Applied Science, Faculty of
Chemical and Biological Engineering, Department of
DSpace
UBCV
Lucero Saucedo, Sofia Liliana
Master of Applied Science - MASc
University of British Columbia
Anaerobic digestion (AD) systems offer a sustainable approach for biomass resource recovery and energy production. This study explores the potential of solid digestate-derived biochar as an additive to enhance the stability and performance of AD systems, focusing on the effects of biochar dosage, pyrolysis temperature, and pyrolysis catalyst on methane production. Two series of tests were conducted: Test Series 1 using a mixture of chicken, hog, and dairy manure as the substrate, and Test Series 2 employing synthetic food waste. In Test Series 1, biochemical methane potential (BMP) tests were performed with biochar dosages of 0.1 and 0.6 g/g Total Solids. Results showed a marginal improvement in specific methane yield (SMY) at the lower dosage, but an increase of 9.0-53.5% at the higher dosage across all biochar types compared to the control. Hence, the study found a dosage-dependent relationship. The influence of pyrolysis temperature (400°C vs. 500°C) and catalysts (20 wt% K3PO4 vs. 10 wt% K3PO4/10 wt% clinoptilolite) on SMY exhibited opposite trends, and no direct correlation was found between specific surface area of biochar and SMY. In Test Series 2, BMP assays using synthetic food waste revealed a dosage-dependent relationship with SMY. At a lower biochar dosage (0.1 g/g TS), improvements in methane production were marginal, while a higher dosage (0.6 g/g TS) increased methane content by at least 10%, with yield improvements ranging from 35% to 52%. A three-way ANOVA indicated that biochar dosage significantly influenced methane yield, while pyrolysis temperature and catalyst had no statistically significant impact. Multiple linear regression analysis identified the dosage-adjusted aromaticity index as a significant factor affecting SMY, with a unit increase correlating to a 327 mL/g VS increase in SMY. The findings highlight the potential of solid digestate-derived biochar as a valuable additive in AD systems. Optimizing biochar dosage, feedstock, pyrolysis temperature, and catalyst can significantly enhance methane production and process stability. Tailored biochar properties and production conditions offer promising opportunities for improving the efficiency and sustainability of AD systems.
Anaerobic digestion of organic waste mixtures with the addition of biochar by Sofia Liliana Lucero Saucedo B.S., Monterrey Institute of Technology and Higher Education, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2024 © Sofia Liliana Lucero Saucedo, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Anaerobic digestion of organic waste mixtures with the addition of biochar submitted by Sofia Liliana Lucero Saucedo in partial fulfilment of the requirements for the degree of Master of Applied Science in Chemical and Biological Engineering Examining Committee: Anthony Lau, Associate Professor, Chemical and Biological Engineering, UBC Supervisor Xiaotao Bi, Professor, Chemical and Biological Engineering, UBC Supervisory Committee Member Susan A. Baldwin, Professor, Chemical and Biological Engineering, UBC Supervisory Committee Member iii Abstract Anaerobic digestion (AD) systems offer a sustainable approach for biomass resource recovery and energy production. This study explores the potential of solid digestate-derived biochar as an additive to enhance the stability and performance of AD systems, focusing on the effects of biochar dosage, pyrolysis temperature, and pyrolysis catalyst on methane production. Two series of tests were conducted: Test Series 1 using a mixture of chicken, hog, and dairy manure as the substrate, and Test Series 2 employing synthetic food waste. In Test Series 1, biochemical methane potential (BMP) tests were performed with biochar dosages of 0.1 and 0.6 g/g Total Solids. Results showed a marginal improvement in specific methane yield (SMY) at the lower dosage, but an increase of 9.0-53.5% at the higher dosage across all biochar types compared to the control. Hence, the study found a dosage-dependent relationship. The influence of pyrolysis temperature (400°C vs. 500°C) and catalysts (20 wt% K3PO4 vs. 10 wt% K3PO4/10 wt% clinoptilolite) on SMY exhibited opposite trends, and no direct correlation was found between specific surface area of biochar and SMY. In Test Series 2, BMP assays using synthetic food waste revealed a dosage-dependent relationship with SMY. At a lower biochar dosage (0.1 g/g TS), improvements in methane production were marginal, while a higher dosage (0.6 g/g TS) increased methane content by at least 10%, with yield improvements ranging from 35% to 52%. A three-way ANOVA indicated that biochar dosage significantly influenced methane yield, while pyrolysis temperature and catalyst had no statistically significant impact. Multiple linear regression analysis identified the dosage-adjusted aromaticity index as a significant factor affecting SMY, with a unit increase correlating to a 327 mL/g VS increase in SMY. The findings highlight the potential of solid digestate-derived biochar as a valuable additive in AD systems. Optimizing biochar dosage, feedstock, pyrolysis temperature, and catalyst can significantly enhance methane production and process stability. Tailored biochar properties and production conditions offer promising opportunities for improving the efficiency and sustainability of AD systems. iv Lay Summary Anaerobic digestion (AD) stands as a pivotal technology for managing organic waste streams like food waste and animal manure, transforming them into biogas, a valuable energy source. This study delved into enhancing AD systems by integrating biochar, a carbon-based conductive material. Focusing on biochars derived from the solid digestate of AD, our research examined their addition to systems using a mixture of animal manure and synthetic food waste as substrates. We observed significant improvements in methane production rates, yield, and content upon the introduction of biochar into the AD process. These enhancements stem from biochar's unique properties, including its porous structure and alkaline characteristics, which help neutralize inhibitory substances in the waste materials and promote biochemical stability within the AD system. Our findings underscore the potential of biochar as a beneficial additive for optimizing AD systems, offering promising avenues for sustainable waste management and renewable energy production endeavors. v Preface The study presented in this thesis is an original work completed by the author, Sofia Liliana Lucero Saucedo under the supervision of Dr. Anthony Lau. The author, Sofia Liliana Lucero Saucedo contributed to the literature review, experimental design and execution, data analysis, and thesis writing and review, with suggestions from the supervisor. vi Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface............................................................................................................................................. v Table of Contents ........................................................................................................................... vi List of Tables ............................................................................................................................... viii List of Figures ................................................................................................................................. x List of Abbreviations ..................................................................................................................... xi List of Symbols ............................................................................................................................ xiii Acknowledgments......................................................................................................................... xv Chapter 1: INTRODUCTION......................................................................................................... 1 1.1 Background ........................................................................................................................... 1 1.2 Research objectives ............................................................................................................... 5 1.3 Structure of thesis ................................................................................................................. 6 Chapter 2: LITERATURE REVIEW.............................................................................................. 7 2.1 Anaerobic digestion .............................................................................................................. 7 2.2 Types of anaerobic digestion ................................................................................................ 8 2.2.1 Mesophilic, thermophilic, and psychrophilic digestion ................................................. 9 2.2.2 Wet and dry anaerobic digestion.................................................................................. 10 2.3 Feedstock properties ........................................................................................................... 11 2.4 Operational and environmental conditions that affect anaerobic digestion ........................ 12 2.5 Co-digestion ........................................................................................................................ 15 2.6 The use of additives in anaerobic digestion ........................................................................ 16 2.6.1 Biochar-assisted AD .................................................................................................... 17 2.6.2 Effects of biochar in AD .............................................................................................. 20 2.6.3 Mechanisms of biochar in AD ..................................................................................... 24 2.6.4 Factors impacting biochar for methane production ..................................................... 29 2.7 Methods for conducting Biochemical Methane Potential (BMP) tests .............................. 31 2.7.1 Gas-density method ..................................................................................................... 35 vii Chapter 3: MATERIALS AND METHODS ................................................................................ 36 3.1 Materials ............................................................................................................................. 36 3.2 Methods............................................................................................................................... 37 3.2.1 Biochemical methane potential assays (BMP) ............................................................ 37 3.2.2 Experimental design..................................................................................................... 40 3.2.3 Characterization of materials ....................................................................................... 43 3.2.4 Data and statistical analysis ......................................................................................... 47 Chapter 4: RESULTS AND DISCUSSION ................................................................................. 52 4.1 Characteristics of biochar ................................................................................................... 52 4.1.1 Pore properties ............................................................................................................. 52 4.1.2 Functional groups......................................................................................................... 56 4.2 Properties of substrates and inocula .................................................................................... 60 4.3 BMP test results .................................................................................................................. 61 4.3.1 Test Series 1 ................................................................................................................. 61 4.3.2 Test Series 2 ................................................................................................................. 70 Chapter 5: CONCLUSION ........................................................................................................... 79 5.1 Summary of results ............................................................................................................. 79 5.2 Recommendations for future work ..................................................................................... 80 References ..................................................................................................................................... 82 viii List of Tables Table 2.1: Methane yield potential of different substrates common to anaerobic digesters, adapted from Chala et al., 2018. ................................................................................................... 12 Table 2.2: Main characteristics of anaerobic organisms, modified from (IWA, 2007). ............... 12 Table 2.3: C/N ratio of different substrates employed in AD, adapted from (González et al., 2022; Wang et al., 2012). .............................................................................................................. 15 Table 2.4: Mono- and co-digestion studies of animal manures and food waste. .......................... 16 Table 2.5: Effect of exogenous substances on AD, modified from (Paritosh, et al., 2020). ........ 17 Table 2.6: Comparison of thermochemical conversion methods for biochar production, extracted from (Johnravindar, 2021). ........................................................................................................... 18 Table 2.7: Effects of operating conditions on the properties of biochar during conventional pyrolysis, modified from (Zhu et al., 2023). ................................................................................. 18 Table 2.8: Summary of reports on biochar addition in AD produced from AD substrate or solid digestate as pyrolysis feedstock. ................................................................................................... 22 Table 2.9: Comparison of biogas measurement methods in BMP testing (Amodeo et al., 2020; Angelidaki et al., 2009; Astals et al., 2020; Hafner et al., 2019; Hafner et al., 2020; Holliger et al., 2016; Justesen et al., 2019; Koch et al., 2019; Strömberg et al., 2014). ................................. 33 Table 2.10: Guidelines for Biomethane Potential (BMP) Testing Protocols and Data Analysis (Angelidaki et al., 2009; Astals et al., 2020; Hafner et al., 2020; Holliger et al., 2016; Koch et al., 2019; Strömberg et al., 2014; Valero et al., 2016). ....................................................................... 34 Table 3.1: Model organic fraction of municipal solid waste (M-OFMSM). ................................ 36 Table 3.2: Experimental design of Test Series 1. ......................................................................... 41 Table 3.3: Experimental design of Test Series 2. ......................................................................... 42 Table 4.1: Composition of solid digestate used as feedstock to produce the biochar samples. .... 52 Table 4.2: Characteristics of biochars used in the batch experiments for Test Series 1. .............. 53 Table 4.3: Characteristics of biochars used in the batch experiments for Test Series 2. .............. 55 ix Table 4.4: Summary of elemental analysis and weight ratio of biochar samples used in Test Series 2. ......................................................................................................................................... 56 Table 4.5: Characteristics of inocula and substrates used in the batch experiments. ................... 60 Table 4.6: C/N ratio of the co-digestion substrates used in the batch experiments. ..................... 60 Table 4.7: Biochemical Methane Potential (BMP) results for Test Series 1 ................................ 62 Table 4.8: Simulated parameters from the fit of experimental BMP data to the First-Order Model and the Modified Gompertz Model............................................................................................... 68 Table 4.9: Change in hydrolysis rate and methane production rate vs the control. ...................... 70 Table 4.10: Biochemical Methane Potential (BMP) results for Test Series 2, displaying the biogas yield, specific methane yield (SMY), its deviation from the control, and the relative standard deviation from the control. ............................................................................................. 72 Table 4.11: Tukey's Honest Significant Difference (HSD) test results for BMP treatments vs. control (control-BMP treatment). ................................................................................................. 73 Table 4.12: Tukey's Honest Significant Difference (HSD) test results for BMP treatments vs. wheat straw 10 wt% (T13) and 20 wt% (T14) with a statistical difference at a 95% confidence level (BMP treatment- T13/T14). ................................................................................................. 74 Table 4.13: Three-way ANOVA results for food waste anaerobic digestion SMY with different biochar dosages, catalysts, and temperatures. ............................................................................... 75 Table 4.14: Correlation coefficients between biochar physicochemical properties and anaerobic digestion methane yields for treatments 2-9. ................................................................................ 75 Table 4.15: Correlation coefficients between dosage-adjusted biochar physicochemical properties and anaerobic digestion methane yields for treatments 3-10. ...................................... 76 Table 4. 16: Results of multiple linear regression analysis of biochar properties and specific methane yield. ............................................................................................................................... 78 x List of Figures Figure 2.1: Main steps and pathways of substrate decomposition through anaerobic digestion, extracted from (Sayara and Sánchez, 2019). .................................................................................. 8 Figure 2.2: Characteristics of biochar, extracted from (Zhao et al., 2021). .................................. 25 Figure 3.1: Test Series 1 set-up consisting of the AMPTS II system. .......................................... 38 Figure 3.2: Test Series 2 set-up consisting of an incubator, media bottles and balance. .............. 39 Figure 3.3: Data collection steps of the GD-BMP measurements for each sampling event (Justesen et al., 2019). ................................................................................................................... 40 Figure 4.1: FTIR spectra of biochar samples produced from MACP of solid digestate, or lignin mixed with different catalysts and temperatures with a 30 min microwave residence time. ....... 59 Figure 4.2: Comparison of specific methane yield among thirteen treatment variations using a manure mixture as the substrate. ................................................................................................... 61 Figure 4.3: Comparison of FOS/TOC ratio among thirteen treatment variations using a manure mixture as the substrate. ................................................................................................................ 63 Figure 4.4: Total surface area per volume of digestion vs specific methane yield for treatments 2-9 of Test Series 1........................................................................................................................... 65 Figure 4.5: Comparison of cumulative methane yield at 20°C and 1 atm from BMP assays across 13 treatments. ................................................................................................................................ 66 Figure 4.6: The kinetic constants of the first-order model and the modified Gompertz model, data are means of duplicates. ................................................................................................................ 69 Figure 4.7: Comparison of specific methane yield among thirteen treatment variations using synthetic food waste as the substrate. ........................................................................................... 