acid-catalyzed transesterification, 132 Acidogenesis, 38 Activated carbon, 84 AD. See Anaerobic digestion (AD) Adenosine triphosphate (ATP), 58 Adsorption technologies, 67e68 Agricultural biomass, 11e12 Agricultural waste, 10e12 in China, 11 compositions of, 14t higher heating value (HHV), 14t inappropriate utilization of, 11e12 in India, 11 lignocellulosic biomass, 11 Air-to-fuel ratio (AF), 23 Alkali and alkaline earth metallic (AAEM) species, 33 Amine-based chemical absorption, 92 Ammonia (NH3), 62 feedstocks, 79e80 syngas cleanup, 63 Anaerobic bacteria, 55e56 Anaerobic digestion (AD), 4, 10, 37e40 acetic acid, 38 acetogenesis, 38e39 acidogenesis, 38 biochar, 140e141 digestate, 37e38 efficiency data for, 40, 40t food waste, 39e40 hydrolysis, 38 methanogenesis, 39 microorganisms, 40, 78 organic fraction of municipal solid waste (OFMSW), 78e79 performance of, 39 retention time, 81e82 siloxanes, 86 stability of, 40 waste feedstocks, 39e40 Anaerobic fluidized bed reactor (AFBR), 56e57 Analytic hierarchy process (AHP), 219e221, 221t Annular-hybrid bioreactor (AHB), 56e57 ASTM D6751, 120e121 Attributional life cycle assessment (LCA), 197e198 Auger type pyrolysis system, 151e153, 152f BARON, 218e219 Bench-scale fluidized bed pyrolysis, 153, 154f BiGchar, 29 Biochar, 29, 34, 228e229 additive, 140e141 adsorbents, 139e140 agrarian benefits, 138 catalyst support, 139 electrochemical applications, 140 gasification, 154e156 inorganic matter contents, 138 porous structure of, 138 pyrolysis of, 30, 144e154 auger reactor, 151e153, 152f beehive kilns, 149f, 150 bench-scale fluidized bed pyrolysis, 153, 154f fast, 147t, 148f fluidized bed reactors, 153 hot-tail kilns, 149, 149f metal kilns, 150e151 practical implementation of, 147e149 rectangular kilns, 149f, 150 retorts, 151 slope kilns, 149f, 150 slow, 146t, 148f strength and attractiveness of, 152f twin-screw configuration, 153 yield and physicochemical properties of, 144e146, 146te147t soil application of, 138e139 thermochemical processes, 139 torrefaction, 141e144, 143f direct heating reactor, 142 feedstocks, 144 fluidized bed reactors, 142 gas components, 141e142 indirect heating reactor, 142 liquid components, 141e142 moving bed reactor, 142e143 oxidative, 143e144 pretreatment, 141e142 production of, 143e144 quality of, 143 yield and physicochemical properties, 145t Biodesulfurization, 85f chemotrophic bacteria, 84e85 influential factors of, 85e86
232 Index Biodiesel biological sources, 119e120 classification of, 122e124 first-generation, 122e123 fourth-generation, 124 second-generation, 123 third-generation, 123e124 concentration of, 120 consumption, 120 with diesel, 120 production of, 125e131 alcohol/oil molar ratio, 127 calcium carbonate (CaCO3), 131 calcium oxide (CaO), 131 catalysts, 127e129 chemical catalyst-based transesterification processes, 130e131 heterogeneous solid catalysts, 129e130 higher molecular weight alcohols, 126 pyrolysis, 125 transesterification, 125, 128t, 130 waste cooking oil, 126e127 properties of, 120e122 cetane number of, 121 composition, 121e122 particulate matters (PMs), 121e122 viscosity, 121 soil and water, 124e125 system composition of, 169f waste biomass, 119e120 Bioethanol cost-benefit analysis (CBA), 169f fermentation, 103e107 first-generation, 101e103 