Viewing Publications and Proposals, Ms

Development of a Biomass-to-Methanol Process Integrating Solid State Anaerobic

Digestion and Biological Conversion of Biogas to Methanol

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Johnathon Patrick Sheets, M.S.

Graduate Program in Food, Agricultural and Biological Engineering

The Ohio State University

2017

Dissertation Committee:

Dr. Jay Martin, Advisor

Dr. Gönül Kaletunç

Dr. Ajay Shah

Dr. Zhongtang Yu

Johnathon P. Sheets

2017

Abstract

Solid-state anaerobic digestion (SS-AD) can be used to convert abundant, low moisture feedstocks, such as switchgrass, to methane (CH4)-rich biogas. However, SS-AD of energy crops is a relatively nascent technology that requires further optimization to increase biogas yields. Additionally, biogas from anaerobic digestion is a gas under ambient conditions, and impurities such as carbon dioxide and hydrogen sulfide need to be removed before it can be upgraded to purified biogas for pipeline injection or to transportation fuels, such as compressed natural gas. These issues have sparked interest in technologies that can convert biogas to methanol, a liquid chemical that can be used directly as a fuel or can be upgraded to a variety of other products. However, the thermochemical process for methanol production has high capital costs, operates at high temperatures and pressures, and requires a

CH4 feedstock with few impurities. In contrast, the biological process for conversion of biogas to methanol may not require biogas purification and can operate under low temperatures and pressures, reducing capital costs and energy demands. Integration of SS-

AD with biological conversion of biogas to methanol has great potential as an environmentally friendly technology to produce methanol from renewable feedstocks.

For this research, there were five inter-related projects: 1) investigation of impacts of environmental conditions on SS-AD of switchgrass for biogas production; 2) isolation of methanotrophs (CH4 oxidizing bacteria) from SS-AD that can directly convert biogas to methanol; 3) development of a trickle-bed bioreactor (TBR) to improve gas-to-liquid mass

ii transport and produce methanol from biogas; 4) mathematical modeling of TBRs to identify the impacts of key operating parameters on biological conversion of biogas to methanol; and

5) techno-economic analysis to compare the economic feasibility of biological conversion of biogas to methanol to other biogas upgrading technologies.

The first project showed that limited air exposure had a minimal effect on SS-AD performance, indicating that this popular method to reduce hydrogen sulfide levels in biogas could be used in scaled-up reactors. Thermophilic temperature (55°C) enhanced biodegradation of cellulosic materials and improved biogas yields (102–145 L CH4 kg

-1 -1 VSadded ) compared to mesophilic (37°C) temperature (88–113 L CH4 kg VSadded ). Net energy analysis of a theoretical scaled up “garage-type” SS-AD reactor (operating in Ohio) suggested that positive net energy could be obtained using either thermophilic or mesophilic conditions at elevated total solids contents (≥ 20% TS).

A new methanotroph strain, called Methylocaldum sp. 14B, was isolated from the digestate of a mesophilic SS-AD reactor in the second project. Methylocaldum sp. 14B had comparable physiological characteristics and had similar 16S rRNA gene sequence identity to strains of the genus Methylocaldum. This methanotroph had comparable growth rates (0.06 h-1) in nitrate mineral salts (NMS) medium using either biogas from a commercial anaerobic digestion facility (70% CH4, 30% CO2, <50 ppm H2S) or purified CH4 (99% CH4) as the primary carbon sources and air as the oxygen source (O2). Strain 14B successfully converted biogas to methanol using phosphate as a methanol dehydrogenase (MDH) inhibitor and formate as an electron donor. The maximum methanol concentration (0.43±0.00 g L-1) and

CH4 to methanol conversion ratio (25.5±1.8%) were obtained using strain 14B suspended in

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NMS medium (0.4 g dry cells/L) containing 50 mM phosphate and 80 mM formate and headspace gas composed of biogas and air at a 1:2.5 ratio (v/v).

Biological conversion of biogas to methanol is likely limited by the low solubility and mass transport of gaseous substrates (O2, CH4) in NMS medium. From the third project, it was determined that abiotic mass transport of O2 to DI water in a TBR packed with ceramic balls was almost two times higher than an unpacked TBR. The TBR was inoculated with

Methylocaldum sp. 14B and then operated non-sterilely under different dilution rates and biogas:air ratios. Maximum CH4 uptake rates were observed at high biogas:air ratios (1:2.5), and the results suggested that the TBR enhanced gas oxidation compared to shake flasks. The non-sterile TBR also was used to convert biogas to methanol using formate as an electron donor and phosphate as MDH inhibitor. Maximum methanol productivity (0.9 g/L/d) was obtained at 12 mmol formate addition and 3.6 mmol phosphate addition and a biogas:air ratio of 1:2.5 (v/v). Operation under non-sterile conditions caused differences in the microbial community of the TBR.

A mathematical model that considered gas-to-liquid mass transport and methanotroph gas consumption kinetics was developed to analyze the use of TBRs for biological conversion of biogas to methanol. The model was used to generate results that were fairly comparable to selected semi-batch data from the lab-scale TBR project. The model was then used to identify the impacts of key operating parameters on biogas to methanol conversion in a theoretical large scale TBR (H=20 m, D=2 m) that had methanotrophs with good methanol tolerance (30 g/L). The results suggested that maximum methanol yields (18 g/L) could be obtained at high gas velocities (>500 m/h), high methanotroph cell density (40 kg cells/m3), and elevated pressure (up to 3 atm), because those conditions improved mass transport and

iv gas uptake kinetics. Sensitivity analysis indicated that TBR packings with high specific surface area should be used to enhance gas-to-liquid transport, and that methanotrophs with higher CH4 oxidation rates and higher methanol tolerance will improve methanol production rates.

Finally, techno-economic analysis was used to compare the economic feasibility of biological conversion of biogas to methanol to conventional and emerging methods for biogas upgrading at a large-scale biogas production facility (5900 Nm3/h, 5,000,000 sft3/d).

Biogas cleaning via pressurized water scrubbing (PWS) for compressed natural gas production (Bio-CNG) had the highest net present value, followed by PWS for purified biogas production, biological conversion of biogas to methanol, and thermochemical conversion of biogas to methanol. Biological conversion had slightly higher methanol production costs than thermochemical conversion because of low gas conversion rates by methanotrophs. Sensitivity analysis indicated that the costs of biological conversion could be reduced if methanotrophs are modified to have higher CH4 oxidation rate, higher CH4 to methanol conversion ratio, and higher methanol tolerance. Furthermore, the cost of formate needs to be significantly reduced or alternative electron donors are needed for biological conversion of biogas to methanol to be economically feasible.

These results from this research indicate that an integrated process consisting of SS-

AD of switchgrass and biological conversion of biogas to methanol is technically feasible.

The knowledge obtained from these studies could be used to assist in the optimization and scale-up of SS-AD and biotechnologies for biogas valorization.

Acknowledgments

I am incredibly grateful to my original advisor, Dr. Yebo Li, for his guidance throughout the past five years. I did not come into graduate school with the same accolades or test scores as other students, and to an outsider, Dr. Li’s commitment of research funding to me could have been perceived as a risk. However, Dr. Li took a chance on my work ethic and aspirations to improve as an engineer and researcher. He knew how to encourage my strengths and helped me improve on my weaknesses. He introduced me to some of the most genuine and intelligent people I now call friends. There is no doubt that the time working with Dr. Li will go down as one of the most influential experiences in my life. I encourage all those involved in research to tackle their projects with the tenacity and positive attitude that

Dr. Li brought to the Bioproducts and Bioenergy Research Laboratory every single day.

Many thanks go to my dissertation committee members including Dr. Jay Martin, Dr.

Gönül Kaletunç, Dr. Zhongtang Yu, and Dr. Ajay Shah for their advice and collaboration throughout the development of my dissertation. I am especially thankful to Dr. Martin and

Dr. Kaletunç for their advisement during my TA appointment and throughout the last year of my PhD work. Several members of the Department of Food, Agricultural and Biological

Engineering were crucial, including Mrs. Mary Wicks for her time reviewing publications and proposals, Ms. Candy McBride for her administrative assistance and encouragement,

Mrs. Peggy Christman for her administrative assistance, and Mr. Michael Klingman and Mr.

Scott Wolfe for their expertise in the engineering and fabrication of experimental equipment. vi

One of the great joys of graduate school was getting to know the diverse and incredibly talented members of the Bioproducts and Bioenergy Research Laboratory. My sincere thanks go to Dr. Xumeng Ge, Dr. Xiaolan Luo, Dr. Fuqing Xu, Dr. Stephen Park, Dr.

Shengjun Hu, Dr. Liangcheng Yang, Dr. Zhiwu Wang, Ms. Juliana Vasco, Ms. Long Lin,

Ms. Kathryn Lawson, Mr. Adam Khalaf, Mr. Josh Borgemenke, Ms. Lo Niee Liew, Mr. Jia

Zhao, Ms. Zhe Liu, and several others for their willingness to provide advice and mentorship throughout this journey. I especially want to thank Juliana Vasco, Dr. Xumeng Ge and Ms.

Kathryn Lawson for their incredible work ethic and support.

I am indebted to the organizations that funded this research, including the U.S. EPA

Science to Achieve Results (STAR) Graduate Fellowship Program, The Ohio State

University Presidential Fellowship Program, The Ohio State University Office of Student

Life, The Ohio Agricultural Research and Development Center SEEDS program, and the

USDA NIFA Biomass Research and Development Initiative. Thank you to the industry and academic collaborators including quasar energy group, Koch Knight LLC, and the Ohio

Supercomputer Center for graciously donating materials and computing services that were critical for this research.