71 xi List of Abbreviations AC Activated carbon AcoD Anaerobic co-digestion AD Anaerobic digestion ANOVA Analysis of variance BJH Barette-Jovner-Halenda BMP Biochemical methane potential BET Brunauer-Emmet-Teller C Carbon CO2 Carbon dioxide C/N Carbon to nitrogen COD Chemical oxygen demand DIET Direct electron transfer DAD Dry anaerobic digestion FW Food waste FTIR Fourier-transform infrared FAN Free ammonia nitrogen FOS/TAC ratio of volatile fatty acids to total alkalinity or total bicarbonate alkalinity GD-BMP Gas density-based biochemical methane potential GHG Greenhouse gas HSD Honest Significant Difference HRT Hydraulic retention time H Hydrogen H2 Molecular hydrogen H2S Hydrogen sulphide IET Interspecies electron transfer IHT Interspecies hydrogen transfer LOD Limit of detection LCFAs Long-chain fatty acids MIET Mediated interspecies electron transfer MAD Mesophilic anaerobic digestion CH4 Methane M-OFMSW Mixture of organic fraction of municipal solid waste N Nitrogen O Oxygen PAD Psychrophilic anaerobic digestion RSD Relative standard deviation SMY Specific methane yield xii SSA Specific surface area S/I Substrate to inoculum S Sulfur TAD Thermophilic anaerobic digestion TAN total ammonium nitrogen TS Total solids VFAs Volatile fatty acids VM Volatile matter VS Volatile solids WAD Wet anaerobic digestion xiii List of Symbols % Percentage °C Degree Celsius °C/s Degree Celsius per second < Less than > More than C-H Carbon-hydrogen bond C=C Carbon double bonds C=O Carbonyl bond cH2O Concentration of water cm-1 Wavenumber cm3/g Cubic centimetre per gram g Gram g/g TS Gram per gram of total solids g/L Gram per litre g/mL Gram per millilitre g/mol Grams per mol h Hour k Total number of measurement intervals K Kelvin KH Kinetic rate kPa Kilopascal kWh Kilowatt-hour L Litre M Specific cumulative methane produced at time t M0 Potential maximum methane yield m2/g Square metre per gram Mb Molar mass of biogas mg Milligram MH2O Water molar mass mL/gVSsubstrate/days Millilitre per gram of volatile solids per day mL/gVS Millilitre per gram of volatile solids mm Millimetre mmol/g Millimole per gram MW Megawatt nm Nanometre pH2O Water vapor pressure phs Pressure of biogas in the bottle headspace xiv R2 Coefficient of determination Rm Maximum methane production rate swc Standard deviation of the water control t Time in days Ths Temperature in the bottle headspace TWh Terawatt-hour Vb Molar volume of biogas Vf Volume of flushing gas wt% Weight percentage x Mole fraction of methane/carbon dioxide Îmb Mass loss λ Lag phase μm Micrometre Ïb Biogas density Ïf Density of flushing gas xv Acknowledgments I want to express my sincere gratitude to Dr. Anthony Lau for his invaluable guidance and mentorship throughout the preparation of this thesis. I also offer special thanks to Jun Sian Lee from the Biomass and Bioenergy Research Group for assisting whenever needed. I also wish to extend my gratitude to Dr. Baldwin and Dr. Bi for serving as my supervisory committee members and for their insightful comments and guidance. I am grateful to Qing An, Pu Yang, and Mohammad Ghazani from the Pyrolysis group for their dedicated efforts in producing the biochar and conducting the characterization of the samples. Their contributions have been pivotal to the success of this research. I want to thank my boyfriend for his unwavering support and understanding during this academic journey. His presence has been a source of strength and motivation. I am also grateful to my beloved cats for their comforting presence throughout long study sessions. 1 Chapter 1: INTRODUCTION 1.1 Background A paradigm shift toward sustainable waste management practices has emerged amidst the backdrop of escalating global energy demands and the burgeoning challenges of waste management spurred by population growth and industrialization. This shift emphasizes the dual imperatives of waste recycling and renewable energy utilization to alleviate environmental degradation and resource depletion. From 2000 to 2022, we witnessed a staggering increase in the global population from 6.12 billion to 7.94 billion, concomitantly driving up waste generation and energy consumption (World Population Prospect, 2022). Projections indicate a further 20% surge in global energy consumption by 2040, with an estimated total reaching 739,1024 TWh by 2019 (Demichelis et al., 2022). The surge in food production demand forecasts a corresponding rise in livestock manure production, exacerbating the environmental challenges entailed in its management. Properly handling organic waste is paramount in mitigating environmental pollution and resource depletion while harnessing its untapped energy potential. Conventional practices involve using manure as fertilizer in agriculture to enhance soil fertility (Westerman and Bicudo, 2005). However, livestock manure poses risks such as ammonia emissions, methane emissions, and nutrient runoff, thus contributing to greenhouse gas (GHG) emissions and eutrophication. For instance, in 2021 alone, the land application of manure contributed to the release of 4.2 gigatons of CO2 equivalent (CO2e) (FAOSTAT Analytical, 2023), underscoring the urgent need for transformative waste management practices. Food waste (FW) which constitutes a significant fraction of municipal solid waste also presents its own environmental challenges. Improper handling of FW can cause environmental degradation, emission of harmful gases, and the dissemination of disease-causing bacteria. Compounded by legal restrictions, escalating land costs, and the complexities of urban expansion and economic growth, the management and disposal of FW have become increasingly formidable (Vergara and Tchobanoglous, 2012). The United Nations Food and Agriculture 2 Organization estimates that approximately one-third of food produced by businesses is squandered annually, exacting a toll of $750 billion on the global economy (Maranon et al., 2012). Despite the prevalence of landfills as a primary method for treating municipal solid waste, their environmental risks render them unsustainable in the long run. Alternative methods, such as composting, offer partial respite but must be improved by protracted treatment cycles and limited efficiency. (Chakraborty et al., 2017; Zamanzadeh et al., 2017). Different techniques have been employed to manage and reduce the growing amount of biowaste. Incineration and gasification are the two leading waste-to-energy technologies that have been widely used throughout the world, though these technologies have secondary environmental impact (Paranjpe et al., 2023). Conversely, substantial volumes of agricultural and food wastes generated annually present an opportunity for conversion to biogas through anaerobic digestion. As one form of bioenergy, biogas is an established renewable energy technology that has been utilized extensively worldwide. It offers critical environmental benefits, the most significant of which is reduced GHG emissions (Li et al., 2020). Some 132,000 full-scale digesters in various sizes are in use around the world. Furthermore, more recent and relatively well-established technology is available to convert biogas to biomethane (ie., renewable natural gas RNG) for vehicle fuel or injection into regional or national grids (International Energy Agency. IEA, 2021). In Canada, emissions reductions were estimated at 8.2 Mt CO2e in 2020, which can increase to 23.6 Mt CO2e by 2050 with favorable policies. Note that these reductions account for avoided methane emissions and displaced fossil fuel use. In 2021, Canada had nearly 300 biogas and RNG projects already processing two million tonnes of manure, crop residues, and off-farm organics annually and generating over 20 PJ of energy from agricultural AD, industrial AD, municipal and industrial WWTFs, and landfills. Existing biogas projects produce 6 PJ of RNG, 130 Mm3 of biogas for heat and direct use, and 198 MW of additional clean electricity capacity. Canada is currently tapping only about 14 percent of its readily available biogas energy potential. 3 This means, the sector can bring additional biogas energy on line by more than 8 times (Canadian Biogas Association, 2023). In British Columbia alone, 1.3 million ODT crop residues, 0.45 million ODT manure, 0.2 million ODT food waste, and 0.2 million ODT municipal wood waste are available annually that can be biologically converted to biogas via AD (Wang et al., 2020). Anaerobic digestion is a microbial process that converts organic waste into biogas, primarily methane (CH4) and carbon dioxide (CO2), in an oxygen-free environment (Wi et al., 2023). Methane from AD can be utilized as an alternative to fossil fuels; the energy capacity can vary, depending on the type of waste, from 20 to 300 kWh of net energy per ton (Hadin and Eriksson, 2016; Rabii et al., 2019). One of the main by-products of the AD process is the digestate (residual), which consists of partially degraded organic matter, inorganic compounds, and microbial biomass (Breunig et al., 2019; Chen et al., 2021). While AD offers a sustainable solution for organic waste management and renewable energy production, several challenges exist in maximizing its efficiency and environmental benefits. Operational instability, low carbon conversion efficiency, and management of digestate streams are among the key challenges facing AD systems. Operational instability in AD systems can arise from factors such as high concentrations of primary and secondary inhibitors and imbalances in the microbial community. The accumulation of volatile fatty acids (VFAs), ammonia nitrogen (NH3-N), and long-chain fatty acids (LCFAs) can inhibit methanogenesis and disrupt the balance of the microbial community, leading to process failure (Chen et al., 2014; Liang et al., 2022). With low carbon conversion efficiency, a large amount of digestate is generated from the AD reactor, which is often spread as organic fertilizer after composting. However, leaching of nutrients into the groundwater can occur because of the low nutrient retention capacity of digestate, and gas pollutants such as CO2 and nitrogen compounds (NH3, NOx, N2O) can be released into the atmosphere (Feng and Lin, 2017). In large-scale AD systems, significant quantities of digestate slurry are generated, which contains abundant nutrients and carbon. Instead of land-spreading, a practice under increasingly stringent regulations, liquid digestate can be upgraded to liquid fertilizer or recycled to the AD reactor if proper detoxification can be controlled in the reactor. Solid digestate that contains a considerable 4 amount of energy can be converted to fuels or heat by combustion, pyrolysis (Troy et al., 2013), or gasification (Yu et al., 2015). Various strategies have been proposed to address these challenges, including co-digestion of different feedstocks to optimize nutrient balance and increase methane yield, pre-treatment methods to enhance biodegradability, and the introduction of additives to stimulate microbial activity and reduce inhibitor concentrations (Pecchi and Naratieri, 2019; Zheng et al., 2014). Among the additives that have been studied is biochar, which is a stable and solid carbon-rich material obtained from the thermochemical conversion of biomass in an oxygen-limited environment (IBI 2012). In recent years, there has been much discussion about their applications related to soil amendment, waste management, and energy production (Lehmann and Joseph 2015). According to the studies of biochar as additive material in AD (for instance, Cai et al. 2016, Luo et al. 2015, Mumme et al. 2014), the possible effects of biochar on the AD process in terms of process stability, higher biogas yields, and mitigating inhibition problems are worth studying. The operational conditions for biochar production (feedstock selection, pyrolysis temperature and residence time, activation method, particle size, and quality control measures) profoundly influence its properties and efficacy when added to AD systems. Biochar produced at higher temperatures typically exhibits greater stability, surface area, and nutrient retention capabilities, potentially enhancing AD performance by promoting microbial activity and biogas production. Activation processes further enhance biochar's adsorption capacity, while particle size and surface area influence its interactions with microbial communities and substrates in AD systems. In view of the significant potential of anaerobic digestion in sustainable waste management and renewable energy production, there arises a need to explore innovative approaches to enhance AD system's conversion efficiency and environmental benefits. One such approach adopts the circular economy principles that emphasize the reuse and recycling of resources, and it involves the integration of a thermochemical process (such as pyrolysis) with a biological process (such as anaerobic digestion). The digestate from AD process could be a suitable feedstock for pyrolysis to make biochar (Mumme et al. 2014). Adding the solid digestate-derived biochar to the 5 substrate in the anaerobic digester is a promising strategy that holds the potential to improve AD process stability, increase its carbon conversion efficiency, and enhance the valorization of digestate (Monlau et al., 2015). While the potential benefits of incorporating solid digestate-derived biochar into anaerobic digestion (AD) systems are promising, several knowledge gaps need to be addressed. To begin with, a number of studies have used biochar derived from the solid digestate of food waste and manure AD for the treatment of contaminated water, but very few studies have utilized the biochar in the anaerobic digestion of food waste and manure. In this regard, no previous studies have been conducted using biochar made by microwave-assisted catalytic pyrolysis (MACP) in AD of food waste and manure. Furthermore, although the correlation between methane yield and the physicochemical properties of biochar such as specific surface area and nutrient retention capability has been rather extensively studied, the results have been inconsistent, necessitating further research to establish clearer patterns. Understanding how these digestate-derived biochars influence methane production, process stability, and the mitigation of inhibitory compounds remains a crucial area of research. By focusing on these aspects, this thesis aims to assess the feasibility and impacts of incorporating solid digestate-derived biochar into AD systems. Specifically, the study will investigate how different operational conditions of the biochar influence the methane yield and methane content within the AD process. By systematically evaluating these parameters, the research seeks to provide insights into the application of biochar for enhanced biogas production and overall AD system performance. 1.2 Research objectives The goal of this study is to explore the potential of solid digestate-derived biochar as an additive for enhancing the stability and performance of AD systems. The specific objectives are as follows: â¢ Evaluate the efficacy of solid digestate-derived biochar in enhancing methane production and stability in AD systems through performing biochemical methane potential (BMP) tests. 6 â¢ Examine the effects of biochar dosage, pyrolysis temperature, and pyrolysis catalyst on the performance and stability of AD systems. â¢ Investigate the correlation between the physicochemical properties of biochar and specific methane yield. Two major series of tests were conducted to achieve these research objectives. Test Series 1 involved the evaluation of two subsets of tests, with one set using a substrate that comprised of a mixture of animal manures. A total of 32 BMP (biomethane potential) assays were conducted. The assays utilized five types of powdered, solid digestate-derived biochar, mixture of manures as the substrate, and control treatment. The other subset of tests is for the co-digestion of mixture of manures with wheat straw. The assessment included measuring specific cumulative methane yield, and modeling the lag phase, methane generation rate, and hydrolysis rate using the Modified Gompertz and first-order models. Furthermore, the correlation of methane yield with biochar specific surface area was examined. Test Series 2 involved 54 BMP assays using synthetic food waste as the substrate. Like Test Series 1, the assays utilized five types of powdered, solid digestate-derived biochar, blanks, positive controls (microcrystalline cellulose), and co-digestion of food waste with wheat straw. The evaluation included measuring specific cumulative methane yield, biogas yield and its methane content for each BMP assay, along with assessing the correlation of methane yield with biochar production operational conditions and physicochemical properties. 1.3 Structure of thesis This thesis comprises five chapters. Chapter 1 introduces the background and motivation for conducting the thesis research, an outline of the main research topics, the goal and specific research objectives. Chapter 2 is for literature review of the basic principles and applications of anaerobic digestion (AD), the use of biochar as additives in AD, and methods for running the biochemical methane potential (BMP) test. In Chapter 3, the methodology, analysis procedures and overall research design are identified. Results of the experiments are presented and discussed in Chapter 4. Finally, Chapter 5 presents the conclusion and makes recommendations for improvements to be done in the future. 7 Chapter 2: LITERATURE REVIEW This section is dedicated to investigating the impact and mechanisms of integrating biochar into anaerobic digestion (AD) systems that process food waste and animal manure. Initially, it provides an overview of the AD process, its various types, and the key parameters influencing system performance, laying the foundation for understanding how biochar can enhance these processes. Within this context, the review thoroughly examines the potential of biochar as a carbon-based additive in AD reactors, exploring its properties and diverse applications. By shedding light on the role of biochar in AD, this review seeks to advance the understanding of how biochar integration can help optimize AD performance and promote sustainable waste management practices. 2.1 Anaerobic digestion AD is a widely employed technology for treating organic waste streams, with its implementation steadily increasing in recent years. The design, operation, and performance of the digesters, as well as process drawbacks, are closely linked to the nature and origin of the organic residue, which can be categorized into sewage sludge, animal manures, food processing wastes, crop harvesting residues, the organic fraction of municipal solid waste, and energy crops. While there is diversity in organic waste, most AD plants utilize continuous stirred tank reactor or upflow anaerobic sludge blanket reactor configurations for particulate and soluble organic streams, respectively (Swiechowski et al., 2022). During AD, organic matter undergoes fermentation degradation in the absence of oxygen following four sequential stages: hydrolysis, acidogenesis, acetogenesis, and methanogenesis (Figure 2.1). First, hydrolytic bacteria convert complex organic compounds into simpler compounds, like monomers (Johnravindar et al., 2021). These products are then utilized by acidogenic bacteria and produce volatile fatty acids (VFAs) along with ammonia, carbon dioxide, hydrogen sulfide, and other byproducts. In the third stage, acetogenic bacteria convert the VFAs into methanogenic substrates that include acetate, CO2, and H2. Finally, two main groups of methanogenic archaea (acetate-utilizing acetoclastic methanogens and H2 & CO2- 8 utilizing hydrogenotrophic methanogens) convert these substrates into methane (Bong et al., 2017; Rabii et al., 2019). Figure 2.1: Main steps and pathways of substrate decomposition through anaerobic digestion, extracted from (Sayara and Sánchez, 2019). The biogas produced from AD technology is composed of around 50-75% methane, 25-50% carbon dioxide, 2-8% N2 and small amounts of H2S, H2 and other gases (Li et al., 2019). This process relies on syntrophic interactions between microbial communities, which involve interspecies electron transfer (IET) mediated by hydrogen (H2), formate, or direct electron transfer (DIET). Efficient IET is crucial for maintaining AD stability and promoting methane production (Goswami et al., 2022). 2.2 Types of anaerobic digestion AD encompasses wet and dry processes, depending on the moisture content of the feedstock. Wet AD is commonly used for processing liquid organic wastes like sewage sludge, while dry AD processes can utilize solid substrates such as agricultural residues. AD can also be categorized by temperature requirements: thermophilic digestion operates at higher temperatures for faster biodegradation, mesophilic digestion functions at moderate temperatures with lower energy needs, and psychrophilic digestion occurs at lower temperatures. Understanding these variations is vital for optimizing biogas production and sustainable waste management (Indren et al., 2020). 9 2.2.1 Mesophilic, thermophilic, and psychrophilic digestion Anaerobic digestion processes can operate under various temperature regimes, including âmesophilic ADâ (MAD), âthermophilic ADâ (TAD), and âpsychrophilic ADâ (PAD) ranges, each influencing the efficiency and characteristics of the digestion process differently. Typically, MAD operates at temperature ranging from 30 to 40°C, and it is favored in full-scale plants due to their stable biogas yields and lower energy requirements compared to thermophilic conditions. Mesophilic bacteria are more robust and can tolerate greater changes in environmental parameters, including temperature. Thus, a continuous and stable production of methane can be attained at these conditions with a richer and more even microbial species. (Siddique et al., 2018; Song et al., 2021; Swiechowski et al., 2022; Wang et al., 2018). TAD operates at temperatures around 50â60°C. It has gained attention as it promotes faster biodegradation and enhanced biogas production compared to MAD. Also, TAD can reduce the pathogen load and odor emission from the process, making the application of the digestate as fertilizers more favorable than MAD (De La Rubia et al., 2013). However, challenges such as the availability of seed sludge and unstable start-up phases hinder the full-scale commercialization of TAD. Irreversible accumulation of VFAs, pH drop, and even system failure are often reported during the start-up of TAD. Irreversible accumulation of VFAs, pH drop, and even system failure are often reported during the start-up of TAD. To address these challenges, the start-up of TAD is often carried out with mesophilic inoculum. Besides, the increase in temperature impacts the microbial community and causes the digestion pathway to shift from acetoclastic to the hydrogenotrophic pathway, needing higher micro-nutrient requirements compared to MAD (Romero-Güiza et al., 2016; Wang et al., 2018; Xing et al., 2020). PAD, operates at temperatures below 15-20°C, offering energy-efficient and cost-effective solutions due to reduced external energy input requirements. Temperature fluctuations can significantly impact PAD performance, with slow temperature rises posing risks to stability. Despite these challenges, PAD shows promise for small-scale AD in cold regions (Song et al., 2021). 