lignocellulosic feedstocks, 108t pretreatment of, 108e111 acids, 109 biological method, 110e111 feedstock, 108 lime, 109e110 liquid hot water, 109 physical method, 110 saccharification, 103e107 second-generation, 102f, 103 yeasts, 111e113 Biogas, 10 contamination, 82e88 concentrations (ppm) of, 83t halogen, 87e88 H2S, 83e86 siloxanes, 86e87 production of, 77e82 feedstock, 78e80 pH, 81 retention time, 81e82 temperature, 80e81 upgrading, 88e96 chemical absorption, 90e93, 91f comparison of, 89t membrane separation, 93e95, 94f pressure swing adsorption (PSA), 95 pressurized water scrubbing (PWS), 88e90 Biohydrogen, 5. See also Wasteto-biohydrogen (WtH) dark-fermentation, 55e56 production, 228 Biomacon, 29 Biomass Crop Assistance Program (BCAP), 172e174 Biomass feedstocks, 227e228 Biooil, 29 steam reforming, 52e53 Biotrickling filters (BTF), 84, 85f BONMIN, 218e219 Boudouard reaction, 51 Bubbling fluidized bed (BFB), 26e27 gasifiers, 36 torrefaction system, 142, 143f Candida antarctica, 130e131 CAPEX, 163, 165e170, 166te167t Carbon capture and storage (CCS), 30 Carbon dioxide (CO2), 16 Carbonization, 28e29 Carbon saving potential, 1e2 Carbon sequestration, 137e138 Carbon tax, 174 Carboxydothermus hydrogenoformans, 66 CBA. See Cost-benefit analysis (CBA) Char, 34 Chemical absorption, 90e93, 91f alkali solvents, 93 amines/by-products, 92 caustic solvents, 92e93 saturated scrubbing solution, 92 solvent selection, 91e92 Chemical Engineering Plant Cost Index (CEPCI), 162e163 Chemical oxygen demand (COD), 55e56 Chemotrophic bacteria, 84e85 Chlorella vulgaris, 154 Circulating fluidized bed (CFB), 26e27 gasifiers, 36 Climate change, 1e2, 21, 206e207 Clostridium butyricum, 81 Collection and transport (C&T) cost, 163 Combined heat and power (CHP), 22 Consequential life cycle assessment (LCA), 197e198 e-Constraint method, 218 Continuous stirred tank reactor (CSTR), 56e57 Conventional practices, 3e4 Cost-benefit analysis (CBA), 161 mathematical principles, 162e174 CAPEX and OPEX, 165e170, 166te167t cost and benefit components, 162e163 economic indicators, 162 external costs, 170e171 project incomes, 171e174
Index reported prices of products, 173t UK gate fees, 173t waste collection and transport, 163e165 uncertainties, 183e184 waste-to-resource development, 174e183 biochar, 183 biodiesel, 181e182 bioethanol, 178e181, 179fe180f biohydrogen, 176e177 biomethane, 177e178 waste-to-energy (WTE), 175e176 Cost-benefit ratio (CBR), 162 COUENNE, 218e219 CPLEX, 218e219 Cryogenic/low-temperature separation, 71 Dark-fermentation, 55e57 anaerobic fluidized bed reactor (AFBR), 56e57 annular-hybrid bioreactor (AHB), 56e57 biohydrogen producers for, 56 chemostat bioreactor, 56e57 continuous stirred tank reactor (CSTR), 56e57 fixed-bed bioreactor, 56e57 hydrogen productivity of, 57 reactor designs for, 56 up-flow anaerobic packed bed reactor (UAnPBR), 56e57 up-flow anaerobic sludge blanket (UASB), 56e57 Data compilation, 162e163 Decarburization, 35e36 Dehydrogenation, 35e36 Deterministic algorithms, 214 Diesel, 119 Digestate, 37e38 Direct carbon fuel cells (DCFCs), 140 Downdraft gasifier, 35e36 Dry cold gas, 63e64 Economics, 161 feasibility, 5e6, 174e183 indicators, 162 Electrical swing adsorption (ESA), 70e71 Electrocatalysts, 140 Elevated-temperature pressure swing