None of this would have been possible without the unconditional love and support from family and friends. I am extremely grateful to my parents, parents-in-law, siblings/siblings-in-law, and extended family for their encouragement, positive attitude, and belief in my abilities. Most of all, I am so thankful to my loving wife Megan and our Golden

Graham. You were always there for me when I needed you, and motivated me to fight through many obstacles. This dissertation is dedicated to you. I love you with all my heart.

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Vita

2011 ...... B.S. Engineering Management, Miami University

2013 ...... M.S. Food, Agricultural and Biological Engineering, The Ohio State University

2013 to 2014 ...... Graduate Teaching Associate, Food Agricultural and Biological Engineering, The Ohio State University

2014 to 2016 ...... Graduate Research Associate, Department of Food Agricultural and Biological Engineering, The Ohio State University

2016 to present ...... U.S. EPA Science to Achieve Results (STAR) and Ohio State University Presidential Fellow, Food Agricultural and Biological Engineering, The Ohio State University

Publications

Sheets, J.P., Lawson, K., Ge, X., Wang, L., Yu, Z., Li, Y. 2017. Development and evaluation of a trickle-bed bioreactor for enhanced mass transfer and methanol production from biogas. Biochem. Eng. J. 122: 103-114.

Sheets, J.P., Ge, X., Li, Y.F., Yu, Z., Li, Y. 2016. Biological conversion of biogas to methanol using methanotrophs isolated from solid-state anaerobic digestate. Bioresour. Technol. 201: 50-57.

Sheets, J.P., Ge, X., Li, Y. 2015. Effect of limited air exposure and comparative performance between thermophilic and mesophilic solid-state anaerobic digestion of switchgrass. Bioresour. Technol. 180: 296-303.

viii

Ge, X., Yang, L., Sheets, J.P., Yu, Z., Li, Y. 2014. Biological conversion of methane to liquid fuels: status and opportunities. Biotechnol. Adv. 32: 1460-1475.

Sheets, J.P., Yang, L., Ge, X., Wang, Z., Li, Y. 2015. Beyond land application: Emerging technologies for the treatment and reuse of anaerobically digested agricultural and food waste. Waste Manag. 44: 94-115.

Sheets, J.P. Ge, X. Park, S.Y., Li, Y. 2014. Effect of outdoor conditions on Nannochloropsis salina cultivation in artificial seawater using nutrients from anaerobic digestion effluent. Bioresour. Technol. 152: 154-161.

Ge. X., Sheets, J.P., Li, Y., Mani, S. 2016. “Algae-Based Feedstocks.” In Li, Y., Khanal, S. Bioenergy: Principles and Applications. Wiley-Blackwell.

Fields of Study

Major Field: Food, Agricultural, and Biological Engineering

Study in: Biological/Bioenvironmental Engineering

Table of Contents

Abstract ...... ii Acknowledgments ...... vi Vita ...... viii Table of Contents ...... x List of Tables ...... xv List of Figures...... xviii Chapter 1: Introduction...... 1 1.1. Background ...... 1 1.2. Goals and Objectives ...... 5 1.3. Contribution of the Dissertation ...... 6 Chapter 2: Literature Review ...... 7 2.1. Introduction ...... 7 2.2. Anaerobic Digestion ...... 8 2.2.1. Solid-state anaerobic digestion ...... 9 2.3. Biogas Upgrading ...... 11 2.3.1. Biogas cleaning ...... 11 2.3.2. Biogas uses ...... 14 2.3.3. Methanol production via thermochemical methods ...... 16 2.3.4. Biological conversion of methane to methanol ...... 18 2.4. Methanotrophs ...... 20 2.4.1. Microbiology and biochemistry ...... 20 2.4.2. Methanol production ...... 22 2.4.3. Other biotechnological applications ...... 26 2.5. Bioreactors for Methanotroph Cultivation ...... 29 2.5.1. Gas-to-liquid transfer ...... 29 2.5.2. Bioreactors for enhanced gas-liquid mass transfer ...... 31 x

2.5.3. Trickle-bed reactors ...... 33 2.5.4. Modeling of trickle-bed bioreactors ...... 35 2.6. Techno-economic Analysis ...... 37 2.6.1. Biogas ...... 37 2.6.2. Methanotroph bioprocesses ...... 39 2.7. Concluding Remarks ...... 40 Chapter 3: Effect of Limited Air Exposure and Comparative Performance Between Thermophilic and Mesophilic Solid-State Anaerobic Digestion of Switchgrass ...... 52 3.1. Introduction ...... 53 3.2. Methods ...... 55 3.2.1. Feedstock and inoculum ...... 55 3.2.2. Solid-state anaerobic digestion ...... 56 3.2.3. Analytical methods ...... 57 3.2.4. Energy analysis ...... 59 3.2.5. Statistical analysis ...... 61 3.3. Results and Discussion ...... 61 3.3.1. Composition of switchgrass and L-AD effluent ...... 61 3.3.2. Effect of air exposure on SS-AD performance ...... 62 3.3.3. Effect of temperature and TS on SS-AD performance ...... 63 3.3.4. Performance perspectives and net energy analysis ...... 65 3.4. Conclusions...... 67 Chapter 4: Biological Conversion of Biogas to Methanol using Methanotrophs Isolated from Solid-State Anaerobic Digestate ...... 78 4.1. Introduction ...... 79 4.2. Materials and Methods ...... 81 4.2.1. Isolation of methanotrophs ...... 81 4.2.2. Cultivation with different chemical and physical inputs ...... 83 4.2.3. Cultivation on different methane sources ...... 84 4.2.4. Methanol formation ...... 85 4.2.5. Analytical methods ...... 86 4.2.6. Statistical analysis ...... 88 4.3. Results and Discussion ...... 88 4.3.1. Isolation and characterization of a methanotrophic strain from digestate ...... 88 xi

4.3.2. Comparison of growth on different methane sources ...... 90 4.3.3. Screening of methanol dehydrogenase inhibitors for methanol production ...... 91 4.3.4. Effects of formate and phosphate concentrations on methanol production ...... 91 4.3.5. Effects of formate concentration on biogas to methanol conversion ...... 93 4.4. Conclusion ...... 94 Chapter 5: Development and Evaluation of a Trickle Bed Bioreactor for Enhanced Mass Transfer and Methanol Production from Biogas ...... 102 5.1. Introduction ...... 103 5.2. Materials and Methods ...... 106 5.2.1 TBR set-up ...... 106 5.2.2. Gas feeding procedure ...... 107 5.2.3. Phase 1: Optimization of operating parameters and effects of biogas ...... 108

5.2.3.1. Phase 1.1: Optimization of TBR operating parameters using purified CH4 as substrate ...... 108 5.2.3.2. Phase 1.2: Impacts of biogas: air ratio ...... 110 5.2.4. Phase 2: Impacts of biogas on reactor startup and methanol production ...... 112 5.2.4.1. Phase 2.1: Non-sterile start-up using biogas as substrate ...... 112 5.2.4.2. Phase 2.2: Methanol production from biogas ...... 112 5.2.5. Control experiments and mass transfer analysis ...... 113 5.2.6. Analytical methods ...... 115 5.2.7. Microbial community analysis ...... 117 5.3. Results and Discussion ...... 118

5.3.1. Phase 1: Influence of operational parameters and biogas on CH4 removal ...... 118

5.3.1.1. Phase 1.1: Determination of optimal operating parameters using purified CH4 ...... 118 5.3.1.2. Phase 1.2: Effect of biogas:air ratio on TBR performance and control experiments ...... 120 5.3.1.3. Preliminary methanol production experiments ...... 122 5.3.2. Phase 2: Start-up on biogas and methanol production ...... 124 5.3.2.1. Phase 2.1: Startup under non-sterile conditions ...... 124 5.3.2.2. Phase 2.2: Effects of biogas and formate on methanol production ...... 124 5.3.3. Microbial community ...... 127 5.4. Conclusions...... 130 xii

Chapter 6: Exploratory Modeling of Biological Conversion of Biogas to Methanol in a Trickle Bed Bioreactor ...... 142 6.1. Introduction ...... 143 6.2. Model Development ...... 145 6.2.1. Methanol production kinetics ...... 145 6.2.2. TBR model...... 147 6.2.3. Laboratory scale TBR simulation ...... 152 6.2.4. Sensitivity analysis ...... 154 6.2.5. Large scale TBR simulation ...... 154 6.2.6. Numerical solutions ...... 156 6.3. Results and Discussion ...... 156 6.3.1. Laboratory scale TBR simulation ...... 156 6.3.1.1. Comparison between simulations and laboratory data ...... 156 6.3.1.2. Sensitivity analysis ...... 159 6.3.2. Large scale TBR simulation ...... 160 6.3.2.1. Effects of cell density, gas velocity, and biogas:air ratio ...... 160 6.4. Conclusions...... 163 Chapter 7: Techno-Economic Comparison of Biogas Upgrading via Purified Biogas for Grid Injection, Compressed Natural Gas, Thermochemical Conversion of Biogas to Methanol, and Biological Conversion of Biogas to Methanol ...... 178 7.1. Introduction ...... 179 7.2. Modeling Overview ...... 182 7.2.1. Biogas cleaning via PWS ...... 183 7.2.2. Bio-CNG ...... 184 7.2.3. Thermochemical conversion of biogas to methanol ...... 185 7.2.3.1. Biogas cleaning via PWS ...... 185