10 2.2.2 Wet and dry anaerobic digestion Different processes are employed based on the moisture content of the feedstock, leading to variations such as wet and dry anaerobic digestion. WAD operates with a total solids (TS) content below 15%. It is favored for high-moisture substrates like manure, food waste, and sewage sludge. The feedstock is slurried with the addition of water to the mixture to reach the desired level, either by adding water, or by recirculation of the liquid effluent fraction, or by co-digestion with wetter wastes. WAD benefits from simpler mechanical equipment and dilution of inhibitory substances, however it also incurs greater water and energy consumption and may face challenges like complicated pre-treatment and reduced working volume due to sedimentation. (Lin et al., 2015; Hadin and Eriksson, 2016; Sayara and Sánchez, 2019; Silva-González et al., 2020). Dry anaerobic digestion (DAD) is for substrates with higher TS contents, typically ranging from 15% to 50%. This process is suitable for substrates like lignocellulosic biomass. DAD offers advantages such as lower water and energy requirements for heating and mixing, higher volumetric methane productivity, limited wastewater generation, and easy-to-handle digestate due to its lower water content (Arelli et al. 2018). However, DAD faces challenges like mass transfer limitations, longer retention times (up to three times compared to WAD), and potential microbial inhibition due to the lack of water in the system. DAD is more difficult to balance physically, chemically, and biologically compared to WAD, as the lack of water in the system can lead to accumulation of intermediary phases that induce microbial inhibition (Dixon, 2018). Balanced DAD systems could potentially have a higher volumetric CH4 production and yield compared to WAD systems due to the higher concentration of substrate to be digested. (Hadin and Eriksson, 2016; Xu et al., 2015) As illustrated in the literature, substrate degradation and consequently biogas production are reduced with an increase in the substrateâs TS content. Fernandez et al., demonstrated that an increase in TS content from 20% to 30% was associated with a decrease in organic waste degradation by 17%. Another study reported reduced methane yields and cellulose hydrolysis rates of lignocellulosic biomass in DAD as compared to WAD (Anukam et al., 2019; Sayara and 11 Sánchez, 2019). The limitation of DAD might be explained by the slow diffusion of dissolved inhibitory products inside the organic matrix causing local accumulation at the microbial scale. 2.3 Feedstock properties Methane yield in AD is a key performance indicator influenced by substrate composition, substrate-to-inoculum ratio, and inoculum source. Various substrates exhibit diverse methane yield potentials, depending on their chemical composition. Moreover, different sources could affect substrate biodegradability results because of the different levels of microbial population. The source of microbial inoculum significantly influences AD performance. Inoculants derived from anaerobic activated sludge outperform aerobic counterparts, enhancing hydrolysis and promoting stable AD conditions. For a defined inoculum, the methane yield of an organic substrate is directly related to the extent of solubilization, while the degradation rate will depend on the slowest of the three steps of the anaerobic digestion process, namely hydrolysis (solubilization), acidogenesis and methanogenesis (Xu et al., 2022). The digestibility of AD feedstock depends on the content of the readily biodegradable compounds like sugars (420 mL/g), moderately biodegradable ones such as hemicelluloses and cellulose, and undigested compounds like lignin. Lignocellulosic biomass such as agricultural residues contain cellulose, hemicellulose, and lignin; the latter can significantly inhibit biogas production. Protein-rich substrates yield substantial methane during AD (630 mL/g), yet their degradation releases ammonium ions that inhibit methanogenic microbes, necessitating careful co-substrate selection and to mitigate inhibition and attain optimal C/N ratio.. Conversely, high-fat content organic waste facilitate enhanced biogas production due to their high biodegradability (990 mL /g) (Siddique et al., 2018; Silva-González et al., 2020). However, excessive triglycerides concentration can lead to issues such as blockages and microbial inhibition in anaerobic digesters, with long-chain fatty acids (LCFAs) hindering microbial activity and causing process instability, particularly at elevated temperatures. Balancing nutrients and enriching microorganisms involve mixing carbohydrate-rich materials with fat-rich materials to accelerate biogas generation (Chan et al., 2018; Hadin and Eriksson, 2016). 12 Table 2.1: Methane yield potential of different substrates common to anaerobic digesters, adapted from Chala et al., 2018. Substrate Methane yield potential (mL/g- substrate VS) Animal manures Cow 130-300 Hog 210-320 Poultry 250-400 Agricultural products Straw 71-240 Maize silage 320-400 Grass 286-324 Municipal wastes Bio-wastes 200-600 Food waste 350-600 Sewage sludge 250-350 2.4 Operational and environmental conditions that affect anaerobic digestion Operational parameters affect methane generation and overall system performance. Factors such as the substrate-to-inoculum (S/I) ratio, pH, particle sizes, hydraulic retention time (HRT), and nutrient composition significantly impact microbial activity and substrate degradation. The stability and efficiency of AD systems hinge upon maintaining a balance between organic substrate degradation, intermediate metabolite conversion, and methane assimilation. In AD, acid-forming and methane-forming microorganisms have distinct physiologies and sensitivities (Table 2.2). Failure to maintain this balance can lead to reactor instability and reduced methane yields, often due to factors like pH fluctuations, ammonia stress, and hindrance of methanogenic activity. Other major reasons for low conversion rates are acidogenic product inhibition and limitation of mass transfer (Chen et al., 2008; Johnravindar et al., 2021; Li et al., 2019). Table 2.2: Main characteristics of anaerobic organisms, modified from (IWA, 2007). Parameters Acidogenic and acetogenic organisms Methanogenic organisms Growth rate High Slow pH Low sensitivity High sensitivity Temperature Moderate sensitivity High sensitivity Toxic agents Moderate sensitivity High sensitivity Volatile acids Low sensitivity High sensitivity pH level affects organic matter solubilization and microbial enzymatic reactions in AD process. Maintaining an optimal pH, typically between 6.8 and 7.2, is crucial for maximizing methane yield. Acidogenic microorganisms prefer pH of 5.5â6.5, while methanogenic microorganisms 13 thrive near a pH of 7.0. Supplementing alkali chemicals, such as CaCO3, has been recommended to overcome pH-related inhibition of methanogenic activity (Feng et al., 2015; Siddique et al., 2015; Wang et al., 2021). The carbon-to-nitrogen (C/N) ratio of the substrate impacts system stability. Optimal C/N ratio tht promotes CH4 production typically lies between 20:1 and 30:1. If the threshold of this ratio is exceeded, nitrogen will be used by organisms participating in digestion, causing reduced methane production efficiency. A deficiency of nitrogen causes insufficient utilization of carbon sources. Alternatively, lower C/N ratios lead to an accumulation of ammonia-N in the reactor, which may consequently inhibit microbial consortia (Rajagopal et al., 2013; Resch et al., 2011; Wang et al., 2022). Particle size affects biogas production, with smaller particles enhancing hydrolysis efficiency. Agyeman and Tao (2014) reported an increase in the methane production rate of food waste by 10-29% when food waste particle size decreased from 8 to 2.5 mm. HRT is the time required by any microorganism to consume and synthesize the substrate; it affects microbial activity, with uncontrolled HRT inhibiting the microbial metabolism and long HRTs risking microbial death. Short HRTs are recommended for industrial-scale applications, though excessively short HRTs can lead to microbial washout and system failure (Siddique et al., 2018). The S/I ratio can profoundly affect methane production. Low S/I ratios result in slow reaction rates and high ratios cause excessive accumulation of VFAs. VFAs are crucial intermediates during AD of organic waste, and their composition determines methanogenic efficiency. However, VFAs can have a stimulating or inhibiting effect on the growth of functional microbes; with undissociated VFAs being more toxic due to their potential to permeate cell membranes. This effect is generally counteracted by syntrophic acetogens and methanogens, which convert VFAs to methane and carbon dioxide. However, in the case of high organic loading of easily biodegradable wastes, VFAs can accumulate, causing pH drop and even AD failure (Chiappero et al., 2020). 14 Studies on spent coffee grounds and cow manure co-digestion observed gradual methane yield increases with rising S/I ratios, peaking at 1:3 before declining. For instance, methane yields reached 225 mL/gVS at an S/I ratio of 1:3, while lower yields were obtained at ratios of 1:3.5 and 1:4. Excessively low ratios, such as 1:0.5, can lead to VFA accumulation, disrupting AD stability (Akyol, 2020). Different S/I ratios can affect the key microorganisms and intermediate metabolites in AD, resulting in variation in the functional metabolic pathways. However, the mechanism of microbial interactions and functional metabolism under the optimal S/I ratio during the process of AD of food waste remain unclear (Li et al., 2020). Inhibition is indicated by decreased methane production rates and organic acid accumulation. Variation in inhibition levels among substances is due to the complexity of AD processes, involving mechanisms such as antagonism and synergism. Aside from VFAs, a wide variety of substances have been reported to be inhibitory, which include ammonia-N, sulfide, excessive amounts of light metal ions, heavy metals, and a wide range of organic compounds (e.g., alkyl benzenes, halogenated benzenes, phenols, alkyl phenols, some LCFA, surfactants, and detergents). Ammonia, a byproduct of nitrogenous matter degradation, can inhibit AD due to its effects on intracellular pH and enzyme activity (Calli et al., 2005; Sung and Liu, 2003). Ammonium ion and free ammonia (FA) are primary forms of inorganic ammonia nitrogen, with FA being more inhibitory due to its membrane permeability. Maintaining ammonia concentrations below 200 mg/L is beneficial, as higher levels can halt microbial growth (Chen et al., 2008; González-Fernández and García-Encina, 2009; Rajagopal et al., 2013). Solutions to alleviate the challenges of AD process instability include optimizing the operational parameters, implementing advanced technologies, and adopting management strategies. Advanced technologies such as two-stage digestion and co-digestion with complementary substrates can mitigate inhibition effects and improve nutrients balance. Employing efficient mixing and heating systems can enhance mass transfer rates and microbial activity. Proper waste pretreatment techniques, such as thermal, chemical, or enzymatic hydrolysis, can increase substrate accessibility and reduce inhibition by complex compounds (Romero-GuÌiza et al., 2016). 15 2.5 Co-digestion Co-digestion within anaerobic digestion systems (AcoD) presents a multifaceted approach to address the challenges encountered in single-substrate digestion. By mixing diverse organic wastes as the substrates, AcoD can enhance system performance, stability, and overall conversion efficiency. Numerous studies have investigated the benefits of AcoD across various waste streams, shedding light on its high potential to achieve sustainable waste management practices. One major advantage of AcoD is the introduction of a wider variety of microorganisms into the digestion system. Studies have shown that co-digestion with different substrates can enrich microbial communities, leading to enhanced degradation of complex organic compounds and improved biogas production (Liu et al., 2023). The increased microbial diversity in AcoD systems allows for more efficient breakdown of organic matter, thereby reducing the accumulation of inhibitory compounds such as VFAs and ammonia nitrogen (Kébé et al., 2021). Moreover, AcoD may enhance the degradation of cellulose and hemicelluloses, leading to increased biogas production potential (Zhou et al., 2014). Table 2.3: Typical C/N ratio of different substrates employed in AD, adapted from (González et al., 2022; Wang et al., 2012) Substrate C/N Ratio Dairy manure 16-22 Hog manure 9-19 Chicken manure 8-10 Wheat straw 66-81 Food waste 10-55 Recommended 20-30 Optimizing the C/N ratio is critical in AcoD to avoid inhibitory effects, particularly from ammonia. Table 2.3 present the C/N ratios of common single AD substrates, as compared to the recommended C/N ratio of 20:1 to 30:1. Studies relevant to co-digestion of food waste, animal manure and agricultural waste are summarized in Table 2.4. Wu et al. (2010) reported that co-digestion of swine manure with corn stalks at C/N ratio of 20 resulted in substantial increases in biogas production compared to single-substrate digestion. Cumulative biogas production increased by up to 11-fold and cumulative net methane volume increased by up to 16-fold. Wang et al. (2012) also suggested that co-digestion of dairy manure, chicken manure and wheat straw, 16 had better digestion performance with stable pH and low concentrations of total ammonia nitrogen (TAN) and free ammonia (FA) at adjusted C/ N ratios of 25 (Demichelis et al., 2022). If organic wastes used for biogas production contain resistant lignocellulosic materials, special pretreatments are needed for their efficient use in AD. These materials contain non-readily biodegradable carbon, thus affecting the estimation of C/N ratios. Therefore, the calculated C/N ratios represent only the general characteristics of the waste, and not necessarily the substances actually utilized by anaerobic microbes (Siddique et al., 2018). Additionally, AcoD offers opportunities to utilize regionally available co-feedstocks, such as fish biomass, agro-industrial residues, and agricultural waste, to enhance methane production and energy output (Bong et al., 2018; Yong et al., 2015). By incorporating these diverse substrates, AcoD can achieve higher organic loading rates without the need for additional treatments. Again, this would have economic advantages and promote sustainable waste management practices (Mata-Alvarez et al., 2010). Table 2.4: Mono- and co-digestion studies of animal manures and food waste. Substrate(s) Mixing ratio (VS based) C/N Methane yield (mL/gVS) Methane content (%) VS removal (%) References Food Waste (FW) - - 353 54 82 Wang et al., 2012 Cow Manure - 6.3 241 66 45 Food Waste: Cow Manure 48:52 12.3 311 62 68 Dairy Manure (DM) - 22.1 177.4 46.0 47.4 El-Mashad et al., 2010 Chicken Manure (CM) - 8.84 126.9 43.7 55.2 Wheat straw (WS) - 81.1 121.2 41.7 48.5 DM:WS 97.3:2.7 25 211.0 42.4 63.8 CM:WS 74.8:25.2 25 156.2 48.8 59.1 DM:CM:WS 42.3: 42.3: 15.4 25 234.7 50.5 62.3 FW - 28.4 281 57.35 - Yong et al., 2015 WS - 43.4 157 52.43 - FW:WS 5:1 30.9 392 67.62 - 2.6 The use of additives in anaerobic digestion Adding exogenous substances to AD systems can significantly impact their performance and stability. These substances (additives) include trace elements, zeolite, conductive materials, lime mud, and surfactants (Paritosh, et al., 2020) and offer different biochemical effects (Table 2.5). 17 Among these additives, carbon-based conductive materials such as activated carbon (AC) and biochar are particularly noteworthy for their ability to mitigate the detrimental effects of temperature variations, pH fluctuations, and increased hydrogen partial pressure (Wu et al., 2020; Yan et al., 2020). While AC boasts a larger specific surface area than biochar, the latter offers advantages in terms of cost-effectiveness and potential reusability from the digestate in AD processes (Luo et al., 2015). Moreover, biochar's higher electron exchange capacity and its ability to accelerate reaction rates make it a promising candidate for improving AD performance (Li et al., 2021; Zhao et al., 2021). Table 2.5: Effect of exogenous substances on AD, modified from (Paritosh, et al., 2020). Additive Effect Carbon-based conductive materials (such as biochar) Synthrophic â¢ Improved DIET â¢ Improved VFA consumption â¢ Cell mobilization and cell population Metals Metabolic â¢ Increased cofactors and coenzymes â¢ Enzyme activation Nanoparticles Catalytic â¢ Improved surface area for methanogenic activity Biologicals Enzymatic â¢ Enhance hydrolisis â¢ Better enzymatic environment â¢ Improved metabolism Zeolite Cation exchange â¢ Reduced ammonia stress â¢ Reduced proton imbalance 2.6.1 Biochar-assisted AD Biochar, a carbon-rich material derived from the thermochemical decomposition of organic materials (including solid digestate) in an oxygen-constrained environment has garnered attention for its potential applications in AD processes (Goswami et al., 2021; Qi et al., 2021). The biochar production methods are shown in Table 2.6. The integration of AD with pyrolysis offers opportunities for converting dewatered digestate into biochar, which can be used to improve biogas and digestate quality or mitigate instabilities and inhibition in the digestors. 18 Table 2.6: Comparison of thermochemical conversion methods for biochar production, extracted from (Johnravindar, 2021). Method Temperature (°C) Residence time Heating rate (°C/s) Biochar yield (%) Slow pyrolysis 300-700 >30 min 0.01-2 25-35 Fast pyrolysis 500-1000 10- 20 s >2 10-15 Hydrothermal carbonization 180-250 1-20 h - 50-80 Torrefaction 200-300 10-60 min - 80 Gasification 750-900 10-20 s - 10 Pyrolysis, the most often used method for biochar production, involves the thermal decomposition of organic matter at temperatures ranging from 300°C to 1000°C in an oxygen-free environment and is typically categorized as slow, fast, or flash pyrolysis. The process parameters of pyrolysis, including temperature, heating rate, and residence time, play a significant role in determining biochar properties and yields. Higher pyrolysis temperatures and faster heating rates tend to produce biochar with greater surface area and smaller particle size, while longer residence times favor higher biochar yields (Chiappero et al., 2020; Zhao et al., 2021). Table 2.7 summarizes the impact of the pyrolysis parameters on the biocharâs properties. In contrast to conventional pyrolysis methods, microwave-assisted pyrolysis introduces a novel approach characterized by enhanced efficiency and several advantages. Firstly, it enables rapid and efficient energy heating, enhancing product yield while requiring no heat convection through fluids. Besides, it facilitates precise heating control and allows for the recovery of waste material energy and chemical value. Microwave pyrolysis also results in better biochar production quality, characterized by superior porosity and surface area, and higher fixed carbon content (Selvam and Paramasivan, 2022). Furthermore, moisture content in feedstock plays a crucial role, with microwave pyrolysis benefiting from the presence of moisture to enhance heating rates, contrary to conventional pyrolysis methods. The ability of microwaves to vaporize moisture content before volatilizing organic content contributes to increased biochar porosity. The greater surface area of biochar produced through microwave pyrolysis enables its effective utilization for adsorption of contaminants (Li et al., 2016). 19 Table 2.7: Effects of operating conditions on the properties of biochar during conventional pyrolysis, modified from (Zhu et al., 2023). Category Factor Key findings Biomass Type â¢ For a higher content of cellulose, hemicellulose, and lignin, biochar has higher specific surface area and lower ash content. â¢ Higher lignin content led to higher specific surface area and total pore volume. â¢ The higher the lignin content, the greater the surface area and porosity of biochar. â¢ Biochar derived from animal manures exhibited a much higher ash content and pH value, whereas lower carbon content compared to plant-based feedstocks. Particle size â¢ Finer biomass particles were prone to volatiles release, higher surface area and micropore volume. Pyrolysis conditions Temperature â¢ High temperature produces biochar with a high specific surface area and porosity. â¢ High temperature reduced aliphatic and oxygen-containing functional groups and induce aromatic active sites. Heating rate â¢ Higher heating rate caused higher surface area; lower heating rate let to low micropore volume and total pore volume. â¢ Increasing heating rate improves the specific surface area of biochar. Expanding upon microwave-assisted pyrolysis, microwave-assisted catalytic pyrolysis (MACP) represents a promising advancement, optimizing product yield and selectivity. The need for using catalysts as microwave absorbers is due to poor absorption of microwave by dry biomass, thus affecting both the heating behavior and the properties of biochar. Various types of catalysts have been studied. The addition of 30 wt% of clinoptilolite into biomass has shorten the heating time by about 7 min to reach 400 ºC compared to 10 wt% of clinoptilolite which required approximately 20 min (Mohamed et al., 2016b). In comparison to the study conducted by Mohamed et al. (2016a), K3PO4 was determined to be a better catalyst for the formation of biochar compared to clinoptilolite due to the ability of the K3PO4 to promote endothermic reaction, thus lowering the heating rate of the sample (Wahi et al., 2017). The addition of catalyst has also afforded different elemental composition of biochar. As the loading percentage of the K3PO4 increases, the percentage of potassium and phosphorus present in the biochar also increases. Mohamed et al. (2016a) also showed an increase in surface area and pore volume as the percentage of clinoptilolite increased in biochar derived from switchgrass. The surface area and pore volume increased from 33.11 m2/g and 0.0091 cm3/g to 46.93 m2/g and 0.0141 cm3/g, respectively. 