adsorption (ETPSA), 68e69 Empty bed residence time (EBRT), 85e86 EN 14214, 120e121 Energy-from-waste (EfW), 21e22 Energy security, 21 Entrained flow gasifiers, 37 Equivalence ratio (ER), 23 Ethanol, 101 European Commission, 3e4 European Commission Waste Framework Directive, 9e10 Evolutionary algorithms, 214 Feed-in-Tariffs (FiT), 172 Feedstocks, 78e80 ammonia (NH3), 79e80 chemical composition of, 78e80 lignocellulosic, 107 nonedible, 122e123 pretreatment, 101e103, 108 size of, 78e79 Fermentation, 53e60, 103e107 dark, 55e57 microorganisms, 53e55 photo, 57e60 First-generation bioethanol, 101e103, 102f FischereTropsch synthesis, 62 Fixed bed gasifiers, 34e35 Fluidized-bed incineration, 25e26, 25f Food versus fuel debate, 101e103 Food waste, 39e40 Fossil fuels, 21, 119e120 Free fatty acids (FFAs), 123e124 Fuel cell electric vehicles (FCEVs), 48, 228 233 GAMS, 218e219 Gasification, 31e37, 48e53 agents, 33 alkali and alkaline earth metallic (AAEM) species, 33 biochar, 34, 154e156, 155f small- to medium-scale, 155e156 biooil, 52e53 char, 34 efficiency data for, 37t hydrothermal, 49e50 oxidation, 32e33 parameters, 31e32 vs. pyrolysis, 32 reactors, 34e35, 34f reduction, 33 steam, 50e51 syngas, 34 Gasoline, 119 GB/T19147-2003, 120e121 Global warming potential (GWP), 122e123 Governmental regulations, 172e174 Grease trap waste (GTW), 169e170 Gross Domestic Product (GDP), 12e13 Halogen, 87e88 Halothiobacillus neapolitanus, 84e85 Heavy metal pollution, 2 Heterogeneous catalysts, 50 Higher heating value (HHV), 13e14, 14t High-pressure water scrubbing (HPWS), 89e90 Hot combustion gas, 22 Human Development Index (HDI), 12e13 Hydraulic retention time (HRT), 53e55 Hydrogen, 47 selectivity and permeability, 67
234 Index Hydrogen (Continued) upgrading and purification processes, 70f Hydrothermal gasification, 49e50 catalysts, 50 gasification and pyrolysis, 53, 54te55t temperature, 49e50 Incineration, 22e28 air-to-fuel ratio (AF), 23 char gasification, 22e23 devolatilization and char formation, 22e23 efficiency data for, 29t electricity consumption of, 27e28 equivalence ratio (ER), 23 flowing airstream, 26e27 fluidized-bed incinerator, 25e26, 25f mass-feed approach, 24e25 moisture evaporation, 22e23 moving grate incinerator, 24e25, 24f reactors of, 24e25 rotary kiln incinerators, 27, 28f rotating fluidized bed (RFB), 26, 27f stoichiometric combustion reaction, 22e23, 23t volatile combustion, 22e23 Industrial waste, 9e10 rules and methods of, 10 Input-output life cycle assessment (IO-LCA), 196e197 In situ desulfurization, 65 Internal rate of return (IRR), 162 International Biochar Initiative, 30 International Energy Agency (IEA), 4, 21 International Organization for Standardization (ISO), 190 International Reference Life Cycle Data System, 197e198 Ionic liquids (ILs), 89e90 Iron oxide adsorbents, 83e84 ISO 14040, 190 ISO 14044, 190 Jatropha oil, 123 Landfills digestate, 175 incineration and, 3e4 pollutants, 3e4 LCA. See Life cycle assessment (LCA) LCI. See Life cycle inventory (LCI) LCIA. See Life cycle impact assessment (LCIA) Lexicographic method, 218 Life cycle assessment (LCA) allocation, 198e200 avoiding, 198e199, 198f physical relationships, 199 relationships, 199e200 attributional, 197e198 consequential, 197e198 cradle-to-cradle, 190 cradle-to-gate, 190 