7.2.3.2. Steam CH4-reforming for syngas production ...... 185 7.2.3.3. Conversion of syngas to methanol ...... 187 7.2.3.4. Methanol purification ...... 189 7.2.3.5. Energy recovery ...... 190 7.2.4. Biological conversion of biogas to methanol ...... 190 7.2.4.1. Methanotroph biomass production...... 190 7.2.4.2. Biogas to methanol conversion in a TBR ...... 193 xiii

7.2.4.3. Methanol purification ...... 196 7.2.4.4. Energy recovery ...... 196 7.2.5. Analyses ...... 196 7.2.5.1. Resource assessment and GHG emissions ...... 196 7.2.5.2. Process economics ...... 197 7.2.5.3. Sensitivity analysis ...... 199 7.3. Results and Discussion ...... 199 7.3.1. Material and resource analysis ...... 199 7.3.2. Economic analysis ...... 202 7.3.2.1. PWS for purified biogas ...... 202 7.3.2.2. Bio-CNG ...... 203 7.3.2.3. Thermochemical conversion of biogas to methanol ...... 204 7.3.2.4. Biological conversion of biogas to methanol ...... 205 7.3.3. Sensitivity analysis ...... 207 7.4. Conclusion ...... 208 Chapter 8: Conclusions and Suggestions for Future Research ...... 222 8.1. Conclusions...... 222 8.2. Suggestions for Future Research ...... 224 References ...... 226 Appendix A: Supplemental Data for Chapter 5 ...... 247 Appendix B: Parameters and Variables for TBR Models ...... 252 Appendix C: Process Flow Data for Pressurized Water Scrubbing ...... 259 Appendix D: Process Flow Data for Bio-Compressed Natural Gas ...... 265 Appendix E: Process Flow Data for Thermochemical Conversion of Biogas to Methanol .. 271 Appendix F: Process Flow Data for Biological Conversion of Biogas to Methanol ...... 284

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List of Tables

Table 2.1: Comparison of biogas cleaning technologies ...... 42

Table 2.2: Comparison of biogas use methods ...... 43

Table 2.3: Summary of conditions and performance for biological conversion of CH4 to methanol (adapted from Ge et al. (2014)) ...... 44

Table 3.1: Composition of L-AD effluent ...... 69

Table 3.2: Reactor characteristics before and after 70 days of SS-AD ...... 70

Table 3.3: Cellulose, hemicellulose and VS removal after 70 days of SS-AD ...... 71

Table 3.4: Assumptions used in net energy analysis ...... 72

Table 4.1: Comparison of growth characteristics of strain 14B to those of Methylocaldum species ...... 95

Table 4.2: Effect of CH4 source on growth of strain 14B ...... 96

Table 4.3: Effects of MDH inhibitors on methanol production ...... 97

Table 5.1: TBR operating phases ...... 131

Table 5.2: TBR performance during Phase 1.1 ...... 132

Table 5.3: Effects of the packing material on mass transfer and performance in the TBR ... 133

Table 5.4: Methanol production efficiencies at different biogas:air ratios and formate additions ...... 134

Table 5.5: Relative abundance of genera in the TBR at different operational phases ...... 135

Table 6.1: Biochemical reaction rates used in TBR modeling ...... 164

Table 6.2: Transport terms, boundary conditions and reactions used in TBR simulation..... 165

Table 6.3: Sensitivity analysis of lab scale TBR model ...... 166

Table 7.1: Economic evaluation parameters for biogas upgrading technologies ...... 209

Table 7.2: Resource requirements for biogas upgrading technologies ...... 210

Table 7.3: Comparison of investment and operational costs for biogas upgrading technologies

...... 212

Table A.1: Summary of sampling data and sequence clustering for microbial community analyses ...... 248

Table A.2: Relative abundance of major bacterial OTUs in Phase 2.1 samples ...... 249

Table A.3: Comparative relative abundance of bacterial taxa in TBR samples ...... 250

Table B.1: Parameters and variables used for lab scale TBR model verification ...... 253

Table B.2: Parameters and variables used for large scale TBR model ...... 256

Table C.1: Parameter values for sensitivity analysis of biogas cleaning via PWS...... 260

Table C.2: Streams report for biogas cleaning via PWS ...... 261

Table C.3: Equipment purchase costs for biogas cleaning via PWS ...... 263

Table C.4: Breakdown of utility costs for biogas cleaning via PWS ...... 264

Table D.1: Parameter values for sensitivity analysis of biogas upgrading to Bio-CNG ...... 266

Table D.2: Streams report for biogas upgrading to Bio-CNG ...... 267

Table D.3: Equipment purchase costs for biogas upgrading to Bio-CNG ...... 269

Table D.4: Breakdown of utility costs for biogas upgrading to Bio-CNG ...... 270

Table E.1: Parameter values for sensitivity analysis of thermochemical conversion of biogas to methanol ...... 272

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Table E.2: Streams report for thermochemical conversion of biogas to methanol ...... 274

Table E.3: Equipment purchase costs for thermochemical conversion of biogas to methanol

...... 280

Table E.4: Breakdown of utility costs for thermochemical conversion of biogas to methanol

...... 281

Table F.1: Parameter values for sensitivity analysis of biological conversion of biogas to methanol ...... 285

Table F.2: Streams report for biological conversion of biogas to methanol ...... 287

Table F.3: Equipment purchase costs for biological conversion of biogas to methanol ...... 292

Table F.4: Breakdown of utility costs for biological conversion of biogas to methanol ...... 294

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List of Figures

Figure 2.1: Thermochemical conversion of natural gas to methanol (adapted from Riaz et al.

(2013)) ...... 48

Figure 2.2: Pathway for methane oxidation in aerobic methanotrophs (adapted from Fei et al.

(2014))...... 49

Figure 2.3: Strategy for methanol production using aerobic methanotrophs (adapted from Ge et al. (2014)) ...... 50

Figure 2.4: Schematic of two film theory (adapted from Cooper and Alley (2011)) ...... 51

Figure 3.1: Effects of reactor conditions on SS-AD performance for N2 purged reactors (a,b) and reactors with limited air exposure (c,d). Asterisks show days when 100 mL of air was displaced into the headspace...... 73

Figure 3.2: Cumulative CH4 yield for N2 purged reactors ...... 75

Figure 3.3: Comparison of CH4 productivity for N2 purged reactors at 35 days and 70 days 76

Figure 3.4: Daily (a) and cumulative (b) net energy production. Note: Initial energy for heating digester contents is included in (b)...... 77

Figure 4.1: Growth, gas consumption and gas production using purified CH4 (a) and biogas

(b) as a substrate...... 98

Figure 4.2: Effects of phosphate and formate concentrations on methanol production from biogas. *=no methanol detected...... 99

xviii

Figure 4.3: Time course of methanol production at 40 mM formate (a), 80 mM formate (b), and 120 mM formate (c)...... 100

Figure 5.1: TBR set up for biogas conversion to methanol: solid lines show direction of liquid flow and dashed lines show direction of gas flow. 1) TBR; 2) Gas feeding and sampling flask; 3) Gas bag; 4) Gas sampling and feeding port; 5) Syringe for vacuum creation; 6) Gas circulation pump; 7) Three-way valve for gas circulation shut off; 8) Three-way valve for liquid sampling and medium replacement; 9) Liquid circulation pump...... 136

Figure 5.2: Dynamics of CH4 removal in the TBR during Phase 1...... 137

Figure 5.3: Effects of biogas: air ratio on TBR performance during Phase 1.2...... 138

Figure 5.4: Effects of biogas:air ratio and formate addition on methanol production in the

TBR: (a): biogas:air=1:2.5, formate=12 mmol; (b): biogas:air=1:2.5, formate=6 mmol; (c): biogas:air=1:6.0, formate=12 mmol; (d): biogas:air=1:6.0, formate=6 mmol...... 139

Figure 5.5: Major bacterial phyla (bolded, each representing >0.5% of total sequences in ≥1 sample) and major orders of Bacteroidetes and Proteobacteria (each representing >5% of total sequence of each phylum in ≥1 sample) ...... 141

Figure 6.1: Conceptualization of trickle bed reactor model for methanol production from biogas...... 169

Figure 6.2: Lab scale model verification using a series of steady-state TBRs: Each pass through the TBR occurred over a set gas retention time, and produced results that were applied as boundary conditions for the following pass through the TBR. A designated number of passes corresponded to theoretical retention times that were compared to laboratory-scale semi-batch data...... 170

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Figure 6.3: Comparison of model predictions from the “TBR in series” approach (solid lines) to laboratory scale data from Sheets et al. (2017) (symbols) at biogas to air ratios of 1:2.5 (a) and 1:6.0 (b)...... 171

Figure 6.4: Impacts of cell density on biogas to methanol conversion in the large scale TBR at atmospheric pressure and gas velocities of (a) 100 m/h, (b) 300 m/h, and (c) 500 m/h (uL=5 m/h; biogas:air=1:1.33 (CH4:O2=1:1.08))...... 172

Figure 6.5: Effects of methane to oxygen ratio on large scale TBR performance at

3 atmospheric pressure, cell density of 40 kg/m and gas velocity of 500 m/h (uL=5 m/h). .... 175