20 2.6.2 Effects of biochar in AD The impact of biochar on AD processes is a topic of growing interest due to its potential to enhance system performance and sustainability. Biochar, derived from various feedstocks through pyrolysis, offers unique properties that influence microbial activity and dynamics, nutrient availability, and gas production in AD systems. Understanding these effects is crucial for optimizing biochar application in AD and harnessing its benefits for organic waste treatment and renewable energy production. This section covers the current understanding of how biochar influences AD processes, highlighting key findings and implications for system performance. Enhancing methane yield The effects of biochar in AD have been widely studied, assessing a variety of parameters such as pyrolysis feedstock, particle size, pyrolysis temperature and biochar dosage. Wang et al. (2021) conducted experiments in which biochar was added at varying concentrations (3%, 5%, and 7%) to AD systems treating pig manure that wass contaminated with heavy metals. The results showed significant increases in methane yield ranging from 23.0% to 26.7%. Optimal biochar dosage is needed for maximizing methane production while mitigating potential adverse effects. Excessive biochar addition may lead to a decline in methane production due to non-selective adsorption of nutrients and metabolites (Qi et al., 2021; Chiappero 2020). Researchers have obtained inconclusive results regarding pyrolysis temperature and specific surface area (SSA) of biochar. For instance, Pan et al. (2022) evaluated the impact of biochar derived from cow manure and mushroom bran pyrolyzed at 400-600°C and found that the cow manure biochar (400°C) and mushroom bran biochar (550°C) had the most increases in methane production by 81.30% and 77.6%, respectively, relative to the control. The SSA and porosity of the biochars increased with increase in pyrolysis temperature but were not positively correlated with biogas production. Addition of biochar gave rise to the enrichment of specific microbial communities, notably Geobacter and Methanosaeta that play pivotal roles in enhancing fatty acid metabolism and methane production within AD systems (Jiang et al., 2020). The microbial enrichment facilitates DIET, thereby expediting the syntrophic oxidation of fatty acids and ultimately promoting 21 methane production. Fewer studies have evaluated the integration of AD with pyrolysis that utilized the digestate or the AD substrate as the feedstock. Table 2.8 lists the studies that are pertinent to biochar addition in AD, whereby the biochar is produced from the AD substrate or solid digestate as pyrolysis feedstock. Improving system stability Improving the AD process stability is essential for enhancing the conversion efficiency and reliability, given the susceptibility of these systems to various factors such as pH fluctuations, inhibitory substances, and organic load variations. Biochar has emerged as a promising solution to stabilize AD process by modulating these factors (Chen et al., 2022; Goswami et al., 2022). One key aspect of biochar's influence on process stability is its ability to regulate pH level within the AD environment. Due to its alkaline nature which is attributed to ash content and acidic functional groups volatilization, biochar can increase AD alkalinity, thereby enhancing microbial activity and facilitating fast methane production. Moreover, biochar's redox ability and the presence of aromatics and phenolic groups can aid in electron transfer processes, further bolstering methanogenic activity and system stability. Studies have shown that biochar supplementation can significantly increase AD alkalinity, particularly under acidic conditions, leading to improved operating conditions and enhanced microbial adaptability (Ramanayaka et al., 2020; Yin et al., 2019). Biochar helps mitigate inhibitory factors such as ammonia and VFAs by reducing major intermediates like fatty acids (FA) that are formed during AD and encouraging methanogenesis under acidic environments, but again it is important to optimize biochar dosage to prevent adverse effects on microbial metabolism. Besides, biochar can immobilize microbial biomass on its surface and complex pore structure, thereby enriching the hydrolysis microbes and accelerating the decomposition of organic compounds at the hydrolysis stage of AD (Zhao et al., 2021). 22 Table 2.8: Summary of reports on biochar addition in AD produced from AD substrate or solid digestate as pyrolysis feedstock. Pyrolysis feedstock Pyrolysis temperature (oC) Biochar specific surface area (m2/g) Cumulative CH4 yield (mL/g VS added) Remarks Reference Dry dairy manure 350 6.3 466.5a 374.7 (control) Solid state anaerobic digestion of dry manure Temperature: 35 oC Biochar dosage: 10 g/L Jang et al., 2018 Dried chicken manure 350 - 450 4 - 7 224 - 243 174 (control) Anaerobic digestion of chicken manure Temperature: 35 oC Biochar dosage: 5% (based on total solids) Pan et al., 2019 Swine manure digestate 550 n/a 242.6 206.3 (control) Anaerobic co-digestion of swine manure and sewage sludge Temperature: 35 oC Biochar dosage: 4 g/L Xu et al., 2020 Sewage sludge 550 109.2 81 69 (control) Anaerobic digestion of waste activated sludge Temperature: 37 oC Biochar dosage: 10 g/L Chiappero et al., 2021 Sewage sludge 550 32.7 287.5 241.9 (control) Anaerobic co-digestion of food waste and sewage sludge Temperature: 35 oC Biochar dosage: 10 g/L Johnravindar et al., 2021 Food waste 400 0.64 360.1 347.9 (control) Anaerobic digestion of food waste Temperature: 35 oC Biochar dosage: 5% (based on total solids) Swiechowski et al., 2022 Alkaline cow manure 400 2.06 165.2 90.7 (control) Anaerobic digestion of alkaline cow manure Temperature: 37 oC Biochar dosage: 5% (based on total solids) Pan et al., 2022 Organic fraction of municipal solid waste (OFMSW) digestate 700 - 990.0b 922.5 (control) Anaerobic digestion of OFMSW Temperature: 35 oC Biochar dosage: 10 g/L Alghashm et al., 2023 a CH4 yield is based on VS removed rather than VS added b Biogas yield reported Shortening lag period Adding biochar to AD systems has shown promising results in shortening the lag period of CH4 production, thereby accelerating the start-up of the process. For instance, LuÌ et al. (2016) found that biochar addition reduced the lag period by 24% under the dual inhibition of ammonia and 23 VFAs. Shi et al. (2022) observed a reduction in the lag phase by 7.8 hours with biochar dosage of 0.6 g/g VS under oily suppression. Wang et al. (2018) reported a decrease in lag time of sludge and food co-digestion by 27.5â64.4% with biochar addition. Similarly, Sunyoto et al. (2016) observed a reduction in the lag period of CH4 production and hydrogen production in two-stage AD with biochar addition, reporting 21.4â35.7% and 41â45% decreases in the lag phase of H2 and CH4 reactors, respectively. Other studies that have also demonstrated shorter lag period of AD using various substrates. For example, straw and cow manure (Shen et al. 2020), beer lees (Sun et al. 2019), phenol (Wang et al. 2020), and leachate (Tsui et al. 2021). Moreover, biochar addition to the AD of simulated food waste resulted in a 41â45% reduction in the lag phase duration while increasing the maximum methane production rate and biomethane potential (Luo et al., 2022). In-situ biogas upgrading In recent years, the in-situ upgrading of biogas using biochar as an adsorbent in AD has gained much attention due to its potential to enhance methane content and mitigate CO2 and H2S levels, thus improving the calorific value of biogas and commercial viability of AD (Choudhury et al., 2020; Nguyen et al., 2021a). While ex-situ methods involving biochar have shown promise, the focus is also given to in-situ approaches for their simplicity and cost-effectiveness. Several studies have demonstrated the effectiveness of biochar in enhancing methane production and reducing CO2 generation when added to AD system. For instance, Shen et al. (2015) reported methane content increases by up to 90%, along with significant reductions in CO2 levels. Shen et al. (2021) also observed a 7.4% increase in CH4 content and a 5.9% decrease in CO2 content during cornstalk AD. Notably, in-situ biogas upgrading using biochar has been shown to enhance biogas quality and achieve methane content up to 95 vol%, meeting pipeline quality standards (Shen et al., 2015; Wang et al., 2019). Furthermore, CO2 removal efficiencies ranging between 54.9% and 86.3% have been reported. Linville et al. (2017) assessed biochar derived from walnut shell and found significant removal of CO2 by coarser biochar (51%) and smaller biochar (61%) in AD of food waste. In two-stage waste-activated sludge AD using pine 24 wood and corn stover biochars, CH4 contents of 81â88.6% and 72.1â76.6% were observed, respectively (Shen et al., 2017). Biochar has also shown promise for in-situ desulfurization, with studies reporting over 95% reduction in H2S content in biogas from AD of chicken manure and dairy manure (Liang et al., 2017; Choudhury et al., 2020). The CO2 adsorption capacity of biochar was found to surpass that of commercial adsorbents, reaching up to 138.2 mg/g, while its H2S adsorption capacity depends on factors such as specific surface area and porosity (Chiappero et al., 2021; Sahota et al., 2018). In addition to laboratory studies, research has explored the feasibility of in-situ biogas upgrading using biochar in larger-scale AD systems. Shen et al. (2015) demonstrated significant methane content increases and process stability improvements in thermophilic AD of sewage sludge with biochar addition. Shen et al. (2017) also reported enhanced substrate utilization and methane productivity in semi-continuous AD system with biochar supplementation. Moreover, the integration of anaerobic digestion and pyrolysis systems offers a promising approach for achieving circular economy principles. By using the substrate or solid digestate from AD as feedstock for pyrolysis, integrated systems can enhance overall methane yield while simultaneously valorizing waste materials (Nguyen et al., 2021). Nevertheless, practical applications of biochar for in-situ biogas upgrading remain limited. Challenges include the potential reduction in adsorption capacity when other compounds occupy biochar's surface adsorption sites. Further research is needed to validate and scale up in-situ biogas upgrading using biochar, addressing challenges that are related to adsorption capacity reduction and optimizing the process parameters. 2.6.3 Mechanisms of biochar in AD Biochar interacts with various aspects of AD, including microbial communities, substrates, and metabolic pathways, thereby modulating key factors such as pH, microbial activity, and gas production. Investigating these mechanisms offers insights on the complex interactions between biochar and AD process, and how biochar amendments can enhance system stability, shorten lag 25 periods, and improve biogas quality. Figure 2.2 illustrates the different proposed mechanisms for biochar in AD systems. Figure 2.2: Characteristics of biochar, extracted from (Zhao et al., 2021). Buffering effect AD efficiency is intricately tied to pH value, where even minor fluctuations can substantially impede microbial activity (Goswami et al., 2022). Acidogenesis, a critical stage in AD, is particularly susceptible to rapid acidification, leading to the accumulation of VFAs and inhibiting methanogen activity. Alkalinity in AD systems refers to the liquid's capacity to neutralize acids and maintain stable pH level throughout the digestion process (Saif et al., 2022). Hence, alkalinity is a pivotal factor. Three components of alkalinity have been identified in the literature: organic functional groups, carbonates (salts of carbonate and bicarbonate), and other inorganic alkalis. Biochar supplementation presents a compelling strategy to bolster alkalinity and sustain optimal pH conditions, thereby curtailing VFAs accumulation and accelerating digestion kinetics. Several studies have demonstrated the positive impact of biochar on buffering capacity and AD performance. For instance, a dosage of 10 g/L biochar increased pH from 7.74 to 8.15 during the start-up of FW AD, mitigating VFA inhibition (Zhao et al., 2021). Ma et al. (2020) reported a 44% decrease in the lag phase and 25% increase in the maximum methane production rate after adding 15 g/L biochar, attributed to its pH buffering ability. Meng et al. (2020) observed improved alkalinity and buffering ability with biochar addition. 26 Biochar's resilience to acidic/alkaline fluctuations hinges on two primary factors: 1) functional groups, and 2) inorganic elements. Functional groups that are inherent to biochar include phenolic (X-OH), carboxylic (âCOOH), and amine (âNH2) moieties, acting as key mediators in resisting abrupt pH shifts. These functional groups are derived during pyrolysis, which exhibit proton adsorption and electron acceptance capabilities, thereby mitigating pH volatility (Saif et al., 2022). The ash content of biochar contains inorganic elements that include alkali and alkaline earth metals such as Ca, K, Mg, Na, Al, Fe, Si, and S. These metals, often present as oxides or carbonates, contribute to the alkalinity of biochar, thereby preventing acidification by reacting with VFAs and enhancing buffering capacity (Saif et al., 2022). The concentration of these alkaline metals is determined by the ash elemental composition of the biochar and their presence is also potentially important for increasec biochar porosity. Furthermore, the pH and alkalinity of biocahr are intricately linked to pyrolysis conditions, feedstock characteristics, and heating rates. For instance, increase in pyrolysis temperatures induce organic acid dehydration, which raises biochar pH (Chen et al., 2014, Li et al., 2019). Concurrently, pyrolysis temperature modulation alters the abundance of phenolic, carboxylic, and amine groups on the biochar surface (Liang et al., 2022). Al-Wabel et al. (2013) found that acidic functional groups decreased from 4.17 to 0.22 mmol/g, and alkaline functional groups increased from 0.15 to 3.55 mmol/g when pyrolysis temperature increased from 200 to 800°C. As pyrolysis temperature increases, the degree of carbonization of organic matters is enhanced, leading to an increase in ash content. Moreover, the heating rate of biochar also affects its structural alkalinity; the faster the heating rates, the higher the alkalinity (Nzediegwu et al., 2021). Despite the predominantly alkaline nature of biochar, variations in feedstock composition may yield biochar having neutral to acidic pH values (Soothar et al., 2021). Disinhibition effect Biochar plays a crucial role in mitigating inhibition risks in AD systems, particularly from ammonia and acids. It has been observed in several studies that biochar supplementation effectively alleviates double inhibition risks by enriching specific methanogenic species, such as Methanosaeta and Methanosarcina, thereby promoting methane production (Zhang et al., 2020). 27 In this aspect, the effectiveness of biochar is attributed to its high specific surface area and adsorption capability. Research has shown that biochar can adsorb various inhibitory substances, including heavy metals, phosphate, and VFAs (Fagbohungbe et al., 2017; Song et al., 2021, (Johnravindar et al., 2021). Biochar exhibits promising results in alleviating ammonia inhibition in AD systems. Chen et al. (2008) reported a wide range of TAN concentrations (between 1.7 and 14 g/L) causing a 50% reduction in methane production. TAN is considered the main cause of inhibitions for methanogens and it was reported to have a toxic effect to anaerobes when its concentration was 150-1200 mg/L. Giwa et al. (2019) reported that biochar supplementation enhances the abundance of Methanosaeta and Methanobacterium, contributing to the alleviation of ammonia stress. Lu et al. (2016) evaluated the effectiveness of biochar obtained from the pyrolysis of fruitwoods at 800â900 °C in alleviating ammonium inhibition during AD of glucose. Biochar applied at a dosage of 10 g/L, and with different particle sizes (2â5 mm, 0.5â1 mm and 75â150 μm) was able to resist ammonium stress up to 0.26, 3.5 and 7 g/L. All biochar treatments also seemed to result in shorter lag phase and higher maximum methane production rate than their respective controls, and the effectiveness was enhanced with increasing biochar particle size. In addition to its role in mitigating inhibition risks, biochar can accelerate the degradation and oxidation of specific VFAs (such as propionate and isobutyrate) to acetate, following the VFAs accumulation at the initial start-up phase. This can be attributed to biochar providing colonizing areas for syntrophic bacteria and methanogens, thereby relieving acid stress and promoting methane production (Giwa et al. 2019). However, Sunyoto et al. (2016) observed that cultures with higher biochar dosages led to more propionic acid accumulation during the latter half of AD, potentially inhibiting methanogenesis. Thus, it is important to note that biochar supplementation can also have adverse effects on VFA degradation in AD systems. Microbial immobilization As previously mentioned, biochar supplementation in AD systems has been shown to enhance various bacterial species critical for methanogenesis and organic matter degradation. Methanolinea contributes to acetate conversion to methane, while Methanobacterium and 28 Methanosarcina are important hydrogenotrophic and acetoclastic methanogens, respectively (Goswami et al., 2022). Additionally, biochar addition accelerates the syntrophic oxidation of VFAs, mitigating the inhibitory effects of acidogenic products (Chiappero et al., 2020). Biochar facilitates the immobilization of microorganisms, promoting biofilm growth and enhancing cation exchange throughout its porous structure (Qi et al., 2021). Biochar's porous structure serves as an excellent matrix for bacterial and methanogen growth in AD digesters (Zhang et al., 2020). Besides, biochar's porosity enables biofilm formation, offering protection for effective microbes in acidic environments. Biochar also enhances extracellular polymeric substance secretion during biofilm formation, enhancing microbial adhesion (Goswami et al., 2022). Biochar also leaches essential nutrients (e.g., Na, K, Mg, N and P), further improving digestion performance (Ambaye et al., 2021; Zhao et al., 2021). The microstructure of biochar governs microbial retention on its surface and in the pores, which enables microbial acclimatization to inhibitory substances and promoting rapid exchange of intermediates between acetogens and methanogens (Luo et al., 2015). Direct interspecies electron transfer Direct Interspecies Electron Transfer (DIET) represents a thermodynamically advantageous form of electron exchange, allowing for cooperative degradation of organic compounds under anaerobic conditions. Electrotrophic microorganisms, such as Methanobacterium-like and Methanococcus archaea, have demonstrated the ability to produce methane faster with elemental iron as the sole source of electrons, indicating their capacity to import electrons through the cell membrane (Dubé and Guiot, 2015). DIET has emerged as a much faster pathway for electron exchange in syntrophic communities. DIET operates in favour of the substrates and intermediates degradation quicker than mediated interspecies electron transfer (MIET); with the electron transfer velocity being 106 times faster than the latter (Jiang et al., 2020; Qi et al., 2021). Studies have demonstrated the selective enrichment of potential DIET-partners, including Geobacter and Methanosaeta, in biochar-amended reactors during AD. This enrichment leads to significant improvements in VFAs utilization, chemical oxygen demand (COD) removal, and methane recovery (Saif et al., 2022; Shi et al., 2022). 29 Biochar plays a crucial role in promoting DIET by enhancing microorganism colonization on its surface. Its redox-active organic functional groups, such as quinone and hydroquinone, serve as electron shuttles to facilitate DIET (Zhao et al., 2021). The efficiency of DIET is closely related to biochar's electrical conductivity and active redox properties. Despite the generated aromatic structure of biochar under higher pyrolysis temperature that promotes electron transferr, the decomposition of oxygen-containing functional groups into CO, CO2, and H2O would decline the electron transfer. Considering that the electron transfer ability of biochar is ultimately due to its functional groups and graphitized structures, tit is critically important to choose the pyrolysis temperature that is beneficial for biochar utilization in AD (Rasapoor et al., 2020). 2.6.4 Factors impacting biochar for methane production Various biochar properties, including porosity, specific surface area, pH, and surface functional groups, play major roles in shaping the outcomes of biochar supplementation. The intricate interplay between these factors and their dynamic effects on microbial activities and metabolic pathways ultimately determines the success of biochar as an additive in enhancing methane yield. Porosity and specific surface area Porosity and specific surface area (SSA) are pivotal parameters influencing the efficacy of biochar in AD systems, particularly in methane production. Porosity, characterized by pore volume and distribution, dictates biochar's capacity for adsorption and microbial habitat provision. Biochar typically exhibits a pore size distribution consisting of micro- (< 2nm), meso- (2â50 nm), and macro-pores (>50 nm) (Yuan et al., 2017). Micro- and meso-pores are vital for adsorption, while macro-pores facilitate microbial colonization. Notably, high-temperature pyrolysis can enhance SSA and pore development, thereby augmenting biochar's porosity. Studies have observed significant contributions of micro-pores to biochar SSA, which have a high capacity for the adsorbate and water uptake (Fagbohungbe et al., 2017; Zabaniotou et al., 2008). 