cradle-to-grave, 190 definition of, 190 development of, 190, 193f goal and scope definition, 191e193 biomethane production, 191 functional unit, 192 system boundary, 192e193, 193f input-output, 196e197 interpretation, 195e196 life cycle impact assessment (LCIA), 194e195 life cycle inventory (LCI), 193 process-based, 196e197 standards, 190 uncertainty analysis, 207e208 waste-to-biochar, 206e207 waste-to-biodiesel, 204e206 waste-to-bioethanol, 203e204 waste-to-biohydrogen, 201e202 waste-to-biomethane, 202e203 waste-to-energy (WtE), 200e201 Life cycle impact assessment (LCIA), 194e195, 194t Life cycle inventory (LCI), 193 Lignin valves, 109e110 Lignocellulosic feedstocks, 107, 108t Lime pretreatment, 109e110 Long-chain fatty acids (LCFAs), 79e80 Marine plastic pollution, 2 MATLAB optimization toolbox, 218e219 Media-G2, 83e84 Membrane separation technologies, 66e67, 93e95, 94f Methane (CH4), 16 Methanogenesis, 39 Methyldiethanolamine (MDEA), 68 Microalgae, 123e124 Microwave-assisted pyrolysis, 30 Microwave irradiation, 30 Minimum ethanol selling price (MESP), 162 Mixed integer linear/nonlinear programming (MILP/ MINLP), 214 Monoethanolamine (MEA), 68 Moving grate incineration, 24e25, 24f MSW. See Municipal solid waste (MSW) MSW-Collect, 164e165, 165f Mucor miehei, 130e131 Multicriteria decision analysis (MCDA), 219e221
Index Multiobjective optimization methods, 214e222 framework definition, 214e215 criteria, 214e215 data preparation, 214e215 sustainability metrics, 215 model definition, 218 optimal solution identification, 219e222 optimization problem formulation, 216e217 solution strategy, 218e219 superstructure generation, 215e216, 216f Municipal solid waste (MSW), 1 in China, 12 components, 15e16 definition of, 12 in Eastern European cities, 12e13 gasification, 31e32 incineration, 4, 9e10 mass and volume of, 9e10 open dumpsites/landfills, 3e4 properties of, 15t in Switzerland, 12 Municipal Solid Waste Rules 2000, 12 Nitrogen, 78 Non-dominated Sorting Genetic Algorithm-II (NSGA-II), 214 Nuclear waste, 10 rules and methods of, 10 sources of, 10 Open dumping, 3e4 Operation and maintenance (O&M) cost, 163, 166te167t OPEX, 165e170, 166te167t Optimal solution identification, 219e222 Optimal steam-to-feedstock ratio, 51 Optimization problem formulation, 216e217 Organic fraction of municipal solid waste (OFMSW), 78e79 ORWARE model, 163e164 Oxidative torrefaction, 143e144 Pareto method, 218e222 Particulate matters (PMs), 121e122 Petro-diesel, 120 vs. biodiesel, 121 Phosphorus, 78 Photo-fermentation, 55e60 advantages of, 57e58 automated control bioreactor, 59, 69f hybrid/integrated systems, 59e60 hydrogen yields of, 58e60, 60t 4m3 pilot-scale baffled continuous flow photoreactor, 58e59, 59f Presaccharification and simultaneous saccharification and fermentation (PSSF), 103e104, 105f Pressure swing adsorption (PSA), 69e70, 95 Pressurized water scrubbing (PWS), 88e90 Process-based life cycle assessment (LCA), 196e197 Pyrolysis, 28e31 biochar, 144e154 auger reactor, 151e153, 152f beehive kilns, 149f, 150 bench-scale fluidized bed pyrolysis, 153, 154f fast, 147t, 148f fluidized bed reactors, 153 hot-tail kilns, 149, 149f metal kilns, 150e151 practical implementation of, 147e149 rectangular kilns, 149f, 150 retorts, 151 slope kilns, 149f, 150 slow, 