Figure 6.6: Effects of methane to oxygen ratio and pressure on methanol production (a), CH4 conversion (b), and O2 conversion (c) in the large scale TBR at inlet gas velocity of 500 m/h

3 and cell density of 40 kg/m (uL=5 m/h)...... 176

Figure 7.1: Annual operating costs for (a) pressurized water scrubbing; b) bio-CNG; c) thermochemical conversion of biogas to methanol; and d) biological conversion of biogas to methanol. *=operating cost based on biogas feed stream (secondary axis)...... 213

Figure 7.2: Breakdown of utility costs for thermochemical (a) and biological (b) conversion of biogas to methanol ...... 215

Figure 7.3: Sensitivity analysis of operating costs for (a) pressurized water scrubbing; (b) bio-

CNG; (c) thermochemical conversion of biogas to methanol; (d) biological conversion of biogas to methanol (includes centrifuge recycle rate); and (e) biological conversion of biogas to methanol (no centrifuge recycle rate). Figures only show parameters that caused an average relative change of 0.025 or higher...... 217

Figure A.1: TBR performance during days 0-17 of Phase 2 ...... 251

Figure C.1: Process model for biogas cleaning via pressurized water scrubbing (PWS)...... 259

Figure D.1: Process model for biogas upgrading to Bio-CNG...... 265

Figure E.1: Process model for thermochemical conversion of biogas to methanol ...... 271

Figure F.1: Process model for biological conversion of biogas to methanol ...... 284

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Chapter 1: Introduction

1.1. Background

The rapid rise in global population will undoubtedly lead to increased demand for food and energy. Crops that are bred to have enhanced yields and fewer requirements for water and nutrients have great potential to meet the food demands of an estimated 9 billion global population by 2050 (Godfray et al., 2010; Tester and Langridge, 2010). However, 30 to 50% of all food produced is lost as waste (Foley et al., 2011; Godfray et al., 2010).

Additionally, food that is consumed by humans and animals is eventually excreted as massive quantities of wastewater sludge and manure, respectively (Murray et al., 2014).

The most common methods of managing such organic wastes, such as landfills and manure storage contribute to the release of methane (CH4), a potent greenhouse gas that traps

20-25 times more heat than carbon dioxide (CO2) (USEPA, 2016a). Furthermore, socioeconomic and environmental concerns about fossil fuel consumption have necessitated the development of renewable energy sources. Therefore, technologies that can convert organic materials to renewable fuels, such as anaerobic digestion, are needed to improve the long-term sustainability of organic waste treatment and energy production (Abbasi et al.,

2012).

Anaerobic digestion (AD) is a robust process that relies on a community of microorganisms to convert organic materials under oxygen (O2) free conditions into two 1 main products: biogas and digestate. Biogas, composed of mostly CH4, CO2, and a few impurities (i.e. hydrogen sulfide (H2S)), can be used directly for renewable power and heat or can be upgraded to transportation fuels such as compressed natural gas (Bio-CNG) (Yang et al., 2014). The digestate has high levels of nitrogen and phosphorus, and is commonly used for fertilizer (Sheets et al., 2015b).

The maximum total energy potential from captured landfill gas and biogas produced from the AD of traditional feedstocks (i.e. wastewater sludge, manures and food waste) could only displace 5% of the natural gas currently used for electricity production in the U.S., and only 33% of all U.S. electricity production was from natural gas in 2015 (NREL, 2013;

USEIA, 2016). In order for biogas production technologies to have a greater contribution to the energy supply, lignocellulosic energy crops such as switchgrass should be considered as a feedstock for AD (Ge et al., 2016).

Switchgrass (Panicum virgatum L.) is an attractive feedstock because it has high biomass yields, low production costs, and limited impacts on food supply and the environment (Frigon and Guiot, 2010; Hudiburg et al., 2016; Keshwani and Cheng, 2009).

However, switchgrass is difficult to digest in traditional liquid-AD systems (i.e. wet digestion) that operate at low total solids contents of less than 15%, because switchgrass has low bulk density that makes it difficult to homogenize using standard mixing equipment

(Liew et al., 2012). Solid-state anaerobic digestion (i.e. dry digestion), which operates at total solids contents of more than 15%, is better suited to convert low-moisture organics to biogas.

Compared to liquid-AD, solid-state systems have smaller reactor volumes and lower energy demands for heating and mixing (Li et al., 2011). Still, research on solid-state anaerobic digestion of switchgrass is limited (Ahn et al., 2010; Brown et al., 2012; El-Mashad, 2013).

Furthermore, several important operational parameters that can impact biogas production and quality, such as limited air exposure and temperature, have been sparingly tested for solid- state AD of lignocellulosic biomass.

An important issue that may hinder bioenergy production via AD is that biogas is difficult to store and transport. Although biogas can be cleaned and upgraded into compressed natural gas, biogas purification and compression (200–250 atm) can be expensive. This issue could be addressed by converting biogas to methanol, a commodity chemical that can be used as fuel or transformed into a variety of products, such as polymers and gasoline (Olah et al., 2011; Wernicke et al., 2014; Yang et al., 2014). Compared to biogas, methanol is less costly to store and transport because it is a liquid under ambient conditions. Thermochemical processes have been commonly used to convert carbonaceous feedstocks (natural gas, coal, biomass) to methanol, but they require non-renewable metal catalysts and operate at high temperatures (200-900°C) and pressures (5-200 atm) (Blug et al., 2014; Riaz et al., 2013). Additionally, thermochemical conversion requires gases that are free of impurities such as H2S. In contrast, biological conversion of biogas to methanol is an emerging approach that uses a renewable biocatalyst, may not require biogas cleaning, and operates under mild conditions (30-50°C, 1-5 atm), reducing operational requirements and energy demands (Ge et al., 2014).

There are aerobic bacteria called methanotrophs that convert CH4 and O2 to methanol with the enzyme methane monooxygenase (MMO). Under normal conditions, methanotrophs oxidize methanol to CO2 via methanol dehydrogenase (MDH), formaldehyde oxidizing enzymes, and formate dehydrogenase (FDH) in a sequential manner. These oxidative reactions generate adenosine triphosphate (ATP) and electrons needed for metabolic

3 processes (Hanson and Hanson, 1996; Kalyuzhnaya et al., 2015). Therefore, MDH inhibitors and exogenous electron donors, such as formate, are needed to support methanol production by methanotrophs (Duan et al., 2011; Han et al., 2013; Mehta et al., 1991). Recent advances in electrochemical and photocatalytic production of formate from CO2 and H2O provide an opportunity to lower the costs of this important electron donor (Yishai et al., 2016). However, several other process improvements are still needed before biological conversion of biogas to methanol can be scaled up.

First, very few methanotrophs can convert raw biogas directly to methanol. Second, biological biogas upgrading can also be limited by the poor solubility of gaseous substrates

(CH4, O2) in the liquid medium (Ge et al., 2014). Therefore, reactors designed to enhance gas-to-liquid mass transport are needed to improve process performance (Munasinghe and

Khanal, 2010). Continuous stirred tank reactors (CSTRs) that have spargers to supply fine gas bubbles and/or impellers for rapid stirring are often used to improve gas-liquid transport, but they have high energy demands (Fei et al., 2014; Petersen et al., 2016). Thirdly, the complex combination of gas-liquid transport and biological kinetics have made it difficult to predict and understand the behavior of bioreactors for gas conversion (Chen et al., 2015).

Finally, the techno-economic feasibility of the biological biogas-to-methanol process has not been evaluated and it needs to be compared to existing and emerging biogas upgrading technologies.

Potentially, methanotrophs isolated from AD systems could use unpurified biogas for methanol production, eliminating the need for expensive biogas cleaning operations (Ge et al., 2014). High mass transfer rates can also be achieved using trickle-bed reactors (TBRs) equipped with packing materials that have a high specific surface area. Contrary to CSTRs,

TBRs are known for low energy demands and low capital costs (Deshusses and Cox, 1999;

Devarapalli et al., 2016). In fact, a type of TBR called a biotrickling filter has been used to convert dilute CH4 emissions to CO2 (Estrada et al., 2014b; Yoon et al., 2009), but never for

CH4 to methanol conversion. Mathematical modeling is a low-cost option to better understand the complexities of TBRs, and could be used to determine the impacts of key design parameters on reactor performance (Chen et al., 2015; Yoon et al., 2009). Lastly, techno-economic analysis can be used to compare the feasibility of the biological biogas-to- methanol process to competing biogas upgrading technologies, and it can help identify process constraints that need to be addressed prior to commercialization (Shah et al., 2016).

1.2. Goals and Objectives

The goal of this project was to develop and analyze an integrated biological process for the conversion of biomass to methanol. To accomplish this goal, the following objectives were implemented:

1. Evaluate the effects of operational factors on biogas production via solid-state

anaerobic digestion of switchgrass.

2. Isolate methanotrophs from solid state anaerobic digestate and optimize the

conditions for the biological conversion of biogas to methanol.

3. Design and evaluate a trickle-bed bioreactor for methanol production from biogas.

4. Simulate biogas-to-methanol conversion in a trickle-bed bioreactor and assess the

impacts of key operational factors on reactor performance.

5. Compare the economic feasibility of the biological biogas-to-methanol process to

other biogas upgrading technologies.

1.3. Contribution of the Dissertation

Three papers have been published in refereed journals (Chapter 3, Chapter 4, Chapter

5) and two are being prepared for publication (Chapter 6, Chapter 7).