30 In assessing adsorbent performance, factors such as pore distribution, surface chemistry, and pH are particularly important. Biochar, with its abundance of micropores, exhibits high adsorption capacity, as evidenced by studies demonstrating micropores' high adsorption potential. Biochar supplementation in AD systems has shown significant improvements in methane production, with varying effects attributed to pyrolysis temperature. For instance, (Pan et al., 2022) reported that methane production increased by 81.3% and 78.0% with the addition of specific biochar types, namely NFC-400 (cow manure biochar, pyrolysis temperature 400°C) and JKC-550 (mushroom bran biochar, pyrolysis temperature 550°C), respectively. Besides, the potential for improving biogas production by adding NFC-400 was higher than that of JKC-550, while the fermentation period was shorter with the addition of JKC-550 than NFC-400. Nevertheless, the SSA and porosity of the biochars were not positively correlated with biogas production. Some other studies present differing views on the correlation between SSA and methane yield. While larger SSA observed in certain biochar types facilitated microbial enrichment and potentially contributed to enhancements in methane production, other research suggests that SSA may not be the sole determinant of methane yield (Johnravindar et al., 2021; Pan et al., 2022). Further investigation is warranted to elucidate the complex relationship between biochar properties, including SSA, and methane production in AD systems. pH The pH of biochar is a crucial factor influencing its performance in anaerobic digestion (AD) systems, particularly in methane production. This pH variation within biochar is primarily attributed to the cleavage of weak bonds, such as hydroxyl bonds, within its structure at high pyrolysis temperatures (Chiappero et al., 2020). Thermal decomposition releases oxygenated carbon, resulting in a deprotonated biochar surface and subsequent alteration in pH. As a result, biochar can be tailored to modify pH levels based on the pyrolysis temperature, providing flexibility in correcting acidic or alkaline environments within the digester. Moreover, higher temperatures generally result in a more basic pH. This shift is primarily driven by the 31 volatilization of acidic functional groups and higher ash content in biochar (Lehmann et al., 2015; Li et al., 2019). Studies have highlighted the importance of biochar's pH in maintaining AD process stability and buffer capacity. Furthermore, biochar's alkalinity can effectively counter pH drop induced by the production of VFAs. Lower pH levels (< 6.6) have been associated with decreased methanogen activity, underscoring the significance of biochar's pH in sustaining optimal methane production (Demirel and Scherer 2008; Zhang et al., 2022). Surface functional groups Surface functional groups of biochar affects electron transfer processes and microbial interactions. Qualitative analysis of biochar surface functional groups using Fourier-transform infrared spectroscopy (FTIR) reveals important insights into their impact on methane production. For instance, hydrophobic functional groups promote microbial attachment to biochar surfaces, potentially facilitating electron transfer processes (Johnravindar et al., 2021). Diverse functional groups formed on the biochar surface also contribute to varied surface chemistry, with oxygen-containing functional groups notably promoting electron transfer between microorganisms and potentially enhancing methane production (Pan et al., 2022). X-ray photoelectron spectroscopy analysis has demonstrated a positive correlation between surface oxygen-containing functional groups and methane production rates in biochar-containing AD systems. Higher concentrations of oxygen-containing functional groups, such as arene rings, phenol moieties, and quinone moieties, were observed to potentially facilitate DIET and exhibit redox activity (Zhang et al., 2022). 2.7 Methods for conducting Biochemical Methane Potential (BMP) tests Biochemical Methane Potential (BMP) tests are fundamental in estimating the maximum methane yield of organic substrates in AD, employing various methods for the determination of CH4 production. Common approaches include volumetric and manometric methods, while direct quantification of CH4 by gas chromatography or gravimetric measurement coupled with gas composition determination are less prevalent (Hafner et al., 2015; Hansen et al., 2004). Table 2.9 32 provides an overview of these diverse methods, reflecting the variety of approaches adopted across laboratories. Despite this methodological diversity, the determination of BMP remains critical for advancing research, facilitating plant design, and optimizing management practices in anaerobic digestion (Koch and Drewes, 2014; Strömberg et al., 2014). BMP serves as a cornerstone for evaluating substrate biodegradability, assessing substrate suitability, and establishing correlations between laboratory-scale findings and full-scale plant performance (Da Silva et al., 2018). However, the comparability of biodegradability data across literature is hindered by a number of factors. Variations in equipment, environmental conditions, and testing protocols pose challenges to data comparison. For instance, differences in the inoculum-nutrients mix, liquid and headspace volumes, pH levels, headspace pressure, and detection systems can vary significantly between studies. The presentation of results in varying units can further complicate the comparison process. This lack of standardization underscores the importance of establishing uniform BMP testing protocols to ensure reliable and consistent data generation (Amodeo et al., 2020). Addressing these challenges is vital for advancing anaerobic digestion technologies and research and driving sustainable waste management solutions forward. Efforts to standardize BMP testing protocols have been proposed through various guidelines and recommendations, aiming to enhance the comparability and reliability of the results. Table 2.10 summarizes these suggested guidelines. Establishing uniform protocols not only improves the consistency of BMP measurements but also facilitates the interpretation and comparison of results across studies. 33 Table 2.9: Comparison of biogas measurement methods in BMP testing (Amodeo et al., 2020; Angelidaki et al., 2009; Astals et al., 2020; Hafner et al., 2019; Hafner et al., 2020; Holliger et al., 2016; Justesen et al., 2019; Koch et al., 2019; Strömberg et al., 2014). BMP test Method Key Features Biogas Composition Measurement Advantages Challenges Manometric Measurement Based on measurement of the headspace pressure and composition as biogas accumulates within a constant headspace volume. Typically relies on gas chromatography (GC) or infrared gas analyzer to analyze biogas composition. Well-established method widely used in BMP testing. Easy to carry out and has high sensitivity Susceptible to systematic errors like gas leaks, temperature fluctuations, and volume corrections. It may underestimate the BMP. Headspace pressure can affect measured biogas production. Gravimetric Measurement Measures by capturing and weighing gas over time. Accurate even in the presence of leakage Requires careful adherence to protocol for accurate results. Volumetric Measurement Based on volumetric displacement in an external vessel at constant pressure. Provides real-time measurement of biogas volume during experiments. Higher equipment costs compared to other methods. Automated Systems (e.g., AMPTS II, AMPTS III) Utilizes automated equipment to continuously monitor biogas production. Scrubs CO2 with an alkaline solution and provides automated measurement of ultra-low residual CH4 flow using tipping buckets. Offers high precision and efficiency in data collection, reducing the need for manual intervention. Initial setup costs can be significant. Maintenance and calibration are necessary for reliable operation. Gas Density Method Determines biogas composition based on gas density. Measures biogas density, using a simple manometer, syringes, and a laboratory scale. Enhances accessibility to BMP testing. Requires thorough validation to ensure accuracy and reliability. Very time consuming. 34 Table 2.10: Guidelines for Biomethane Potential (BMP) Testing Protocols and Data Analysis (Angelidaki et al., 2009; Astals et al., 2020; Hafner et al., 2020; Holliger et al., 2016; Koch et al., 2019; Strömberg et al., 2014; Valero et al., 2016). Suggested guidelines for BMP Substrate â¢ Characterize as thoroughly as possible: -Total solids and volatile solids -Chemical oxygen demand -Nitrogen and phosphorus -Lignin, cellulose and hemicellulose (particularly for energy crops and agro-waste) Inoculum â¢ The inoculum should be taken from an active anaerobic digester and is at steady-state at the time of sampling. It is preferred to use fresh inoculum, but it could be stored for up to 1 month at 4 C or room temperature without a major impact on BMP test results. â¢ The BMP assay temperature, mesophilic or thermophilic, is usually the same as the operating temperature of the inoculum digester. â¢ Endogenous methane production should be below 20% of total methane production. â¢ Should be âdegassedâ at the same temperature as the process temperature (~2-5 days) â¢ Coarser materials, if present, should be removed by sieving through a 1â5 mm mesh screen. Dilution should be avoided. Blank and controls â¢ Carry out blank assay from inoculum and a positive control (e.g. microcrystalline cellulose, tributyrine) Experimental set up â¢ Perform assays at least in triplicates. â¢ ISR should for most applications be between two and four. For easily degradable substrates an ISR greater than or equal to four should be applied. For less degradable substrates, an ISR less than or equal to one can be applied. â¢ Assay should end when daily methane production during three consecutive days is <1% of the accumulated methane. â¢ If substrate inhibition is suspected, tests with several S/I ratio should be carried out in parallel. â¢ Incubation without any mixing should be avoided. If continuous mixing is applied, it should be gentle. Manual mixing once a day to avoid scum layer formation is in most cases sufficient, except if one wants also to determine kinetic parameters. Data analysis â¢ BMP should be expressed as the volume of dry methane gas under standard conditions (273.15 K and 101.33 kPa) per mass of volatile solids. â¢ Test results must be rejected: - If the relative standard deviation (RSD) of the blank or the positive control is <5%, even after applying a statistical test to eliminate a single outlier -If the RSD of a homogenous substrate is <5%, even after applying a statistical test to eliminate a single outlier - If the RSD of a heterogeneous substrate is <10%, even after applying a statistical test to eliminate a single outlier -If the BMP of the positive control is >85% and <100% of the theoretical BMP (e.g. for cellulose: >352 mL gVS -1 and <414 mL g VS -1). To eliminate a single outlier from triplicate measurements, the Dixonâs test can be used. 35 Gas-density method The gas density-based biochemical methane potential (GD-BMP) method, as outlined by Justesen et al. (2019), operates by leveraging biogas density derived from bottle (reactor) mass loss and biogas volume to accurately measure BMP. This innovative approach eliminates the need for expensive instrumentation, specially to determine the biogas composition, making it particularly suitable for resource-constrained settings. In the GD-BMP method, biogas composition is determined based on the density of the biogas, which is calculated from the mass loss of the bottle and the volume of biogas produced. These parameters can be accurately measured using standard laboratory scales, inexpensive syringes, and a simple manometer. By applying this method, researchers can obtain reliable BMP measurements without the need for costly equipment. Through rigorous validation across various laboratories and experimental conditions, the GD-BMP method has demonstrated consistent accuracy, with minimal bias compared to established gravimetric or volumetric methods. It has also consistently yielded comparable results to industry-standard automated systems (e.g. AMPTS II), validating its reliability across different scenarios. Notably, the method's precision remained robust even in scenarios involving biogas leakage, due to its ability to exclude leakage from mass loss measurements. Error in the GD-BMP method is mainly related to weighing accuracy, and therefore selection of an appropriate scale is important. Scale accuracy should always be reported with GD-BMP results. For high accuracy and low random error (RSD < 5%) in BMP, a reported scale accuracy of 30 mg or better appears to be sufficient when using as little as 1 g of the substrate VS. Focusing instead on observed measurement variability, a limit of 5 mg for standard deviation in mass of water control bottles appears to be sufficient for 1 g of substrate VS. High weighing accuracy may provide results that are comparable to the best existing BMP measurements. Where substrate VS mass is larger, less weighing accuracy is needed. 36 Chapter 3: MATERIALS AND METHODS This Chapter describes the procedures and equipment used to conduct the study. Focus is placed on investigating the performance of mesophilic anaerobic digestion through biochemical methane potential (BMP) assays. The contents of this Chapter include the materials utilized in the AD experiments (substrate, inoculum, and biochar), along with the methodologies employed for experimental setup and analysis. 3.1 Materials The substrates utilized in the BMP tests comprised a mixture of animal manure (50 wt% dairy manure, 25 wt% hog and 25 wt% chicken manure) sourced from three farms (Abbotsford, BC), wheat straw sourced from a farm in Alberta, synthetic food waste (as model organic fraction of municipal solid waste, M-OFMSW)) prepared using food items that were procured from Choices Markets (Vancouver, BC), and microcrystalline cellulose (Sigmacell). The composition of M-OFMSW is shown in Table 3.1. Size reduction of the synthetic food waste was done by grinding 1 kg of the materials at high speed in a Kitchen Aid food processor for 30 seconds. Table 3.1: Model organic fraction of municipal solid waste (M-OFMSM). Food item wt% Cooked rice 15.0 Cooked pasta 15.0 Cabbage 9.2 Carrot 9.2 Apple 9.2 Banana 9.2 Corned ground beef 8.3 Dog food 8.3 Tap water 16.6 The inoculum, designated as I1 and I2, were collected from anaerobic cultures sourced from a full-scale anaerobic digester at Seabreeze Farms (Delta, BC). 37 Biochar samples were made by the Pyrolysis Group at the Clean Energy Research Centre, UBC using the microwave-assisted catalytic pyrolysis and a lab-scale fixed bed microwave reactor (Enwave Corporation, Vancouver, BC). Primary solid digestate collected from Seabreeze Farms and lignin were used as the feedstocks for pyrolysis. Two types of catalysts were employed: â20Kâ (i.e., 20 wt% potassium phosphate, K3PO4), and â10K10Câ (i.e., a mixture of 10 wt% K3PO4 and 10 wt% clinoptilolite (zeolite). The pyrolysis process was conducted at either 400°C or 500°C, with 30 min residence time. The different feedstocks, pyrolysis catalysts, and pyrolysis temperature led to the production of five different types of biochars. After pyrolysis was done, each biochar sample was washed with distilled water for 30 min with agitation, followed by overnight drying at 105°C. Subsequently, it was ground with a mortar and pestle, and sieved to <0.5 mm (device; Newark, USA), and stored in a desiccator. 3.2 Methods Detailed procedures are presented for the characterization of materials, data collection, and statistical analysis employed in this study. These methods are essential for elucidating the properties of biochar, feedstock, and inoculum, as well as for accurately measuring methane content and yield. In addition, the estimation of kinetic parameters and application of statistical techniques enable a comprehensive analysis of the anaerobic digestion process. 3.2.1 Biochemical methane potential assays (BMP) BMP assays were conducted using two different setups: AMPTS II (Test Series 1) and GD-BMP (Test Series 2). Established guidelines from the literature (Angelidaki et al., 2009; Filer et al., 2019; Ohemeng-Ntiamoah & Datta, 2019) were followed for the batch BMP assays setup. Test Series 1 BMP assays using the AMPTS II system (BPC Instruments, AB) involved 12 bioreactors with a capacity of 0.5 L each. The instrument comprised a sample incubation unit, CO2-absorbing unit, gas volume measuring device, and data acquisition system. Each vial containing media was incubated in a water bath at 37°C and continuously mixed by a slow rotating agitator. Biogas produced was channeled to the CO2-absorbing unit, where acid gas fractions were retained by 38 chemical interaction with sodium hydroxide (NaOH), allowing only CH4 to pass through to the biomethane gas volume measuring device. CH4 gas volume was determined using a wet gas flow measuring device, and an embedded data acquisition system for recording and analyzing the results. Assays were performed in duplicates for each treatment, with a retention time of 30 days. Figure 3.1: Test Series 1 set-up consisting of the AMPTS II system. Test Series 2 GD-BMP setups consisted of a 20 L incubator (Vervor) at 37°C and nine 500 mL media bottles (Corning, Pyrex) fitted with a bromobutyl rubber stopper (DWK Life Sciences, Duran) as bioreactors. The inoculum was stored at 20°C for up to 30 days prior to use. Moreover, they were pre-incubated for 5-7 days at 37°C to degas and eliminate the impact of residual organic matter on methane production (Angelidaki et al., 2009). Bottles and stoppers were dried overnight at 60°C before starting the digestion. 39 Figure 3.2: Test Series 2 set-up consisting of an incubator, media bottles and balance. The inocula and substrates were prepared following the experimental design, and bottles were sealed after being flushed with nitrogen gas for 5 min to maintain anaerobic conditions. The liquid volume of each bottle was around 100 mL. To prevent gas pressure from exceeding 200 kPa inside the serum bottles, a minimum of 400 ml of headspace was left in them. BMP bottles were monitored for biogas production daily, whereby mass loss and biogas volume were measured. To determine biogas volume at atmospheric pressure, an electronic scale with a readability of 0.01 g (Vervor), 150 mL plastic syringes with 21-gauge needles (VWR), a 3-way stopcock (Qwork), and a digital differential manometer (Dwyer) were used. For each sampling event, the bottle was mixed by swirling and weighted (pre-venting mass), then the biogas was removed with a syringe (vented biogas volume) and weighted again (post-venting mass). Water-only control (triplicates) is also weighted to assure a stable response from the balance. The assay was performed in triplicate for each scenario to ensure the reproducibility of the tests and improve statistical analysis. Retention time was ca. 18 days (until daily biogas production reached within 1% of accumulated biogas). 40 Figure 3.3: Data collection steps of the GD-BMP measurements for each sampling event (Justesen et al., 2019). 3.2.2 Experimental design The experimental design encompassed two main test series, each using distinct substrates for the digestion. Test Series 1 employed a mixture of animal manures as a substrate, while Test Series 2 utilized synthetic food waste. A total of five powdered biochars were added to BMP assays in various combinations of substrate. In addition, co-digestion with wheat straw at two mixing ratios was tested. Wheat straw has high carbon-to-nitrogen (C/N) ratio but lower biodegradation rate compared to the more readily biodegradable manure and food waste. Hence, co-digestion of manure with wheat straw is expected to reduce the risk of ammonia nitrogen (NH3-N) inhibition and stabilize the digestion process. Control assays, set up similarly but without biochar amendments, were included in the test. Aside from the blanks that contain only inoculum and water-only controls. a positive control with microcrystalline cellulose as the substrate was tested. The experimental setup consisted of 32 BMP bottles for Test Series 1 (duplicates) and 54 BMP bottles for Test Series 2 (triplicates). The substrate to inoculum ratio (SIR) was maintained at 0.5, and two dosages of powdered biochar were employed for each substrate - low dosage of 0.1 g/g substrate TS (or 8 g/L substrate), and high dosage of 0.6 g/g substrate TS (or 48 g/L substrate). Further details regarding the experimental treatments are provided in Table 3.2 and 3.3. 41 Table 3.2: Experimental design of Test Series 1. Treatment Pyrolysis temperature (oC) Pyrolysis catalyst Biochar dosage (g/g TS) T1 (Control) T2-T9: Using solid digestate as the pyrolysis feedstock T2 400 20K 0.1 T3 400 20K 0.6 T4 500 20K 0.1 T5 500 20K 0.6 T6 400 10K10C 0.1 T7 400 10K10C 0.6 T8 500 10K10C 0.1 T9 500 10K10C 0.6 T10-T11: Using lignin as the pyrolysis feedstock T10 500 20K 0.1 T11 500 20K 0.6 Treatment Mass ratio (manure:wheat straw based on VS) T12 95:5 T13 80:20 The substrate for anaerobic digestion (AD) test is âmanure + biocharâ for Treatments T2-T11. The substrate for AD test is âmanure + wheat strawâ for Treatments T12-T13, and no biochar was added. The methodology ensures a systematic approach to investigating the influence of digestate-derived biochar on methane yield improvement, and HRT reduction during mesophilic anaerobic digestion. 