146t, 148f 235 strength and attractiveness of, 152f twin-screw configuration, 153 yield and physicochemical properties of, 144e146, 146te147t biochar production, 30 biodiesel, 125 carbonization, 28e29 efficiency data for, 31t fast, 29 microwave-assisted, 30 production, factors, 30e31 reactors, 30e31 slow, 29 system design, 156, 157f Pyruvate ferredoxin oxidoreductase (PFOR), 55e56 Pyruvate formate-lyase (PFL) pathway, 55e56 Radioactive waste, 10 Raney nickel catalysts, 50 Reduce, reuse, and recycle (3R) methods, 2e3, 225 Resource recovery (RR), 226 Retention time, 81e82 Rhodospirillum rubrum, 66 Rotary kiln incinerators, 27, 28f Rotating fluidized bed (RFB), 26, 27f Rural waste management, 17 Saccharification, 103e107 Second-generation ethanol, 102f, 103 Separate hydrolysis and fermentation (SHF), 103e104, 104f Siloxanes, 86e87 Simultaneous saccharification and co-fermentation (SSCF), 103e104 Simultaneous saccharification and fermentation (SSF), 103e104, 104f
236 Index Solar energy, 124 Solid sorbents, 87 Solution strategy, 218e219 Steam gasification, 50e51 Steam-to-feedstock ratio, 51 Stochastic algorithms, 214 Sulfide-oxidizing bacteria (SOB), 83 SulfaTreat, 83e84 Sulfur, 78 Sulfur-Rite, 83e84 Supercapacitors, 140 Superstructure generation, 215e216, 216f Sustainability metrics, 215 Sustainable development goals (SDGs), 2 Sustainable waste management (SWM), 1 hierarchical strategy for, 2e3, 3f reduce, reuse, and recycle (3R) methods, 2e3 Syngas, 4 cleanup, 61e65 ammonia, 63 contaminants, 61e62 dry cold gas, 63e64 hot gas, 64 in situ desulfurization, 65 water, 62 wet cold gas, 63 energy forms, 34 System optimization, 213e214 multiobjective optimization methods, 214e222 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), 221e222 Temperature swing adsorption (TSA), 69, 71f Thauer limit, 56 TorrCoal, 142, 143f Torrefaction, 141e144, 143f direct heating reactor, 142 feedstocks, 144 fluidized bed reactors, 142 gas components, 141e142 indirect heating reactor, 142 liquid components, 141e142 moving bed reactor, 142e143 oxidative, 143e144 pretreatment, 141e142 production of, 143e144 quality of, 143 yield and physicochemical properties, 145t TORSPYD, 142, 143f Transesterification, 125 acid-catalyzed, 126 advantages and disadvantages of, 128t alkaline-catalyzed, 126 catalysts, 127e129 configurations, 132 esterified low-acidity oil, 132 low-carbon option for, 126 mutton tallow, 131f Triethanolamine (TEA), 68 Tar, 61e62 tDomination algorithm (tDOM), 214 Updraft fixed bed gasifier, 35 Up-flow anaerobic packed bed reactor (UAnPBR), 56e57 Up-flow anaerobic sludge blanket (UASB), 56e57 Volatile methyl siloxanes (VMS), 86. See also Siloxanes Waste, See also specific types definition of, 9e10 incineration, 4 mismanagement, 2 properties of, 13e16 higher heating value (HHV), 13e14 Waste oils, 123e124 Waste-to-biohydrogen (WtH), 47e48 carbon footprints, 58f product upgrading, 61e72 separation and purification, 66e72 syngas cleanup, 61e65 thermochemical, 48e60 fermentation, 53e60 gasification, 48e53 Waste-to-energy (WTE), 21e22, 175e176 anaerobic digestion (AD), 37e40 gasification, 31e37 incineration, 22e28 pyrolysis, 28e31 Waste-to-resource systems, 5, 16 Waste-to-Wheel strategy, 48, 228 Wet cold gas, 63 Yeasts, 111e113, 113t Zymomonas mobilis, 178e179