Papers published:

Sheets, J.P., Ge, X., Li, Y.F., Yu, Z., Li, Y. 2016. Biological conversion of biogas to methanol using methanotrophs isolated from solid-state anaerobic digestate. Bioresour. Technol. 201: 50-57.

Chapter 2: Literature Review

2.1. Introduction

Concerns about fossil fuel depletion and the impacts of climate change have stimulated interest in renewable energy sources that limit greenhouse gas (GHG) emissions.

More than 50 million tons of organic materials are sent to U.S. landfills each year, where they are anaerobically converted to biogas (30-65% CH4, 25-47% CO2, <1-17% N2, <1-3%

O2, trace H2S, siloxanes) (Yang et al., 2014) that is often released to the atmosphere without intervention (USEPA, 2016a, 2016b). In fact, landfills are the source of about 20% of U.S. methane (CH4) emissions (2% of all GHG emissions). Additionally, manure management activities and human wastewater treatment facilities produce 8% and 2% of U.S. CH4 emissions, respectively (USEPA, 2016a). These waste management practices contribute to global climate change and represent a lost opportunity to employ organic materials as feedstocks for renewable chemical and fuel production.

The efficiency of organic waste management could be improved through widespread development of engineered anaerobic digestion (AD) systems that can convert organic wastes to biogas (50-70% CH4, 30-50% CO2, 0-2000 ppm H2S, other trace impurities) (Yang et al.,

2014) in a controlled manner. AD is very robust, is easily optimized, and can be adapted to digest a variety of organic feedstocks, such as lignocellulosic biomass. AD of organic wastes and lignocellulosic feedstocks has great potential to supply economically significant 7 quantities of CH4 for fuel and chemical production. Gas wells can also be installed at existing landfills to collect additional bio-CH4 (USEPA, 2015). However, less than 20% of the AD facilities and landfills that could produce and/or collect biogas for renewable energy have been implemented (USDA et al., 2014). One of the major reasons for lack of development is that biogas from AD and landfills is dilute (30-70% CH4) compared to low cost natural gas

3 ($3-5/thousand ft , >90% CH4). In addition, biogas contains impurities (i.e. H2S, CO2) that are expensive to remove (Yang and Ge, 2016). To compete with natural gas, cost-effective and environmentally friendly processes are needed to improve biogas value. In this chapter, applied aspects of the AD process, current and emerging technologies for biogas valorization, and strategies to improve those technologies are reviewed. Emphasis was placed on an emerging biological process that uses methanotrophs (methane-oxidizing bacteria) to convert biogas to methanol.

2.2. Anaerobic Digestion

Anaerobic digestion is a complex process by which microbes convert organic matter to biogas under oxygen free conditions (Korres et al., 2013). The process consists of four synergistic phases: 1) hydrolysis of polymers such as fats, proteins and carbohydrates to fermentable monomers (fatty acids, amino acids, peptides, sugars); 2) acidogenesis of monomers to short chain fatty acids (SCFAs), hydrogen (H2) and CO2; 3) syntrophic acetogenesis of non-acetate SCFAs to acetate, H2, and CO2; and 4) methanogenesis of fermentation/acetogenesis products to CH4-rich biogas (Yu and Shanbacher, 2010).

Most engineered AD systems are designed to convert high moisture wastes, such as sewage sludge, food wastes, and manures to biogas (Korres et al., 2013; Yu and Shanbacher,

2010). Biogas from AD is composed of ~50-70% CH4, ~30-50% CO2, and small quantities of water vapor (1-5%), O2 (0-5%), N2 (2-6%), NH3 (<100 ppm) and H2S (0-2000 ppm) (Yang et al., 2014). NH3 and H2S are produced by degradation of proteins during AD (Strik et al.,

2005). Biogas can be combusted for renewable electricity and heat, upgraded into transportation fuels such as compressed natural gas (Bio-CNG), or converted to chemicals such as methanol, as discussed in Section 2.3 (Yang et al., 2014). AD also generates a nutrient rich (N,P) digestate that is commonly land applied as a fertilizer for crop production

(Sheets et al., 2015b).

AD is an intriguing option for bioenergy production from lignocellulosic materials such as crop residues, municipal solid waste, forestry wastes, and energy crops. For example, if all the crop residues, the organic fraction of municipal solid waste (OFMSW), forestry wastes, and potential biomass from energy crops produced in the U.S. (>200 million metric

-1 tons (MT) yr ) were sent to AD, the CH4 produced would be energetically equivalent to >20 million MT of crude oil (Ge et al., 2016). However, most large scale AD processes (>2,000

AD facilities in the U.S.) are operated at total solids (TS) contents of less than 15% and are classified as liquid-AD (L-AD) (Xu et al., 2014). Lignocellulosic materials that have high TS content and low bulk density, such as switchgrass, are difficult to homogenize in L-AD reactors, which reduces biogas production (Jha et al., 2011; Liew et al., 2011).

2.2.1. Solid-state anaerobic digestion

Solid-state anaerobic digestion (SS-AD) is operated at TS contents greater than 15%, and is well suited to convert lignocellulosic biomass to biogas (Ge et al., 2016; Li et al.,

2011; Xu et al., 2014). SS-AD can handle similar organic loading rates (12-15 kg volatile

9 solids (VS) m-3 d-1) to L-AD (0.7-15 kg VS m-3 d-1), but in smaller reactors (Korres et al.,

2013). Thus, SS-AD has produced higher volumetric CH4 productivity (volume CH4/volume working reactor volume) than L-AD of similar lignocellulosic feedstocks (Brown et al.,

2012).

SS-AD has longer retention times (30-60 days) and lower CH4 yields per unit of volatile solids (L CH4/kg VS) than L-AD because SS-AD has slower reaction rates (Korres et al., 2013; Li et al., 2011). This is likely because the rate at which hydrolytic enzymes diffuse to the substrate surface is lower at the low moisture contents encountered in SS-AD. This lowers the rate of biomass hydrolysis and the overall rate of digestion (Xu et al., 2014).

Additionally, nitrogen supplementation is often needed in SS-AD because lignocellulosic feedstocks have high carbon to nitrogen (C/N) ratios. High C/N ratio can lead to rapid fermentation and acidification of the SS-AD system (Ge et al., 2016). However, the C/N ratio can be balanced and acidification can be controlled using an inoculum with high nitrogen content and high alkalinity, such as L-AD digestate (Shi et al., 2014).

Several environmental conditions, such as limited air exposure and temperature, can positively impact AD performance. For example, increasing the reactor temperature from mesophilic (37°C) to thermophilic (55°C) conditions can change the microbial community, elevate process kinetics, and improve biogas production (Li et al., 2016). Also, limited air exposure to the digester headspace can improve substrate degradation by increasing the activity of facultative anaerobes involved in hydrolysis/fermentation (Lim and Wang, 2013).

Limited air exposure can also improve biogas quality, because it has been shown to promote in-situ H2S removal via oxidative/biological processes (Botheju and Bakke, 2011; Díaz et al.,

2010; Labatut et al., 2014). Although these environmental conditions have been well studied for L-AD, they have not been assessed for the SS-AD of energy crops.

CH4 yields between 200-300 L CH4/kg VS have been reported for L-AD of switchgrass (Frigon et al., 2012; Frigon and Guiot, 2010; Jackowiak et al., 2011; Massé et al.,

2010). However, most of those studies used switchgrass that was physically, chemically, or biologically pretreated prior to digestion, and none mentioned the issues regarding floating and stratification of lignocellulosic fibers. The few studies on the SS-AD of switchgrass have

-1 reported CH4 yields between 100-200 L CH4 kg VS (Ahn et al., 2010; Brown et al., 2012;

El-Mashad, 2013). However, the effects of temperature, TS content and limited air exposure on SS-AD performance have not been evaluated, and a comparative energy analysis between mesophilic and thermophilic conditions has not been conducted.

2.3. Biogas Upgrading

2.3.1. Biogas cleaning

Biogas has a complex composition (30-70% CH4, 30-50% CO2, 1-17% N2, 0-5% O2,

0-2000 ppm H2S) and the impurities (i.e. CO2, H2S) can lower heating value, cause corrosion on pipes, poison chemical/biological catalysts, or impact human health and the environment

(Yang et al., 2014). Therefore, impurities need to be removed before biogas can be combusted, injected into natural gas pipelines, or used as transportation fuel or chemical feedstock. The required intensity of biogas cleaning is dependent on the selected end-use application. For example, only H2S needs to be removed from biogas prior to combustion.

But before purified biogas (>97% CH4) can be injected into U.S. natural gas pipelines, CO2

3 (<3%), H2S (<4 ppm) and others (H2O<112 mg/m , O2<1 ppm) need to be at very low levels. 11

Biogas also needs to be purified (>97% CH4) before it can be compressed/liquefied to compressed natural gas (Bio-CNG) or liquefied biogas (LBG) (USEPA, 2015; Yang et al.,

2014; Yang and Ge, 2016). The most common technologies for biogas cleaning are pressurized water scrubbing (used by ~40% of all plants), pressurized swing adsorption

(23%), amine scrubbers (22%), membrane permeation (8%), organic solvent scrubbers (7%), and cryogenic methods (<1%) (Bauer et al., 2013; Muñoz et al., 2015).