42 Table 3.3: Experimental design of Test Series 2. Treatment Pyrolysis temperature (oC) Pyrolysis catalyst Biochar dosage (g/g TS) T1 (Control) T2 (Positive Control) T3-T10: Using solid digestate as the pyrolysis feedstock T3 400 20K 0.1 T4 400 20K 0.6 T5 500 20K 0.1 T6 500 20K 0.6 T7 400 10K10C 0.1 T8 400 10K10C 0.6 T9 500 10K10C 0.1 T10 500 10K10C 0.6 T11-T12: Using lignin as the pyrolysis feedstock T11 500 20K 0.1 T12 500 20K 0.6 Treatment Mass ratio (food waste:wheat straw based on VS) T13 95:5 T14 80:20 The substrate for anaerobic digestion (AD) test is âfood waste + biocharâ for Treatments T3-T12. The substrate for AD test is âfood waste + wheat strawâ for Treatments T13-T14, and no biochar was added. 43 3.2.3 Characterization of materials In this section, the materials utilized in the anaerobic digestion process, including biochar, feedstock, and inoculum, are comprehensively characterized. By comprehensively characterizing these materials, insights are gained into their properties and interactions within the anaerobic digestion system. 3.2.3.1 Biochar Measuring the physicochemical properties of biochar is pivotal in understanding its role as an additive in AD for biogas production and the potential implications for the overall process efficiency. Pore properties and elemental analysis The specific surface area of biochar was measured by N2 adsorption/desorption isotherm at 77 K on a Micromeritics ASAP 2020 instrument in the Department of Forestry, UBC (Vancouver). First, 0.4~0.5 g biochar sample was degassed under vacuum atmosphere at 150°C for 8 h on the built-in degassing port of the instrument. After degassing, the sample was weighed again and recorded as the real mass before analysis. The total surface area and average pore diameter were determined by Brunauer-Emmet-Teller (BET) analysis, whereas the micropore volume and pore size were determined by Barette-Jovner-Halenda (BJH) method. Elemental analysis of the five biochars produced was conducted using Thermo Flash 2000 Elemental Analyzer (Thermo Fisher Scientific, U.S.A) in the Department of Chemistry, UBC (Vancouver). The carbon (C), nitrogen (N), sulphur (S), and hydrogen (H) contents of the samples were measured from the instrument while, oxygen (O) content was calculated by the difference. Surface functional groups Fourier Transform Infrared (FTIR) spectroscopy was employed to analyze the functional groups present in the biochar samples. The FTIR analysis was conducted using an INVENIO-S (Bruker Corporation) equipped with a DTGS (Deuterated Triglycine Sulfate) detector. Prior to analysis, the biochar samples were prepared by grinding them into a fine powder and mixing them with 44 potassium bromide (Sigma-Aldrich) at a ratio of 1:10. The mixture was then pressed into pellets under high pressure. FTIR spectra were acquired over a spectral range of 4000-400 cm-1 with a resolution of 4 cm-1. Each sample was scanned 64 times to ensure reproducibility of the results. Spectral data were processed and analyzed using Excel and Tableau. The functional groups present in the biochar samples were identified by comparing the obtained spectra with reference spectra available in the literature and spectral libraries. Peaks corresponding to specific functional groups were assigned based on their wavenumber and intensity. The peaks in an FTIR spectrum represent bond vibrations at specific frequencies and provide qualitative information about the functional groups being analyzed. Functional groups that can be distinguished using FTIR include carboxylic acids, esters, alcohols, and ketones. pH To determine the pH of the biochar samples, 1 grams of air-dried ground biochar sample was added to 10 mL of deionized water. The mixture was then mechanically shaken for 1 hour at a temperature of 25°C to ensure thorough mixing and suspension of the biochar particles. Subsequently, the suspension was allowed to stand undisturbed for approximately 1 hour to facilitate settling. pH measurement was conducted using a pH meter calibrated with pH 7 and pH 10 buffers (Cole-Palmer, Oakton Instruments) to ensure accuracy. The pH meter (Fisher Science Education) was immersed into the suspension, and the pH value was recorded once stabilization was achieved. 3.2.3.2 Substrate and inoculum for AD The success of AD process is greatly influenced by the characteristics of both the substrates and inocula used. Proximate analysis The measurement of TS and VS followed standard methods outlined in APHA 2540 B and 2540 E protocols (APHA, 2005), respectively. Initially, crucibles were prepared by immersing them in 45 diluted sulfuric acid (20%) for approximately 2 hours. Following conditioning, the crucibles were cleaned, heated to 550°C to eliminate any contaminants and particles, and then placed in a desiccator to stabilize their weight and prevent moisture ingress. Using an analytical balance, empty crucibles were weighed, and well-mixed substrate samples (approximately 10 g) were added in triplicate and reweighed. The samples were then dried overnight at 105°C in an oven. After drying, the samples were transferred to a desiccator to cool to room temperature, and their weight was recorded. To determine the VS, the dried samples were subjected to further heating in a furnace at 550°C for 90 minutes. Following the heating program, the samples were allowed to cool to room temperature by initially placing them in the oven and then in the desiccator. Finally, the crucibles containing the remaining ash were weighed, and the TS and VS were calculated using the following Equations (3.1) and (3.2): ðð (%) =ððððâð¡ðð£ðð ððððð ððððâð¡ðððð¡ððð ð ðððððâ 100 (3.1) ðð (%) =ððððâð¡ðð£ððâðððððâððððâð¡ððð¡ðð ðð¢ððððð ððððâð¡ðððð¡ððð ð ðððððâ 100 (3.2) Nitrogen and carbon contents analyses Nitrogen content was measured at the Pacific Soil Analysis Lab (Richmond, BC), using the Kjeldahl method that involves three steps: digestion, distillation, and titration. Samples were homogenized to ensure uniformity. Once dried, the samples were ground into a fine powder to increase surface area and facilitate digestion. Digestion of the samples was carried out using a Kjeldahl digestion apparatus. In this process, concentrated sulfuric acid was added to the sample along with a mixture of copper sulfate and selenium. The mixture was heated under reflux until the organic nitrogen compounds were converted to ammonium sulfate. After digestion, the digested sample was cooled and transferred to a distillation apparatus. Distillation was performed by adding sodium hydroxide, to release the ammonia gas from the 46 digested sample. The liberated ammonia gas was then distilled into a known volume of a boric acid solution, which trapped the ammonia as ammonium borate. The excess acid in the distillate was then titrated with a standardized solution of sulfuric acid to determine the amount of acid that had reacted with the ammonia. The endpoint of the titration was detected using methyl orange. Organic matter content was measured using loss of ignition (LOI) method, with a procedure that follows the same steps as volatile solids content determination. The carbon content is then calculated as: ð¶ (%) = ððððððð ððð¡ð¡ðð ðððð¡ððð¡ /1.8 (3.3) pH The pH of the biochar suspensions was determined using a pH meter (Fisher Science Education). Prior to measurement, the pH probe was immersed in the sample until a stable reading was observed on the pH meter's screen, ensuring accurate and consistent results. In the case of food waste samples, 100 g of the waste material was blended with 20 g of distilled water before pH measurement. This step was undertaken to ensure uniformity and homogeneity of the sample, facilitating reliable pH determination. FOS/TAC ratio In monitoring the AD process, the FOS/TAC ratio is a key indicator of system stability and biomass input. The FOS value corresponds to the volatile fatty acids (VFA) concentration, and the TAC value is an estimation of the buffering capacity of the sample. A FOS/TAC ratio above 0.6 indicates excessive biomass input. Optimal biogas production occurs with ratios between 0.3 and 0.4, where biomass input should remain constant. A sample of 5 mL of fermentation substrate is titrated by 0.1 N of sulfuric acid solution (H2SO4) up to pH 5.0 to calculate the TAC value, expressed in mg/L of calcium carbonate (CaCO3). Then the FOS value is obtained after a second titration step between pH 5.0 and pH 4.4. It is expressed in mg/L of acetic acid (CH3COOH). At the end of the titration the electrode screen displays among other results the value of the FOS/TAC ratio. 47 3.2.4 Data and statistical analysis Various analytical approaches are employed to interpret the experimental data and assess the performance of the anaerobic digestion process. Firstly, the gas density method is utilized to determine the methane content and yield of the biogas produced during anaerobic digestion of Test Series 2. This method involves sampling the biogas from the reactors, measuring its volume and mass loss, and then calculating the biogas density. From the biogas density measurements, the methane content and yield are determined, providing valuable insights into the efficiency of methane production. Additionally, kinetic parameters are estimated to characterize the anaerobic digestion process and understand its underlying kinetics. This involves fitting experimental data to mathematical models, such as the first-order kinetic model and the Modified Gompertz model. By analyzing the kinetics of methane production over time, key parameters such as the hydrolysis rate and maximum methane production rate are determined. These parameters offer valuable information about the overall performance and stability of the anaerobic digestion process. Furthermore, statistical analysis is conducted to evaluate the significance of experimental results and identify any trends or correlations. Various statistical techniques, such as analysis of variance (ANOVA) and correlation analysis, are employed to assess the impact of different factors on methane production and identify potential relationships between variables. Gas-density method The gas density method described by Justesen et al. (2019) was followed to obtain the composition of the biogas. Biogas density under standard conditions of 101.325 kPa and 0 °C (ðð , g mLâ1) is calculated from bottle mass loss (âðð, g) and standardized biogas volume (ðð, mL), with a correction for the water vapor content of vented biogas (ðð»2ð, g mLâ1). Standardized biogas volume is determined from the measured vented volume by correcting for moisture, temperature, and pressure. Mass loss is determined from changes in post-venting bottle mass between sampling events or the change in bottle mass during venting: ðð =âððððâ ðð»2ð . (3.4) 48 Water vapor pressure (ðð»2ð, kPa) can be calculated assuming saturation: ðð»2ð = 0.61094 â ð17.625 â ðâð 243.04+ ðâð . (3.5) ðâð is the bottle headspace temperature at the time of venting (°C). Assuming bottles are removed from an incubator for measurement, ðâð will be between incubation and room temperatures. The concentration of water present in vented biogas (ðð»2ð, g mL-1, dry standardized biogas volume basis) is then calculated from this value, water molar mass (ðð»2ð = 18.02 g mol-1), pressure of biogas in the bottle headspace just prior to venting (ðâð , kPa), and the molar volume of biogas at standard conditions (ð£ð, assumed to be 22300 mL mol-1): ðð»2ð = ðð»2ð âðð»2ððâð âðð»2ðâ1ð£ð. (3.6) bottle headspace temperature and pressure must be considered. Pressure can be approximated from the volume of vented biogas (ratio of vented biogas volume to headspace volume times ambient pressure, plus ambient pressure). Temperature was approximated, as 5°C below incubator temperature. For both pressure and temperature, a single, fixed value for all observations is sufficient. The molar mass of biogas (ðð, g mol-1) is then obtained from biogas density: ðð = ðð â ð£ð. (3.7) Finally, the mole fraction of CH4 in biogas (ð¥ð¶ð»4 , dimensionless) normalized for CH4 and CO2 (ð¥ð¶ð»4+ ð¥ð¶ð2= 1) is calculated from molar masses of CO2, CH4, and biogas (ðð¶ð2= 44.01 g mol-1, ðð¶ð»4= 16.04 g mol-1): ð¥ð¶ð»4 =ðð¶ð2âðððð¶ð2âðð¶ð»4. (3.8) 49 Equation (3.8) is based on the assumption that biogas contains only CH4 and CO2. The presence of flushing gas will have a small effect (usually <5%). When N2 is used as a flushing gas, error will be negligible when xCH4 is ca. 0.58, because biogas has the same density as N2 in this case (1.25 mg/mL). The use of other gases or mixtures complicates this issue. In particular the high density of N2/CO2 mixtures inflates this source of error, and also adds uncertainty about the fate of flushing gas (vented or in solution). If nearly all flushing gas has been removed by the end of a trial, this source of error can be addressed by simply subtracting the flushing gas mass from âðð and volume from ðð using Equations (3.9) and (3.10). ðâ²ð = ðð â ðð. (3.9) âðâ²ð = âðð â ðð â ðð. (3.10) In Equations (2.9) and (2.10), ðð is the standardized volume of flushing gas in the bottle headspace prior to any venting, and ððis the density of flushing gas under standard conditions. For an individual bottle over a single incubation interval, bottle leakage is calculated as the difference between the pre- and post-venting bottle mass. The sum of these values across all measurement intervals is an estimate of total leakage for an individual bottle. Leakage detection limit can be estimated from the standard deviation in measured mass of sealed bottles with water only over all measurements. From this value a standard deviation in the sum of k values can be determined, and the limit of detection (LOD) may be taken as 3 (or more) times this value. ð¿ðð· = 3ð ð¤ðâð (3.11) Where ð is the total number of measurement intervals, and ð ð¤ð is the standard deviation in the measured mass of a water control bottle over all measurement intervals. Measured leakage larger than the detection limit is strong evidence that leakage occurred. In this case, an approach analyzing per sampling event may be used. However, precision is higher when analyzing the test totals and a more prudent approach is to repeat the test taking care to avoid leakage. For large leaks (e.g., perhaps > 20% of total mass loss) the test should always be repeated. 50 Kinetic rates estimation The estimation of kinetic rates in BMP assays involved fitting experimental methane production data to two distinct models: the modified Gompertz model and first-order kinetic model. The Modified Gompertz Model, as represented by Equation (3.12), is a widely accepted method for describing the cumulative methane production curve from anaerobic digestion in batch mode (Yu et al., 2019). This model is used to predict the biogas potential corresponding to the bacterial growth rate in the digester and is one of the most frequently employed sigmoidal models for fitting growth data. When there is a delay in biogas production, other models may not accurately represent the process, making the Gompertz equation particularly useful. One significant advantage of the Gompertz model is its flexibility in fitting growth data with or without a lag phase (Gomes et al., 2021). This model accounts for parameters such as cumulative methane production, the lag phase duration, and the maximum daily methane yield. The Modified Gompertz Model equation is represented as follows: ð = ð0 â ðð¥ð {âðð¥ð [ð ðâððð(ð â ð¡) + 1]} (3.12) M represents the specific cumulative methane produced at time t (ml g-1 substrate VS). ð0 is the potential maximum methane yield (ml g-1 substrate VS). ð ð denotes the maximum methane production rate (ml g-1 substrate VS-1). ð represents the lag phase (d). t is the duration of the assay (d). Furthermore, the coefficient of determination (R2) was obtained to evaluate the goodness-of-fit of the observed data to the Modified Gompertz Model. The first-order model, represented by Equation (2.13), is commonly employed to estimate the overall hydrolysis constant in BMP tests. In general, the rate-limiting step of anaerobic digestion of particulate wastes is the first step of hydrolysis or solubilization. This model simplifies the hydrolysis reaction to first-order kinetics, assuming that the rate of methane production is directly proportional to the concentration of substrate. It provides valuable insights into the initial 51 phase of anaerobic digestion, where complex organic compounds are broken down into simpler compounds by hydrolytic enzymes. ð = ð0 â [1 â ð(âð¾ð»âð¡)] (3.13) ð represents the specific cumulative methane produced at time t (ml g-1 substrate VS). ð0 is the potential maximum methane yield (ml g-1 substrate VS). e is the base of the natural logarithm. ð¾ð» is the hydrolysis rate (dâ»Â¹). t is the time (d). By analyzing the kinetics of hydrolysis, insights into the efficiency of substrate degradation and the overall performance of anaerobic digestion processes can be obtained. Statistical analysis For test series 2, statistical analyses were conducted to assess the impact of the operating conditions of biochar production on the specific biomethane yield. One-way and three-way analyses of variance (ANOVA) were employed to determine the statistical significance of the different treatments. Tukeyâs honestly significant difference (HSD) post hoc tests were then conducted to identify specific differences between treatment groups. Descriptive statistics were generated at a 95% confidence level, providing a comprehensive overview of the data distribution. The ANOVA table was utilized as the primary criterion for determining the statistical significance between parameters, with a significance threshold set at 95% confidence level (p < 0.05). The calculated F values from ANOVA allowed for the identification of dominant parameters influencing methane yield. This analysis facilitated the identification of significant factors driving variations in methane yield. Additionally, multiple linear regression was performed to evaluate the relationships between the physicochemical properties of the biochar and the specific methane yield. Correlation analysis further determined the strength and direction of these relationships. All statistical analyses were performed using R Studio. 52 Chapter 4: RESULTS AND DISCUSSION This chapter presents the results that are pertinent to the characteristics of biochar, substrate, and inocula, followed by the BMP test results and discussion in two sub-sections. The effects of adding biochar on AD performance in terms of the specific cumulative methane yields for Test Series 1 and Test Series 2 are presented in the first sub-section and the second sub-section, respectively. 4.1 Characteristics of biochar Results of biochar characterization in terms of its fundamental properties and traits are presented here. Parameters that include surface area, porosity, chemical composition, and pH are among the critical factors explored to elucidate the influence of biochar on the AD performance. This section lays the groundwork for a comprehensive analysis of biochar's impact on biogas production, setting the stage for the subsequent discussion on specific cumulative methane yields across different test series. 4.1.1 Pore properties Biochar pore properties, encompassing SSA, average pore diameter, and pore volume constitute crucial biochar quality parameters, particularly its adsorption capability. Table 4.1 shows the composition of the solid digestate that was used as feedstock for microwave-assisted catalytic pyrolysis in Test Series 1. Table 4.1: Composition of solid digestate used as feedstock to produce the biochar samples. Water (%) Hemicellulose (%) Cellulose (%) Lignin (%) Ash (%) Solid digestate 2.9 38.8 24.1 23.9 10.3 Tables 4.2 and 4.3 summarize the characteristics of biochar samples that were used in the batch AD experiments in Test Series 1 and Test Series 2, respectively. The different types of biochars were derived from pyrolysis of solid digestate and lignin using various catalysts and pyrolysis temperatures. 