Pressurized water scrubbing (PWS) takes advantage of the fact that CO2 and H2S have much higher solubility in water than CH4 (Cooper and Alley, 2011; Muñoz et al., 2015;

Yang et al., 2014). First, water is pressurized (6-20 atm) and sent to the top of an absorption tower that contains packing materials with a high specific surface area (Bauer et al., 2013;

Muñoz et al., 2015; Yang et al., 2014). Biogas is compressed and sent to the bottom of the absorption tower, where it flows counter-current to the water. Most of the CO2 and H2S and small levels of CH4 (1-5%) are absorbed in the water stream (Muñoz et al., 2015; Sun et al.,

2015). The gas exiting the absorption tower normally contains 93-99% CH4, 1-7% CO2, and small quantities of water (Sun et al., 2015; Yang et al., 2014). The water exiting the absorption tower is then sent to a flash unit (2-4 atm) to release CH4 that is recycled back to the absorption tower (Yang et al., 2014). Finally, the water exiting the flash unit is sent to a stripping tower where air is used to desorb dissolved gases that will require further treatment

(CO2 and H2S). A large portion of the water from the stripping tower is recycled back to the absorption tower. Usually, 0.1-0.2 Nm3 of water is required per Nm3 of biogas for effective

CO2 and H2S removal (Muñoz et al., 2015). The primary advantages of PWS are that no special chemicals are needed and that both CO2 and H2S can be removed. The primary disadvantage of PWS is high water demand (Yang et al., 2014) (Table 2.1).

Organic solvent and amine scrubbers are similar to PWS systems, except that absorbents with higher affinities for CO2 and/or H2S are used (i.e. polyethylene glycol, amine solutions) (Muñoz et al., 2015; Sun et al., 2015; Yang et al., 2014). Therefore, total material requirements and absorber sizes are lower than PWS (Muñoz et al., 2015). However, organic solvent and amine solutions can be expensive, and several heating/cooling steps are needed to absorb/desorb dissolved CO2, H2S, and CH4 (Bauer et al., 2013; Muñoz et al., 2015) (Table

2.1).

Pressurized swing adsorption (PSA) uses adsorbents (zeolite, carbon molecular sieve, silica gel, activated carbon) that have pore sizes (3.7 Å) designed to capture CO2 (2.8 Å), O2

(2.8 Å), and N2 (3.0 Å), but not CH4 (4.0 Å). PSA has three process steps: 1) pressurized adsorption (700-800 kPa); 2) depressurization (100 kPa); 3) desorption (0 kPa); and 4) pressure build up (Yang et al., 2014). The main advantage of PSA is that it can remove CO2,

O2 and N2. In fact, PSA is used extensively at landfills because landfill biogas can have high

N2 (1-17%) and O2 (1-5%) levels (USEPA, 2015; Yang et al., 2014). The disadvantage of

PSA is that H2S/NH3 must be removed beforehand because these “sticky” gases irreversibly adsorb to the adsorbents, lowering gas removal efficiencies (Yang et al., 2014) (Table 2.1).

Membrane permeation has garnered recent interest because membranes can be used to selectively remove CO2, O2, N2, H2S and H2O from biogas while retaining the majority of

CH4 (Yang et al., 2014). Membrane permeation is simple to operate, requires little maintenance, and has low energy requirements. However, membranes can be costly and the technology needs further optimization to attain comparable CH4 purity as conventional methods (Yang et al., 2014). This could be solved by integrating membrane permeation with traditional cleaning technologies (i.e. amine absorption, PWS) (Scholz et al., 2013).

Cryogenic biogas cleaning is the progressive cooling of raw biogas under pressure to remove CO2 (boiling point= -78.5°C) and then CH4 (boiling point= -161°C) (Yang et al.,

2014). The process is very efficient and purified CO2 is produced as a byproduct, but it is also very energy intensive (Table 2.1) (Sun et al., 2015; Yang et al., 2014). Other technologies such as air exposure in AD for in-situ H2S/CO2 removal, biological H2S removal using biofilters, and biological CO2 removal via algae/methanogens have promising laboratory/pilot scale results, but have not been widely adopted at commercial scale (Iranpour et al., 2005; Muñoz et al., 2015).

Table 2.1 compares the advantages, disadvantages, costs, and energy demands of the most popular biogas cleaning technologies. In general, capital and operational costs are fairly similar and are dependent on the scale of operation (Bauer et al., 2013). PWS has been implemented at most biogas production facilities because it is simple, well understood, and does not require expensive chemicals. Low cost, environmentally friendly organic chemicals/amines that have good heat transfer properties could enhance the adoption of chemical absorption technologies. PSA techniques will continue to be popular because no other technology can be used to remove O2 and N2 at high efficiency. Development of low cost, highly selective membranes is an appealing area of research that has great potential to improve the viability of membrane permeation (Deng and Hägg, 2010; Scholz et al., 2013).

2.3.2. Biogas uses

Conventional uses of biogas include: 1) combustion in gas engines, turbines, boilers, and flares; 2) upgrading to purified biogas (>97% CH4) that can be injected to natural gas pipelines; 3) upgrading to compressed natural gas (Bio-CNG); and 4) upgrading to LBG

(Muñoz et al., 2015; Yang et al., 2014). Combustion is the most common end-use application for biogas. In fact, about 75% of the 632 landfill gas projects in the U.S. EPA’s Landfill

Methane Outreach Program use biogas to generate electricity and/or heat in combustion engines, gas turbines, microturbines, and combined heat and power units (CHP). About 19% of landfill gas projects combust biogas directly for heat and the remaining 6% purify biogas for pipeline injection (USEPA, 2016c). Nearly 50% of the farm-based AD systems in the

U.S. EPA AgSTAR program use CHP to generate electricity and recover waste heat, 34% use biogas strictly for electricity, 7% use biogas as a boiler/furnace fuel, and 6% flare the biogas (3% are unknown) (USEPA, 2017). Yang and Ge (2016) estimated that there were 14 biogas production systems (landfills or AD) that compress (200-250 atm) cleaned biogas to

Bio-CNG. There are only a few biogas production sites in the U.S. that employ cryogenic/liquefaction processes (< -161°C) to generate LBG (USEPA, 2016c; Yang et al.,

2014; Yang and Ge, 2016).

The primary reason why biogas is mostly used as a source of heat/electricity is because combustion equipment is well optimized and relatively inexpensive, and there are minimal biogas cleaning (i.e. H2S removal) requirements as compared to other biogas utilization methods (Table 2.2) (Yang and Ge, 2016). In fact, high upgrading costs and low natural gas prices ($3-5/thousand ft3 industrial price) have made it very difficult to implement biogas-based CH4 for grid injection or transportation fuels (Bio-CNG, LBG) (Table 2.2)

(USEIA, 2017a). Policy incentives such as the Renewable Fuel Standard from USEPA and

Rural Energy for America Program (REAP)/Environmental Quality Incentives Program

(EQIP) from USDA could help lower the cost of biogas production (USDA et al., 2015).

Still, concerns about conventional biogas upgrading, such as cleaning and compression costs,

15 have sparked interest in thermochemical/biological processes to convert biogas to flexible liquid chemicals such as methanol (Blug et al., 2014; Ge et al., 2014; Yang et al., 2014).

2.3.3. Methanol production via thermochemical methods

Methanol (CH3OH) is a commodity chemical (>$25 billion market, market price

≈$400/ton) that can be used directly as a fuel or can be converted to other chemicals such as acetic acid, formaldehyde, MTBE, DME, olefins, and gasoline (Da Silva, 2016; Riaz et al.,

2013). Most methanol is produced from natural gas in a multi-step process that consists of: 1) natural gas cleaning/purification; 2) reforming of natural gas to syngas (CO+H2+CO2); 3) syngas to methanol conversion; and 4) methanol purification (Figure 2.1).

Nearly 75% of methanol plants produce syngas via steam-CH4 reforming (700-

1000°C, 5-30 atm) (CH4+H2O↔3H2+CO, ΔHrxn,298K=+206 kJ/mol) over nickel/alumina

(Ni/Al2O3)-based catalysts (Da Silva, 2016; Riaz et al., 2013; Summers, 2014). The endothermic steam-reforming reaction step is energy intensive, but highly efficient (>90% conversion) (Riaz et al., 2013; Wernicke et al., 2014). Syngas can then be cooled (150-

400°C) and combined with steam to control the stoichiometric number (SN=(H2-CO2)/

(CO+CO2)) of syngas via the water gas shift reaction (CO+H2O↔CO2+H2, ΔHrxn,298K=-41 kJ/mol, Cu/Zn, Cu/Fe/Cr catalysts) (Riaz et al., 2013; Zhang et al., 2017). Conditioned syngas is cooled/compressed (200-300°C, 50-150 bar) and sent to a syngas to methanol conversion reactor (Cu/ZnO/Al2O3 catalysts) (Riaz et al., 2013; Yang and Ge, 2016; Zhang et al., 2017). In the methanol conversion reactor, two exothermic syngas to methanol reactions

(CO+2H2↔CH3OH ΔHrxn,298K=-91 kJ/mol; CO2+3H2↔CH3OH+H2O ΔHrxn,298K=-50 kJ/mol) and the water-gas shift reaction (CO+H2O↔CO2+H2 ΔHrxn,298K=-41 kJ/mol) occur. The

16 reactions are equilibrium limited, and typical per-pass conversions are low (5-25%)

(Wernicke et al., 2014; Yang and Ge, 2016). Therefore, syngas is usually recycled (up to

96% recycle ratio) to improve overall conversion (Blug et al., 2014; Jones and Zhu, 2009;

Summers, 2014). Additionally, the methanol production reactions generate significant process heat, so large quantities of cooling/chilled water are needed. The crude product contains methanol (60-90%), water (10%-35%) and dissolved gases (CO2, H2S, CO, H2; ppm level). Dissolved gases are flashed off at low pressure and methanol is purified by distillation

(Riaz et al., 2013; Wernicke et al., 2014; Zhang et al., 2017).

Overall, the multi-step process to convert natural gas to methanol has high carbon yields (70-75%) and can be scaled up to >5,000 tons per day (Bertau et al., 2014; Da Silva,

2016). However, the process requires a CH4 source practically free of impurities (i.e. H2S), there are major utility requirements (i.e. steam, cooling water), and heat integration networks need to be optimized to lower costs. Economies of scale are needed, and methanol production costs are tied to the price of natural gas (Blug et al., 2014). Syngas can also be generated through the gasification of other carbonaceous feedstocks, such as biomass and coal, and via dry reforming of natural gas (CH4+CO2↔2CO+2H2 ΔHrxn,298K=247 kJ/mol, 700-900°C)

(Yang and Ge, 2016).

To bypass the syngas generation step, which is estimated to contribute 60-70% of total methanol processing costs, partial oxidative conversion of CH4 to methanol

(CH4+0.5O2→CH3OH, ΔHrxn,298K=-127 kJ/mol, homogeneous/heterogeneous catalysts) has been explored (Karakaya and Kee, 2016; Kondratenko et al., 2017; Riaz et al., 2013; Zakaria and Kamarudin, 2016). However, partial oxidation operates at high temperatures and pressures (30-200 bar, 200-500°C) and CH4 conversion (<20%) and methanol selectivity

(<30%) are low. Low selectivity is observed because methanol is more reactive than CH4 (C-

H bond strength for methanol=389 kJ/mol, CH4=440 kJ/mol) and is often oxidized completely to CO2 (Da Silva, 2016; Kondratenko et al., 2017; Soussan et al., 2016; Zakaria and Kamarudin, 2016). Catalytic hydrogenation of CO2 in biogas to methanol

(CO2+3H2↔CH3OH+H2O ΔHrxn,298K=-50 kJ/mol) could also be used. High H2 requirement is a major issue with this process, but if renewable energy were used to electrolyze H2O to H2, the technology could be a sustainable method to “store” renewable electricity (i.e. solar, wind) and CO2 (Blug et al., 2014; Kim et al., 2011). Photo-catalysis of light, water, and CH4 to methanol is another emerging option, but the process is at laboratory scale, has low yields, and requires expensive catalysts and complicated reactor designs (Zakaria and Kamarudin,

2016). These emerging thermochemical/photocatalytic technologies for direct conversion of biogas components (CH4/CO2) to methanol are highly attractive, but require significant advances in catalyst development, reactor design, and process optimization to attain carbon conversion efficiencies and costs that are comparable to those for the multi-step, syngas- based methanol production process (Blug et al., 2014; Zakaria and Kamarudin, 2016).

2.3.4. Biological conversion of methane to methanol

Biological conversion of CH4 to methanol applies the capability of the methane monooxygenase (MMO) enzyme to insert one oxygen atom into the CH4 molecule at physiological temperatures (Blanchette et al., 2016; Ge et al., 2014; Soussan et al., 2016).

The enzyme has high selectivity towards methanol, operates under mild conditions, and can directly convert CH4 to methanol (Soussan et al., 2016). Therefore, biological conversion is expected to have lower energy demands, lower capital costs, and fewer environmental

18 emissions compared to thermochemical methods (Ge et al., 2014; Soussan et al., 2016). The fact that MMOs can convert CH4 to methanol under mild conditions has led to considerable efforts to isolate and use MMOs as biocatalysts, or mimic its properties using novel chemical catalysts (Kondratenko et al., 2017; Soussan et al., 2016; V. C. Wang et al., 2016). However,

MMOs are notoriously difficult to isolate, immobilize and retain activity, and bio-inspired chemical catalysts are costly and have low yields and low selectivity (Kondratenko et al.,

2017; Soussan et al., 2016). Recently, Blanchette et al. (2016) isolated and immobilized

MMOs in 3-D printed polyethylene glycol diacrylate hydrogels and retained about 100% of

MMO activity. These results are very promising, but large scale production, extraction, and immobilization of enzymes could be costly.

Whole cell biocatalysis of CH4 to methanol is advantageous because there is no need for enzyme extraction and cofactors (i.e. NADH+H) can be generated from co-substrates (i.e. formate) via metabolic processes (Soussan et al., 2016) (Section 2.4). The downsides of whole cell biocatalysis are that methanol is often overoxidized and that sterilization is often needed to maintain strain homogeneity (Blanchette et al., 2016; Ge et al., 2014; Soussan et al., 2016). Ammonia oxidizing bacteria (AOB) can also convert CH4 to methanol, but methanol production rates are much lower than from methanotrophs (Ge et al., 2014; Taher and Chandran, 2013). Thus, most studies use live methanotroph cells for the conversion of

CH4 to methanol (Soussan et al., 2016) (Section 2.4).

2.4. Methanotrophs

2.4.1. Microbiology and biochemistry

Methanotrophs are gram negative bacteria that use CH4 as a primary source of carbon and energy (Ge et al., 2014; Hanson and Hanson, 1996). Methanotrophs are affiliated with four major taxonomic groups: (1) Class Gammaproteobacteria; (2) Class

Alphaproteobacteria; (3) Phylum Verrucomicrobia; and (4) Candidate phylum NC10

(Chistoserdova and Lidstrom, 2013). Methanotrophs have been isolated from many environments (i.e. soil, freshwater, saltwater, wastewater) and have a wide range of growth preferences (pH 1-10, 0-72°C, 0-15% NaCl) (Semrau et al., 2010). Although anaerobic methanotrophs have been identified, they have never been isolated (Haynes and Gonzalez,

2014). Consequently, aerobic methanotrophs are employed for most biotechnological applications (Lawton and Rosenzweig, 2016). In general, aerobic methanotrophs have the same catabolic pathway for oxidation of CH4 to CO2, which is shown in Figure 2.2.

In the first step of degradative metabolism, CH4 and O2 are converted to methanol

+ - and H2O by MMO according to the reaction: CH4 + O2 + 2H +2e  CH3OH + H2O (Ge et al., 2014; Hanson and Hanson, 1996; Lawton and Rosenzweig, 2016) (Figure 2.2). MMO exists in the cytoplasm in a soluble form (sMMO) or as a membrane bound particulate form

(pMMO) (Sirajuddin and Rosenzweig, 2015). sMMO is a diiron (Fe-Fe) containing enzyme that uses NADH+H+ as an electron donor. pMMO is a copper containing enzyme. The physiological electron donor for pMMO is not known, but could possibly be ubiquinol

(Kalyuzhnaya et al., 2015). Nearly every isolated methanotroph produces pMMO, some strains produce both pMMO and sMMO (Methylococcus capsulatus Bath, Methylosinus

20 trichosporium OB3b), and few only produce sMMO (Methylocella sp.) (Dedysh and

Dunfield, 2011; Smith et al., 2010). sMMO can oxidize a variety of aromatic and aliphatic hydrocarbons, while pMMO can oxidize a restricted number of alkanes and alkenes (Soussan et al., 2016). Although methanotrophs with pMMO have lower CH4 uptake rates (2.5-9.0 mmol CH4/g dry cell weight (DCW)/h) than those with sMMO (21.8 mmol CH4/g DCW/h), pMMO is expected to have higher affinity for CH4 (KCH4=8.3-62 μM) than sMMO (KCH4=92

μM) (Lawton and Rosenzweig, 2016). Electrons for the initial oxidation of CH4 to methanol are generated by downstream enzymes.

The methanol produced via MMO is oxidized to formaldehyde by the periplasmic

+ - methanol dehydrogenase (MDH) according to the reaction: CH3OH HCHO + 2H +2e

(Chistoserdova and Lidstrom, 2013). The electrons generated by MDH can be transferred via cytochrome and terminal oxidase to O2 in order to generate a proton gradient needed for ATP synthesis, substrate transport and/or cell motility (Trotsenko and Murrell, 2008). Some have also hypothesized that MDH is structurally coupled with pMMO, and the electrons generated by methanol oxidation are immediately used by pMMO for CH4 oxidation (Kalyuzhnaya et al., 2015; Lawton and Rosenzweig, 2016). Some methanotrophs can also use methanol as their sole carbon substrate.

Formaldehyde can be used for cell biosynthesis or can be further oxidized to formate to generate reducing equivalents. Most aerobic methanotrophs assimilate carbon by two pathways: 1) the Ribulose Monophosphate (RuMP) cycle or 2) the Serine Cycle. Generally, the methanotrophs grouped in Gammaproteobacteria (Type I and Type X methanotrophs) use the RuMP cycle and the methanotrophs grouped in Alphaproteobacteria (Type II, facultative methanotrophs) and some Type X methanotrophs use the Serine Cycle

(Chistoserdova and Lidstrom, 2013; Dedysh and Dunfield, 2011; Semrau et al., 2010). Some methanotrophs assimilate carbon at the level of CO2 via the Calvin Benson-Bassham Cycle

(CBB) (Rasigraf et al., 2014). The oxidation of formaldehyde to formate is conducted by either a NADH-dependent formaldehyde dehydrogenase (FaDH) or a tetrahydromethanopterin (H4MPT) dependent pathway (Chistoserdova and Lidstrom, 2013;

Fei et al., 2014; Hanson and Hanson, 1996; Smith et al., 2010).