53 A positive relationship between temperature and SSA is evident in Test Series 1 (Table 4.2), as SSA increased with pyrolysis temperature for both catalysts. But contrary to this trend, Test Series 2 exhibited a 30% decrease in SSA for samples produced using 20% K3PO4, as catalyst and a slight increase of 5% for samples made using 10% K3PO4/10% clinoptilolite as catalyst. Although higher temperatures would typically result in increased SSA, some studies have reported a decrease in SSA at temperatures that exceed 600°C, often attributed to pore fusion. A few other studies have reported decrease in SSA at lower temperatures. Variations in the chemical composition within the feedstock or performance of the heating unit in the pyrolyzer may also influence the SSA. For Test Series 1 (Table 4.2), the biochar with the highest SSA (264 m2/g) is SD-20K-500, which is 29% greater than the lignin-derived biochar LN-20K-500 (205 m2/g). In contrast, for Test Series 2 (Table 4.3), the biochar with the highest SSA (387 m2/g) is LN-20K-500, which is 120% greater than the solid digestate-derived biochar SD-10K10C-500 (176 m2/g). Usually, lignin-rich substrates are expected to have a higher SSA and porosity, as the thermal degradation of lignin during pyrolysis leads to the formation of aromatic carbon structures and porous networks, enhancing the surface area (Saif et al., 2022). Table 4.2: Characteristics of biochars used in the batch experiments for Test Series 1. Biochar BET surface area (m2/g) Pore volume (cm3/g) Average pore diameter (nm) Feedstock Pyrolysis catalyst Pyrolysis temperature (°C) B1 SD 20K 400 37 0.082 8.8 B2 SD 20K 500 264 0.220 4.7 B3 SD 10K10C 400 149 0.150 4.0 B4 SD 10K10C 500 172 - - B5 LN 20K 500 205 - - SD: Solid digestate; LN: Lignin; 20K: 20% K3PO4; 10K10C: 10% K3PO4/10% clinoptilolite 54 The following discussion is based on the results from Test Series 2 and presented in Table 4.3. To begin with, the average pore diameter of the biochar samples ranged from 3.98 nm to 8.7 nm, with higher temperatures associated with smaller pore sizes. This finding aligns with previous research, suggesting that higher pyrolysis temperatures lead to biochar formation with smaller pore sizes due to heat-induced decomposition of organic matter and the formation of more compact and stable carbon structures (Tomczyk et al., 2020). Generally, pores with a radius over 25 nm are considered to facilitate transport, while pores smaller than 25 nm are considered to facilitate adsorbing (Zdravkov et al., 2007). Besides absorption, pores can provide a habitat for microorganism proliferating since the typical size of bacteria is 0.3 μm to 13 μm; thus, the higher the SSA, the more effective biochar is in interacting with the surrounding species (Codignole Luz, 2018). All biochars displayed a positive correlation between pyrolysis temperature and pore volume. Depending on the specific pyrolysis conditions, these newly formed pores can range from micropores to mesopores. When the temperature increased from 400 to 500 °C, the total pore volume increased by around twofold; however, the micropore volume decreased by 41% and 5% for the SD-20K biochar and the SD-10K10C biochar, respectively. Interestingly, the biochar LN-20K-500 had a similar total pore volume, but almost three times greater micropore volume as compared to the biochar SD-10K10C-500. This is because feedstocks having high volatile matter and lignin contents tend to develop a dense porous structure through the release of volatile compounds during pyrolysis, which is preserved by the thermal stability of lignin (Leng et al., 2021), resulting in higher porosity and SSA. The ash content of biochars derived from solid digestate ranged from 42.4% to 51.0%, with a general trend of increasing ash content with higher pyrolysis temperatures. As expected, the lignin-derived biochar has a lower ash content of 13.19% due to the feedstock composition. The pH of the lignin-derived biochar is a bit lower than that of the solid digestate-derived biochar, indicating that the latter is more alkaline, primarily related to their higher ash content. The pH of the biochar samples is within the typical alkaline pH range for biochars (7.1-10.5) (Lehmann et al., 2011). 55 In the literature, biochars made from sludge and manure exhibit a relatively low SSA, probably due to the large ash content that induces structural cracking or micropore blockage during pyrolysis (Tomczyk et al., 2020). Overall, the adsorption performance of biochar hinges on both pore structure and functional groups, as well as the characteristics of the adsorbates. Adsorbates are preferentially adsorbed in pores with sizes like their molecular dimensions, facilitating more significant contact between pores and adsorbates. Micropores and mesopores serve specific applications for adsorbates of different sizes. Table 4.3: Characteristics of biochars used in the batch experiments for Test Series 2. Biochar BET surface area (m2/g) pH Ash (wt%, d.b.) Pore volume (cm3/g) Micropore volume (cm3/g) Average pore size (nm) FS Pyrolysis Catalyst PT (°C) B1 SD 20K 400 160.94 10.10 42.42 0.145 0.0628 8.70 B2 SD 20K 500 113.09 10.15 43.64 0.183 0.0300 5.89 B3 SD 10K10C 400 167.50 10.24 45.82 0.144 0.0599 7.49 B4 SD 10K10C 500 175.72 10.19 50.98 0.228 0.0567 5.76 B5 LN 20K 500 386.50 9.86 13.19 0.231 0.1649 3.98 FS: Feedstock; PT: Pyrolysis temperature SD: Solid digestate; LN: Lignin; 20K: 20% K3PO4; 10K10C: 10% K3PO4/10% clinoptilolite The elemental analysis results are presented in Table 4.4. Several weight ratios that involve the (C, H, N, O) contents were calculated to determine the relationships between pyrolysis temperature and the biocharâs degree of aromaticity and hydrophobicity. During pyrolysis, the volatile matter in the feedstock is lost, resulting in biochar with the remaining minerals and carbon. The carbon content is a prominent property that indicates the graphitic level of biochar. The H/C ratio of biochar indicates the degree of original organic carbon preserved. Here, a decrease in the H/C ratio indicates increased aromatization and carbonization, and a drop in the O/C ratio and (O + N)/C ratio indicates the less hydrophilic nature of the biochar. A lower H/C ratio suggests a high degree of aromaticity and high resistance against chemical and microbial decomposition, while a lower O/C ratio indicates the evolution of the carbonization process. In general, H/C and O/C ratios tend to decrease with increasing pyrolysis temperatures (Brickler et al., 2021; Liu et al., 2020). 56 For the biochars used in Test Series 2, the H/C ratio varied from 0.012 to 0.023, with the lowest ratio occurring for biochar LN-20K-500. The O/C ratio ranged from 0.096 to 0.414. Higher O/C ratios have been attributed to hydrophilic surfaces with more O-containing functional groups (Bartoli et al., 2020). When the temperature increased from 400 to 500 °C, for the solid digestate-derived biochar SD-20K, the aromaticity index (H/C) and polarity index (O/C) increased, hence less complete carbonization was achieved at 500 °C. But for the biochar SD-10K10C, there is a slight decrease in the H/C ratio and a significant reduction in the O/C ratio. Brickler et al. (2021) has made similar observations; less predictable results were obtained for biochars produced by microwave-mediated pyrolysis instead of slow pyrolysis. The lack of gas circulation and the heating of water molecules in the microwave pyrolysis process may result in less dehydration, which could account for the differences in the trends of the ratios. Table 4.4: Summary of elemental analysis and weight ratio of biochar samples used in Test Series 2. Biochar Elemental analysis (wt %, d.b.) AI PI HP C H N S O# H/C (O+N)/C O/C B1 (SD-20K-400) 51.37 0.88 1.01 0.42 3.9 0.017 0.096 0.076 B2 (SD-20K-500) 40.88 0.94 0.75 0.41 13.38 0.023 0.35 0.33 B3 (SD-10K10C-400) 37.59 0.63 0.48 0.39 15.09 0.017 0.41 0.40 B4 (SD-10K10C-500) 44.18 0.64 0.68 0.37 3.15 0.014 0.087 0.071 B5 (LN-20K-500) 72.21 0.87 N.D. 2.19 11.54 0.012 0.34 0.160 AI: Aromaticity index; PI: Polarity index; HP: Hydrophilicity *d.b: dry basis; N.D.: not detected # O content was determined by difference: O (%) = 100%âash (%)âC (%)âH (%)âN (%)âS (%). 4.1.2 Functional groups The FTIR spectra of the various biochar samples are displayed in Figure 4.1. The results revealed the presence of distinct functional groups across all samples, with variations attributed to differences in feedstock type, pyrolysis temperature, and catalyst composition. 57 Broad peaks in the wavenumber 3450â3250 cmâ1 range were observed in all biochar samples, indicative of OH-stretching frequencies in phenolic and H-bonded hydroxyl groups. These hydroxyl groups can be derived from various components of the solid digestate, including lignin, cellulose, hemicellulose, and proteins. During pyrolysis, these components undergo thermal decomposition, generating hydroxyl-containing compounds. This broadband appeared more intense at 400°C pyrolysis temperature compared to those pyrolyzed at 500°C. This observation aligns with the typical expectation that higher pyrolysis temperatures result in increased degradation and carbonization of organic matter, leading to a reduction in the intensity of hydroxyl peaks. Additionally, bands around 3100 cmâ1 were attributed to C-H stretching in aromatic compounds, albeit with weaker signals than OH-stretching (Janu et al., 2021). The bands between 2850 and 2950 cmâ1 indicated C-H symmetric and asymmetric stretching of residual CHx groups commonly found in organic matter. These groups are remnants of the initial organic material present in the digestate, such as fatty acids and proteins, which partially decompose during pyrolysis. Notably, this peak exhibited higher intensity in Biochar 5 (LN-20K-500). Overlap peaks around 1700 cmâ1 were assigned to C=O stretching in aromatic aldehydes, ketones, and carboxylic acids. These carbonyl groups are likely products of the thermal decomposition of cellulose and hemicellulose, major components of the digestate. The presence of C=C stretching in olefins or aromatic rings suggests the formation of stable polyaromatic structures at higher temperatures, which are typical of biochars produced at 500°C. Peaks around 1580 cmâ1, attributed to the C=C stretching of aromatic moieties, showed medium intensity, with higher intensity observed in samples SD-20K-400 and LN-20K-500. These aromatic structures result from the extensive breakdown and reformation of organic molecules during pyrolysis, leading to the creation of stable, carbon-rich compounds. The characteristic bond of C-OH, indicative of phenolic components including polyphenols, was observed, which can be potentially oxidized to quinone. Quinone-containing compounds may act as electron shuttles, facilitating electron exchange via quinone-mediated interspecies electron transfer (Elnour, 2020). 58 The presence of hydrophobic functional groups promotes attraction between microorganisms and biochar (Schimmelpfennig & Glaser, 2012). Furthermore, various functional groups are formed on the biochar surface by the association of heteroatoms including S, P, N, O, and H with aromatic rings, resulting in varied surface chemistry of biochars. Consequently, electron-accepting (NO2, (C=O)H, (C=O)OH) or electron-donating (O(C=O)R, OR, NH2, OH) functional groups can be formed (Amonette & Joseph, 2009). A high abundance of oxygen-containing functional groups is considered favorable for electron transfer between microorganisms (Xu et al., 2020). This can be explained by the fact that oxygen functional groups promote interaction between biochar and microbial groups, thereby increasing the likelihood of electron transfer between the attached microorganisms on the biochar surface (LÃ¼ et al., 2019). In this study, biochars SD-20K-500 and SD-10K10C-500 exhibited very weak signals, possibly due to lower concentrations of the surface groups. This observation is consistent with the formation of varied functional groups on the biochar surface as influenced by pyrolysis conditions. Further investigation using FTIR-ATR, which probes only the sample's top few micrometers, may provide deeper insights into these biochars' surface chemistry, particularly regarding surface interactions and functional group concentrations (Margenot et al., 2017). 59 Figure 4.1: FTIR spectra of biochar samples produced from MACP of solid digestate, or lignin mixed with different catalysts and temperatures with a 30 min microwave residence time. 60 4.2 Properties of substrates and inocula Table 4.5 presents an overview of the critical properties of the substrates and inocula utilized in the batch experiments conducted for the BMP analysis. The substratesâ total carbon (C) content ranged from 30.8% to 47.2%, with chicken manure exhibiting the lowest carbon content and food waste displaying the highest. Similarly, the total nitrogen (N) content varied across the substrates, ranging from 0.35% to 3.16%. Carbon and nitrogen content differences can significantly influence the methane production potential and nutrient balance during AD. Table 4.5: Characteristics of inocula and substrates used in the batch experiments. Substrate TS (wt%) VS (wt%) Carbon (%) TKN (%) C/N Ratio Inoculum 1 3.92±0.012 2.82±0.030 - - - Inoculum 2 3.05±0.295 2.17±0.485 - - - Dairy Manure 67.3±0.172 35.6±0.284 45.4 1.04 43.7 Hog Manure 2.94±0.092 1.99±0.106 44.2 1.55 28.5 Chicken Manure 1.77±0.016 0.79±0.097 30.8 1.97 15.6 Food Waste 27.5±1.094 26.2±1.047 47.2 3.16 15.0 Wheat Straw 94.2±0.054 90.6±0.120 44.0 0.35 125.7 Cellulose 95.4± 1.27 95.0±1.01 - - - The C/N ratio ranged from 15.0 (food waste) to 125.7 (wheat straw). The values of the C/N ratio for the co-digestion treatments (food waste mixed with wheat straw) are shown in Table 4.6. Table 4.6: C/N ratio of the co-digestion substrates used in the batch experiments. Substrates Mass ratio (based in VS) C/N ratio Dairy: hog: chicken manure (manure mixture) 50:25:25 29.6 Manure mixture: wheat straw 95:5 30.8 Manure mixture: wheat straw 80:20 35.3 Food waste: wheat straw 90:10 16.3 Food waste: wheat straw 80:20 18 61 4.3 BMP test results In this section, we delve into the outcomes of the BMP tests, focusing on Test Series 1 and Test Series 2, each comprising distinct substrate compositions. Within these test series, a comprehensive analysis is conducted on the biogas production potential of various substrates and additives, including different types of biochar as additives and two mixing ratios for wheat straw co-digestion. Through experimentation and comparative analysis, we aim to elucidate the performance of each treatment combination in terms of cumulative methane yields. These findings provide valuable insights into the efficiency and effectiveness of anaerobic digestion processes and offer critical information for optimizing biogas production systems. 4.3.1 Test Series 1 The anaerobic digestion experiments were conducted using a mixture of chicken, hog, and dairy manure as the substrate, with varying dosages of different biochars. The specific methane yield (SMY) and change in SMY compared to the control for each treatment are summarized in Table 4.7. Also, Figure 4.2 shows a bar graph with the treatments' specific methane yield. Overall, the highest methane yield was produced with biochar LG-20K-500 at a dosage of 0.6 g/g TS with 307 mL/g VS. Figure 4.2: Comparison of specific methane yield among thirteen treatment variations using a manure mixture as the substrate. 62 The mean FOS/TAC ratio measured at the end of each batch test ranged between 0.10-0.16 (Figure 4.3). These values imply a low level of biomass feeding and a complete consumption of the VFAs generated during the AD process. In practice, the biogas system is stable when FOS/TAC ratio lies between 0.3-0.4; therefore, the amount of input substrate may be increased. Table 4.7: Biochemical Methane Potential (BMP) results for Test Series 1 Treatment Biochar SMY (mL/g VS) Change SMY (%) Feedstock PC PT (°C) Dosage (g/gTS) 1 (Control) - - - - 200 - 2 SD 20K 400 0.1 220 10 3 SD 20K 400 0.6 300 50 4 SD 20K 500 0.1 217 8.5 5 SD 20K 500 0.6 219 9.5 6 SD 10K10C 400 0.1 204 2 7 SD 10K10C 400 0.6 238 19 8 SD 10K10C 500 0.1 204 2 9 SD 10K10C 500 0.6 277 38.5 10 LN 20K 500 0.1 266 33 11 LN 20K 500 0.6 307 53.5 Treatment Mass ratio (manure:wheat straw based on VS) 12 95:5 221 10.5 13 80:20 300 50 FS: Feedstock; PC: Pyrolysis catalyst; PT: Pyrolysis temperature SD: Solid digestate; LN: Lignin; 20K: 20% K3PO4; 10K10C: 10% K3PO4/10% clinoptilolite 63 Figure 4.3: Comparison of FOS/TAC ratio among thirteen treatment variations using a manure mixture as the substrate. Influence of biochar dosage For biochars derived from the pyrolysis of solid digestate, a lower dosage of 0.1 g/g of TS had marginal improvement in the specific methane yield (SMY), but biochar derived from lignin (LN-20K-500) displayed a remarkable increase in SMY by 33% to 266 mL/g VS. Increasing the dosage to 0.6 g/g TS led to notable increase in SMY by 9.0-53.5% across all biochar types compared to the control. Hence, for SMY, adding biochar to manure mixture as the substrate exhibited a notable dosage-dependent relationship. The influence of the pyrolysis feedstock is also noteworthy, as the biochar derived from lignin is associated with the highest methane yield at both dosage levels. Lignin-rich feedstocks often have more complex and porous structures than feedstocks with a higher cellulose content. During pyrolysis, lignin undergoes thermal decomposition; this can contribute to forming porous structures in biochar with higher SSA and porosity, facilitating the attachment and growth of methanogenic bacteria in the anaerobic digester (Chiappero et al., 2022). As previously shown in Table 4.1, the solid digestate that was used as pyrolysis feedstock to make biochar samples #1â4 had 23.9% lignin content. Lignin-rich feedstocks could produce biochar that is more suitable for enhancing AD methane yield and stability. 64 Influence of pyrolysis temperature and catalyst With a higher biochar dosage of 0.6 g/g TS, increasing the pyrolysis temperature from 400°C to 500°C resulted in a decrease in SMY by 27% when using 20K as the pyrolysis catalyst, yet an increase in SMY by 14% when using 10K10C as the catalyst. The choice of catalyst during MACP could also significantly influence methane production. In this case, using 10K10C instead of 20K as the catalyst led to a decrease in SMY by 21% at 400°C, but increase in SMY by 26% at 500°C. One possible reason for the opposite trends could be changes in biochar composition or pore structure induced by mixing potassium phosphate with clinoptilolite to make the catalyst. Apparently, an intricate relationship could exist between pyrolysis temperature and pyrolysis catalyst. Either there may be synergetic effect between the two parameters, or conversely, alteration of biocharâs surface functional groups might occur under elevated pyrolysis temperatures. These results underscore the importance of considering both catalyst type and pyrolysis temperature in biochar production for anaerobic digestion applications. Influence of biochar BET specific surface area No direct correlation could be established between biochar's BET specific surface area (SSA) and SMY, countering conventional expectations regarding the influence of SSA on methane production. While biochars with larger SSA might be expected to exhibit greater methane yields due to increased surface interaction with microbial consortia, specific instances contradicted this correlation. For example, biochar SD-20K-400 had the smallest SSA (37 m2/g), but SMY was higher (300 mL/g VS), as compared to biochar SD-20K-500, which had the largest SSA (264 m2/g) but a lower SMY (220 m2/g VS). These findings suggest that factors beyond surface area, such as pore structure, surface functional groups, and catalyst-induced alterations, may play a major role that could significantly influence biochar's effectiveness in enhancing AD performance. Furthermore, Figure 4.4 shows that there is a very weak positive correlation between the total surface area per volume of the reactor (or, dosage-adjusted surface area, calculated as SSA [m2/g] * biochar dosage [g/L]) and SMY, with a correlation coefficient of 0.148. 65 Figure 4.4: Total surface area per volume of digestion vs specific methane yield for treatments 2-9 of Test Series 1. Regarding cumulative methane production, there are two distinctive trends (Fig. 4.5), both without a noticeable lag phase and a fast initial phase of gas production. The microbial community within the inoculum may have been well-acclimated to anaerobic conditions and capable of expediting the hydrolysis and fermentation stages of AD through efficient substrate utilization expedite the hydrolysis and fermentation stages. 66 Figure 4.5: Comparison of cumulative methane yield at 20°C and 1 atm from BMP assays across 13 treatments. Labels are ordered by methane yield in descending order. 67 The process parameters of temperature, pH, mixing, and substrate-to-inoculum ratio may also have been well-controlled, creating favorable conditions for rapid microbial activity. Treatments 3, 9, and 11 with biochar addition had lower methane production rates but achieved higher SMY after 30 days. Somewhat different substrate-to-inoculum ratios were used in these treatments, and the mixing ratio of the three types of manure varied slightly due to the composition of the available hog manure at the time. Both factors could potentially impact the methane production rate. This observation aligns with the findings of the meta-analysis by Chiappero et al. (2022), which involved 76 BMP tests in batch mode, and revealed a significant enhancement in methane production rates, albeit accompanied by a shorter lag phase for biomass adaptation. Comparison of biochar-enhanced AD and co-digestion with wheat straw As mentioned in Chapter 3, the primary purpose of mixing wheat straw with manure (at less than 25 wt% of wheat straw) was to achieve a suitable C/N ratio for AD. This would reduce the risk of ammonia nitrogen (NH3-N) inhibition and stabilize the digestion process through increased buffering capacity. Besides, co-digestion with wheat straw introduces a diverse range of fermentable organic matter, potentially enhancing microbial activity and promoting higher methane yields. By comparison, while potentially altering microbial activity and nutrients retention, biochar addition does not directly supply fermentable substrates, potentially exerting different effects on microbial communities and pH buffering. When examining the BMP test results, it becomes evident that biochar addition and wheat straw co-digestion contributed to enhanced methane yields. Experimental treatments that involve biochar addition (T2 to T11) show varying degrees of improvement in SMY by 2.0-53.5% compared to the control; whereas experimental treatments that incorporate wheat straw co-digestion (T12 and T13) exhibit SMY enhancements by 10.5% and 50%, respectively. Kinetic parameters Table 4.8 lists the kinetic parameters (potential maximum methane yield Mo, hydrolysis rate KH, maximum daily methane production rate Rm, and lag phase duration ð) that were obtained by nonlinear curve-fitting using the first-order model and the Modified Gompertz Model. Applying both models to the mesophilic BMP test data has brought about an accurate estimation with a coefficient of determination (R2) of 0.98-1.00. A plot of the kinetic constants, KH and Rm, is also 68 shown in Fig. 4.6. Moreover, for visual confirmation, experimental and predicted specific cumulative methane yields (with 95% confidence level) using the Modified Gompertz Model and First-Order Model are illustrated for all three scenarios (Appendix A and B). It should be noted that the treatments highlighted in blue occurred in the same run, so the comparisons made are within the same control (R1 or R3). Table 4.8: Kinetic parameters obtained from curve fitting of experimental BMP data to the first-order model and the Modified Gompertz Model. Treatment First-Order Model Modified Gompertz Model M0 KH R2 M0 Rm λ R2 T1 (R1, R2) 192.63 0.29 1.00 190.29 33.57 -0.22 0.99 T1 (R3) 203.8 0.082 1.00 183.7 11.13 -0.4679 0.9945 T2 243.41 0.086 1.00 220.01 14.38 -0.18 1.00 T3 342.55 0.083 0.99 305.44 20.46 0.22 1.00 T4 215.31 0.28 1.00 212.53 35.69 -0.22 0.99 T5 216.68 0.31 1.00 214.02 41.10 -0.07 0.99 T6 197.54 0.32 1.00 195.17 37.80 -0.17 0.99 T7 235.73 0.29 1.00 232.86 43.05 -0.12 0.99 T8 230.81 0.078 1.00 206.76 11.97 -0.59 0.99 T9 314.11 0.085 0.99 281.73 19.28 0.22 1.00 T10 265.87 0.25 0.98 258.12 48.92 0.39 0.99 T11 352.96 0.078 0.99 312.87 19.54 -0.06 1.00 T12 220.90 0.26 0.98 214.30 43.15 0.52 0.99 T13 302.60 0.24 0.99 297.37 43.87 -0.15 0.99 * The data represents the arithmetic mean of the duplicates for each treatment ð0: potential maximum methane yield (ml g-1 substrate VS) ð¾ð»: hydrolysis rate (d-1) ð ð: maximum daily methane production rate (ml g-1 substrate VS d-1) ð: lag phase duration (d) 69 Figure 4.6: The kinetic constants of the first-order model and the modified Gompertz model, data are means of duplicates. These results demonstrate that the addition of powdered biochars to mesophilic BMP assays did not lead to a significant rise in the hydrolysis rate under all treatment conditions (biochars or co-digestion with wheat straw) vs. the controls. In seven of the treatments, the change in the hydrolysis rate (Table 4.9) ranged from -18.8% (T13) to 8.8% (T6). Nevertheless, the predicted methane production rates using the Modified Gompertz Model increase for all treatment conditions, ranging from 6.3 to 83.8%; and for each type of biochar, higher methane production rate is achieved with higher biochar dosage of 0.6 g/g TS substrate. 70 Table 4.9: Change in hydrolysis rate and methane production rate vs the control. Change (%) KH Rm T2 5.13 29.2 T3 0.73 83.8 T4 -4.38 6.3 T5 5.31 22.4 T6 8.79 12.6 T7 -1.03 28.2 T8 -4.76 7.6 T9 4.03 73.2 T10 -14.1 45.7 T11 -4.40 75.6 T12 -10.7 28.5 T13 -18.8 30.7 4.3.2 Test Series 2 Experiments were conducted using a mixture of synthetic food waste and wheat straw as the substrate for co-digestion. The biogas yield, SMY, methane content, change in SMY compared to the control, and relative standard deviation (RSD) of SMY for each treatment are summarized in Table 4.10. Also, Figure 4.7 shows all the treatments' specific methane yield in the form of a bar graph. The highest biogas yield (906 mL/g VS) and methane yield (525 mL/g VS) were produced with the addition of biochar LG-20K-500 at a dosage of 0.6 g/g TS. Although the biochar derived from solid digestate âSD-10K10C-400â and âSD-20K-500â had a slightly lower SMY. One-way ANOVA and Tukey's honest significant difference (HSD) statistical tests were carried out to compare the treatments vs. the control, and the results are presented in Table 4.11. Overall, the methane content associated with the biochar and co-digestion treatments ranged from 49â61%, as compared to methane content of 48% for the control. Moreover, all treatments with a dosage of 0.1 g/g TS of solid digestate-derived biochar resulted in a decrease in biogas yield and a slight increase in methane content and yield. As for the methane content, all biochars at the high dosage except for the lignin one increased the methane content by at least 10% at the high dosage, which in turn means that the biogas yield increase was not as high as the methane yield increase, which was from 35â52% at the high dosage and 18.6% at the 10% WS. 71 As for the wheat straw, only the treatment with the 10% mixing ratio displayed a statistically significant difference, although this treatment had a lower C/N ratio, which could again represent that we shouldn't base the mixing ratios solely on adjusting the nutrient ratio. Compared to these co-digestion treatments, the five biochars yielded a statistically significant increase at the higher dosage (0.6 g/g TS) for 10% and 20% wheat straw. The Tukey's HSD results for the treatments that had a statistically significant difference with the two mixing ratios of wheat straw are shown in Table 4.12. Figure 4.7: Comparison of specific methane yield among thirteen treatment variations using synthetic food waste as the substrate. 72 Table 4.10: Biochemical Methane Potential (BMP) results for Test Series 2 Biochar Biogas yield (mL/g VS) SMY (mL/g VS) RSD of SMY (%) Methane content (%) Change biogas (%) Change SMY (%) Treatment Feedstock Pyrolysis catalyst Pyrolysis temperature (°C) Dosage (g/gTS) T1 (Control) 714 ± 24 345 ± 17 4.82 48 - - T2 (Positive control) 715 ± 21 372 ± 12 3.23 52 - - T3 SD 20K 400 0.1 672 ± 9 356 ± 4 1.10 49 -5.9 3.2 T4 SD 20K 400 0.6 776 ± 34 475* ± 44 9.24 61 8.7 35.7 T5 SD 20K 500 0.1 700 ± 23 358 ± 12 3.43 51 -2.0 3.8 T6 SD 20K 500 0.6 809 ± 49 492* ± 27 5.45 61 13.3 42.6 T7 SD 10K10C 400 0.1 692 ± 8 352 ±15 1.20 53 -5.9 3.5 T8 SD 10K10C 400 0.6 854 ± 24 501* ± 7 1.34 61 19.6 45.2 T9 SD 10K10C 500 0.1 709 ± 8 351 ± 15 4.28 50 -0.7 1.7 T10 SD 10K10C 500 0.6 797 ± 4 480* ± 11 2.32 60 11.6 39.1 T11 LN 20K 500 0.1 759 ± 56 403* ± 0.06 0.02 53 6.3 16.8 T12 LN 20K 500 0.6 906 ± 85 525* ± 36 6.80 56 26.9 52.2 Treatment Mass ratio (food waste:wheat straw based on VS) T13 90:10 705 ± 19 409* ± 12 2.83 58 -1.3 18.6 T14 80:20 672 ± 27 389 ± 18 4.66 58 -5.9 12.8 * Statistically different than the control treatment at a 95% confidence level. SD: Solid digestate; LN: Lignin; 20K: 20% K3PO4; 10K10C: 10% K3PO4/10% clinoptilolite. 73 Table 4.11: Tukey's Honest Significant Difference (HSD) test results for BMP treatments vs. control (control-BMP treatment). Treatment Difference Lower limit Upper limit p adj T3 13.1 -39.0 65.1 1.00 T4 130.3* 78.2 182.3 0.00 T5 6.4 -45.7 58.4 1.00 T6 147.3* 95.3 199.4 0.00 T7 11.5 -40.5 63.5 1.00 T8 156.0* 103.9 208.0 0.00 T9 8.6 -43.5 60.6 1.00 T10 132.5* 80.5 184.5 0.00 T11 56.5* 4.4 108.5 0.02 T12 180.1* 128.0 232.1 0.00 T13 63.8* 11.8 115.8 0.01 T14 44.9 -7.2 96.9 0.14 * Statistically different than the control treatment at a 95% confidence level. Influence of biochar dosage For biochars derived from the pyrolysis of solid digestate, a lower dosage of 0.1 g/g TS did not enhance SMY methane yield, but the biochar derived from pyrolysis of lignin (LN-20K-500) led to a 16.8% increase in SMY to 403 mL/g VS, exceeding all other treatments at this dosage level. Increasing the dosage to 0.6 g/g TS resulted in a notable increase in SMY by 36-52% compared to the control. Three-way ANOVA revealed significant main impact due to biochar dosage, while no significant interaction effect was found between factors. Moreover, with higher dosage of 0.6 g/g TS, all biochar treatments performed better than the food waste-wheat straw co-digestion treatments (without biochar addition). Influence of pyrolysis temperature and catalyst With a higher biochar dosage of 0.6 g/g TS, increasing the pyrolysis temperature from 400°C to 500°C had no significant effect on biogas yield and methane yield (SMY) for both types of 74 catalysts, 20K and 10K10C. Likewise, for both pyrolysis temperatures of 400°C to 500°C, using 10K10C rather than 20K as the catalyst had negligible effect on biogas yield and SMY. Table 4.12: Tukey's Honest Significant Difference (HSD) test results for BMP treatments vs. wheat straw 10 wt% (T13) and 20 wt% (T14) with a statistical difference at a 95% confidence level (BMP treatment- T13/T14). 10 wt% wheat straw (T13) 20 wt% wheat straw (T14) Treatment diff lwr upr p adj diff lwr upr p adj T4 66.5 8.3 124.6 0.02 85.4 27.2 143.6 0.001 T6 83.5 25.4 141.7 0.001 102.5 44.3 160.6 0.00 T8 92.1 34.0 150.3 0.00 111.1 52.9 169.3 0.00 T10 68.7 10.5 126.9 0.01 87.6 29.5 145.8 0.001 T12 116.2 174.4 58.1 0.00 135.2 193.4 77.0 0.00 Diff: difference; lwr: lower limit; upr: upper limit Subsequently, a three-way ANOVA was also performed to determine the impact of the three factors (biochar dosage, pyrolysis temperature, and pyrolysis catalyst) on the biochars for treatments T3âT10 (with the addition of solid digestate-derived biochar). The results as presented in Table 4.13 reveal that variations in biochar dosage level constitute significant impact on SMY. Yet, no significant impacts are observed for pyrolysis temperature or pyrolysis catalyst. Furthermore, no significant interaction impacts are found between biochar dosage and either pyrolysis temperature or pyrolysis catalyst, and between pyrolysis temperature and pyrolysis catalyst. This means, variations in biochar dosage largely dominate the observed variance in methane yield, while the contribution of pyrolysis temperature and catalyst to the outcomes in this study may be limited. The coefficient of correlation was calculated for the parameters BET SSA, micropore volume, polarity index, and aromaticity index at both low and high biochar dosages. The aim is to investigate the correlation between the biochar physicochemical properties and the methane yield in the above-mentioned experimental treatments. The plots and coefficients are presented in Table 4.14. 75 Table 4.13: Three-way ANOVA results for food waste anaerobic digestion SMY with different biochar dosages, catalysts, and temperatures. Factor Df Sum Sq Mean Sq F value Pr(>F) Dosage 1 103959 103959 263.80 2.3E-11* Temperature 1 96 96 0.24 0.63 Catalyst 1 50 50 0.13 0.73 Dosage:Temperature 1 4 4 0.01 0.92 Dosage:Catalyst 1 39 39 0.01 0.76 Temperature:Catalyst 1 506 506 1.29 0.27 Dosage:Temperature:Catalyst 1 736 736 1.87 0.19 Residuals 16 6305 394 * Statistically significant factor at a 95% confidence level. Table 4.14: Correlation coefficients between biochar physicochemical properties and anaerobic digestion methane yields for treatments T2-T9. Coefficient of correlation Dosage Aromaticity index Polarity index BET SSA Micropore volume 0.6 g/g TS 0.196 0.432 -0.134 -0.140 0.1 g/g TS -0.173 -0.079 0.215 0.297 The parameters BET SSA, micropore volume, polarity index, and aromaticity index were then adjusted by incorporating the biochar dosage applied in each treatment. The aim is to standardize the variables that are affected by different biochar dosages across treatments. A new variable was created by multiplying these parameters by the biochar dosage, allowing for a comparative study of the relative impact of the biochar's physicochemical properties on methane yield while accounting for differences in biochar dosage. These variables are labeled as dosage-adjusted âSSA, micropore volume, polarity index, and aromaticity indexâ, as displayed in Table 4.15. It shall be noted that the dosage-adjusted SSA is equal to the total surface area of biochar per unit volume of the substrate, in [m2/L]. 76 Table 4.15: Correlation coefficients between dosage-adjusted biochar physicochemical properties and anaerobic digestion methane yields for treatments 3-10. Dosage-adjusted parameters Coefficient of correlation, r Specific surface area 0.923 Micropore volume 0.874 Aromaticity 0.934 Polarity intex 0.737 The dosage-adjusted SSA and micropore volume have a strong positive correlation with methane yield (SMY), the correlation coefficient r being 0.923 and 0.874, respectively. Based on separate calculations, the dosage-adjusted SSA and micropore volume also hold a very strong positive correlation (r = 0.983). This is reasonable, since during pyrolysis the feedstock undergoes thermal degradation in the absence of oxygen, forming biochar with a complex pore structure. The development of micropores (< 2 nm) occurs as volatile components are driven off, leaving behind a porous carbon matrix. This process contributes to an increase in both the biochar SSA and micropore volume. Correlation between the dosage-adjusted aromaticity index and SMY has r = 0.934, whereas correlation between the dosage-adjusted polarity index and SMY has r = 0.737, suggesting that both the surface groups and pore properties impact the methane yield. Aromaticity index is associated with the aromatic carbon structures within the biochar, which are characterized by conjugated double bonds in a ring-like arrangement, imparting stability and degradation resistance. The polarity index is related to the surface functional groups, and it indicates the polarity, hydrophilicity, or hydrophobicity of the biochar surface, reflecting its affinity for polar or nonpolar molecules. Aromatic compounds typically possess nonpolar characteristics due to the delocalization of Ï-electrons within the ring structure, resulting in minimal interaction with polar solvents or molecules. Consequently, biochar with higher aromaticity index is expected to have lower polarity index, indicating a predominantly hydrophobic surface. Separate calculations also revealed that there is a mild positive correlation between both indices, with a coefficient of 0.579. 77 Furthermore, for biochar treatments (T2-T9), a multiple linear regression analysis was conducted that involves three of the dosage-adjusted parameters, with an aim to have a quantitative assessment of the relative impact of different biochar parameters on SMY. The analysis has generated an empirical equation, with F (3,20) = 108.2, p < 0.05, and R2 = 0.942 indicating a strong fit, and represented by: ððð = 328.77 + 0.01086 â ððð ððð´ + 1.295 â ððð ðð¼ + 73.68 â ððð ð´ð¼ (4.1) SMY represents the specific methane yield (mL/g substrate VS); adj SSA is the dosage-adjusted BET specific surface area; adj PI is the dosage-adjusted polarity index; and adj AI is the dosage-adjusted aromaticity index. Further examination of the individual independent variables indicated that the aromaticity index and the specific surface area are statistically significant (t=2.946, p < 0.05 and t=4.918, p <0.05, respectively) (Table 4.16); a unit increase in the dosage-adjusted aromaticity index corresponds to an increase in methane yield by 73.68 mL/g VS. Moreover, a unit increase in the dosage-adjusted BET SSA corresponds to an increase in methane yield by 0.01086 mL/g VS. In contrast, the dosage-adjusted polarity index with (t = 1.457, p > 0.10) are not statistically significant. Thus, the results suggest that an increase in aromaticity (or decrease in aromaticity index) in the biochar led to a decrease in methane yield. Additionally, an increase in the SSA led to an increase in methane yield. After scaling and centering the predictor variables, the dosage-adjusted SSA exhibited the strongest positive impact on the SMY, with a coefficient of 35.486 and a highly significant p-value (< 0.001), indicating a robust statistical relationship. The dosage-adjusted AI also significantly affects the SMY, with a coefficient of 28.029 and a p-value of 0.00799, suggesting a substantial, albeit smaller, positive influence compared to dosage-adjusted SSA. 78 Table 4. 16: Results of multiple linear regression analysis of biochar properties and specific methane yield. Dosage-adjusted parameter Estimate Std. Error T value Pr(>|t|) Specific surface area 0.065 0.042 1.56 0.14 Micropore volume -35.62 98.88 -0.36 0.72 Polarity index 5.89 4.17 1.41 0.18 Aromaticity index 327.22 129.59 2.53 0.021 * * Statistically significant predictor at a 95% confidence level. 79 Chapter 5: CONCLUSION 5.1 Summary of Results This study aimed to explore the potential of solid digestate-derived biochar as an additive for enhancing the stability and performance of anaerobic digestion (AD) systems. The influence of pyrolysis process conditions on the physicochemical properties of biochar and hence the AD process was investigated. Two major series of tests were conducted. Test Series 1 used a mixture of chicken, hog, and dairy manure as the substrate, and Test Series 2 used synthetic food waste as the substrate for AD. For Test Series 1, results indicated that a lower biochar dosage of 0.1 g/g of TS had marginal improvement in the specific methane yield (SMY), but increasing the dosage to 0.6 g/g TS led to increase in SMY by 9.0-53.5% across all biochar types compared to the control. Hence, adding biochar to manure mixture as the substrate exhibited a notable dosage-dependent relationship. However, the influence of pyrolysis temperature (400 oC vs. 500 oC) and pyrolysis catalyst (20 wt% K3PO4 vs. 10 wt% K3PO4/10 wt% clinoptilolite) on SMY exhibited opposite trends. Furthermore, no direct correlation could be established between the specific surface area (SSA) of biochar and SMY, suggesting that other factors such as pore structure and surface functional groups may influence biochar's effectiveness in enhancing AD performance. As an alternative approach to adding biochar, co-digestion of manure mixture with wheat straw (mixing ratio 80:20 by mass) to obtain a more favorable C/N ratio for AD also improved SMY by 50% compared to the control. Curve fitting with the Modified Gompertz Model to the data indicates co-digestion had methane production rates that exceed that of the control by about 30%. For Test Series 2, Adding biochar to food waste also exhibited a notable dosage-dependent relationship with SMY. At lower dosage (0.1 g/g TS), the incremental improvement in methane production was marginal across most biochar treatments compared to the control. All biochars at higher dosage of 0.6 g/g TS increased methane content by at least 10%, with significant improvements in methane yield ranging from 35% to 52% compared to the control. A three-way ANOVA showed that variations in biochar dosage significantly influenced methane yield, while pyrolysis temperature and catalyst had no statistically significant impact. Moreover, multiple linear regression analysis revealed that the dosage-adjusted specific surface area and aromaticity 80 index had the most significant impact on methane yield. A unit increase in the dosage-adjusted aromaticity index and the SSA corresponded to a 73.68 mL/g VS and 0.01086 mL/g VS increase in methane yield respectively, highlighting the importance of biochar properties in enhancing AD performance. Although both properties displayed significant impact the dosage-adjusted SSA exhibited the strongest positive impact on the SMY. This research underscores the potential of solid digestate-derived biochar as a valuable additive in AD systems. The findings emphasize the importance of optimizing biochar dosage, feedstock, pyrolysis temperature, and pyrolysis catalyst to maximize methane production and process stability. Tailored biochar properties and production conditions can significantly enhance methane yield and stability, offering promising opportunities for improving the conversion efficiency and sustainability of anaerobic digestion systems. 5.2 Recommendations for Future Work Despite the insights gained, the following limitations were encountered during the thesis research. Addressing these limitations and implementing the recommended strategies will contribute to a deeper understanding of the impact of biochar addition on anaerobic digestion processes. Insufficient biochar availability and substrate variations: Test Series 1 faced some challenges due to limited availability of biochar from the same batch, leading to incomplete characterization and reliance on duplicate runs. Moreover, variations in the substrate composition, particularly the hog manure moisture content, may have influenced kinetic rates and methane yields. Future studies shall ensure consistent substrate composition and sufficient biochar availability to accurately assess the impact of biochar properties. Limited pyrolysis temperature and biochar dosage variations: This study examined four types of biochar derived from solid digestate and applied to AD process at two dosage levels. To comprehensively evaluate the effect of solid digestate-derived biochar, future research shall explore dosage levels in between the lower dosage (0.1 g/g TS) and higher dosage (0.6 g/g TS) that were used in this study, with an aim to delineate an optimal biochar dosage for different AD 81 substrates. A broader range of experimental conditions can also provide a more nuanced understanding of the effects of biochar on the AD process. Experimental planning and design for future studies can take into consideration the desire to gain further understanding of how each biochar property (BET SSA, pore volume, surface functional groups, alkalinity) may contribute to improved biogas/biomethane production, so that correlations can be developed. Considering co-digestion of organic waste with wheat straw vs. addition of biochar, both strategies offer promising opportunities to boost biogas production in AD systems, with their effects varying based on substrate composition, biochar properties, and process conditions, underscoring the need for tailored approaches to optimize biogas production efficiency in future studies. Low FOS/TAC ratio at the end of both BMP Test series 1 and 2: It is desirable to investigate the AD performance under higher organic loadings. Also, implementing more frequent measurement could provide insights into the dynamics of AD. By optimizing process parameters and monitoring key indicators, future studies can delineate the conditions that are most favorable for system performance and stability. This study's findings indicate that biochar's most impactful property on anaerobic digestion (AD) performance may be the dosage-adjusted specific surface area (SSA), the total surface area per substrate volume. This suggests that biochar's effectiveness in enhancing AD performance is closely linked to its surface characteristics, which facilitate microbial interactions and nutrient exchange. Additionally, the aromaticity index of biochar also exhibited a significant impact on methane yield, further underscoring the importance of biochar's surface properties. These results suggest that optimizing the specific surface area and aromaticity of biochar, alongside careful consideration of dosage, could be key to maximizing methane production and improving the overall efficiency of AD systems. 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Thesis/Dissertation
eng
Chemical and Biological Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lau, Anthony
Anaerobic digestion of organic waste mixtures with the addition of biochar
Text