Formate can either be oxidized to CO2 or converted to methyl tetrahydrofolate

(CH2=H4F) for biosynthesis via the Serine pathway (Fei et al., 2014). Oxidation is carried out

+ by formate dehydrogenase (FDH) according to the reaction: HCOOH+NAD  CO2+

NADH+H+ (Smith et al., 2010). Methanotrophs have low activity of NADH oxidase,

+ indicating the NADH+H produced from formate oxidation is primarily used for CH4 oxidation by MMO. In fact, addition of exogenous formate to methanotroph growth medium causes methanol accumulation (Ge et al., 2014; Smith et al., 2010) (Figure 2.2, Figure 2.3).

2.4.2. Methanol production

The most common strategy to convert CH4 to methanol via whole methanotroph cells is shown in Figure 2.3. First, methanotrophs are cultivated in nitrate mineral salts medium

(NMS) and centrifuged to reach a desired cell density. Then, chemicals are added to inhibit methanol oxidation by MDH. However, this cuts off the enzymatic reactions that produce electrons for CH4 oxidation by MMO. Therefore, exogenous formate is supplied to generate electrons via FDH. Adding an MDH inhibitor and formate enables methanol accumulation

(Ge et al., 2014; Soussan et al., 2016).

Several chemicals, such as chelating agents (EDTA, 0.05-1.0 mM), NaCl (100-200 mM), cyclopropanol (0.07 mM), phosphate buffer (40-400 mM), NH4Cl (40 mM), and

MgCl2 (5-20 mM), have been used inhibit MDH (Ge et al., 2014). Chelating agents inhibit

MDH by removing Ca2+, monovalent cations can inhibit electron transfer, and cyclopropanol impacts MDH structure/electron transfer (Ge et al., 2014). Most studies attempted to optimize MDH inhibitor and/or formate concentrations (15-100 mM) to convert purified CH4

(>99% CH4) to methanol using Methylosinus trichosporium OB3b at mild temperatures (30-

37°C), atmospheric pressure, and neutral pH (6.0-7.0) (Table 2.3). Although some medium formulations were used in several studies (Furuto et al., 1999; Kim et al., 2010; Pen et al.,

2014; Takeguchi et al., 1997), an optimal medium has not been identified (Table 2.3).

Phosphate was used in nearly every study, indicating that it is an effective MDH inhibitor

(20%-100% MDH activity inhibition) (Han et al., 2013; S. K. S. Patel et al., 2016c; Yoo et al., 2015). However, MDH inhibitors can also inhibit MMO activity (up to 80% inhibition)

(Takeguchi et al., 1997; Yoo et al., 2015). Clearly, media need to be optimized to inhibit

MDH and retain MMO activity.

Cell density was an important factor for methanol production. For example, Duan et al. (2011) achieved the highest methanol yield (1.1 g/L) at 17 g DCW/L (Table 2.3), presumably because higher cell concentrations increased the CH4 oxidation rate (Kim et al.,

2010; Yoon et al., 2009). Mehta et al. (1991) and S. Patel et al. (2016c) maintained high cell densities by immobilizing methanotrophs in DEAE-cellulose and Chitosan, respectively. In fact, the methanol productivity obtained by Mehta et al. (1991) (4.1 g/L/d) is still the highest reported in the literature, and S.K.S. Patel et al (2016c) showed that the reusability and performance of immobilized cells were better than free cell systems. Membrane bioreactors

23 that can improve mass transfer of gaseous substrates (CH4, O2) have also been applied to improve process safety and enhance methanol productivities. For example, Duan et al. (2011) achieved a high methanol yield (>0.9 g/L) and Pen et al. (2014) had among the highest cell- based methanol productivities (75±25 mg methanol/g cells/h) reported (Soussan et al., 2016).

The highest conversion efficiencies (mol methanol produced per mol of CH4 oxidized) were obtained by Yoo et al. (2015) (90%), Han et al. (2013) (80%), Takeguchi et al. (1997) (61%) and Duan et al. (2011) (60%) (Table 2.3). The mild conditions and high conversion efficiency via biological conversion are advantageous compared to partial oxidative thermochemical approaches (Section 2.3.3). Low final methanol concentration is a major disadvantage for biological conversion and it is commonly attributed to the fact that methanol is toxic to methanotrophs. This indicates that strains need to be developed with high methanol tolerance (Kim et al., 2010; Pen et al., 2014; Soussan et al., 2016). Previously, Best and Higgins (1981) used adaptive evolution to improve the methanol tolerance of M. trichosporium OB3b up to ~30 g/L, indicating higher methanol concentrations can be attained.

The cost of formate is currently too high (>$500/ton) for commercial applications of bioconversion of CH4-to-methanol (Yishai et al., 2016). Xin et al. (2007) indicated intracellular polyhydroxybutyrate (PHB) could be an alternative electron donor for methanol production. However, PHB is currently being evaluated as a higher value methanotroph- based product (Section 2.4.3), so it will not likely be used for methanol production. S.K.S.

Patel et al (2016a) and Blanchette et al. (2016) suggested that exogenous H2 could be an alternative electron donor because it can be mixed with biogas and H2 is not a carbon source, so it will not attract other microorganisms. The challenge with H2 is that an O2 tolerant

24 hydrogenase is needed (Blanchette et al., 2016) and the solubility of H2 in water is even lower than CH4 (Cooper and Alley, 2011). Some methanotrophs with sMMO also have a

“peroxide shunt” in their natural metabolism, meaning H2O2 could also be used for electron generation. However, the catalytic efficiency of the peroxide shunt is low and the hydroxylase component of sMMO is inactivated by oxidative degradation (Soussan et al.,

2016).

Electro-microbial conversion has been used to generate formate for biofuel production. For example, Li et al. (2012) engineered a system to electrochemically convert

CO2 to formate that was used by genetically modified Ralstonia cells to produce more than

140 mg L-1 of biofuels (butanol, 3-methyl-1-butanol). Similarly, Reda et al. (2008) used a tungsten-containing FDH enzyme from Syntrophobacter fumaroxidans to electrochemically produce formate from CO2 at high thermodynamic efficiencies (73.3-96.6%). However, genetic modification of methanotrophs to become “electrophilic” is needed before electromicrobial conversion of CH4 to methanol is a possibility (Ge et al., 2014).

Finally, Ge et al. (2014) proposed that facultative methanotrophs (Type II methanotrophs: Methylocella, Methylocystis, Methylocapsa) have the metabolic potential to use acetate as an electron donor. Thus, wastewater that contains acetate (i.e. from AD) could potentially be used as an electron donor source for CH4 to methanol conversion. However, wild-type facultative methanotrophs do not consume acetate and CH4 simultaneously, indicating genetic modification is required (Dedysh et al., 2005; Ge et al., 2014).

The easiest and fastest route to lower the costs of electron donors may be to lower the cost of formate. Recently, Yishai et al. (2016) mentioned that catalytic hydrogenation of CO2 to formate, photo-reduction of CO2 to formate, selective oxidation of biomass to formate, and

25 electrochemical reduction of CO2 and H2O to formate as technologies to lower formate production costs. Out of those technologies, electrochemical reduction of CO2 and H2O to formate is the lowest cost option. In fact, it was estimated that the cost of formate could be reduced to $200/MT if off-peak electricity and concentrated CO2 emissions from power plants were used (Yishai et al., 2016). This process is similar to CO2 hydrogenation to methanol (Section 2.3.3), because renewable electricity and waste CO2 could be “stored” as formate (Yishai et al., 2016).

Several research gaps still exist for the methanotrophic conversion of CH4 to methanol. Until recently, no studies used biogas from a commercial AD facility as a substrate for methanol production (S. K. S. Patel et al., 2016a; Su et al., 2017; W. Zhang et al., 2016).

Methanotrophs isolated from AD that can convert biogas directly to methanol could lower production costs because CO2 removal would not be necessary. Secondly, most studies sterilized reaction equipment prior to CH4 to methanol conversion. Non-sterile, methanotrophic consortia could offer enhanced stability and lower production costs (Han et al., 2013; Su et al., 2017). Thirdly, few studies used reactors designed for enhanced gas- liquid mass transfer to increase methanol production rates (Duan et al., 2011; Pen et al.,

2014). Finally, no studies used mathematical modeling and techno-economic analysis to guide reactor design and identify bottlenecks that hinder commercialization.

2.4.3. Other biotechnological applications

The recovery of abundant and low cost natural gas in shale formations and the glut of uncaptured CH4 from anthropogenic sources (leaking gas wells, landfills, manure management) has rekindled interest in the use of methanotrophs for biological conversion of

CH4 to value-added products (Kalyuzhnaya, 2016; Strong et al., 2016, 2015). The most successful commercial application of methanotrophs is a process for single-celled-protein

(SCP) production from Methylococcus capsulatus cultures (Strong et al., 2015). In fact, companies such as Unibio A/S (Denmark, www.unibio.dk) and Calysta, Inc. (CA, USA, http://calysta.com/) are working to commercialize the technology. However, bacterial SCP has high nucleic acid content and needs to be pretreated via hydrolysis/heat treatment to be suitable for human and animal consumption (Strong et al., 2015). Biodegradable polyhydroxyalkanoates (PHAs), such as polyhydroxybutyrate (PHB), can be produced from methanotrophs that use the Serine cycle metabolism (Section 2.4.1) (Strong et al., 2015). A few companies (Newlight Technologies (https://www.newlight.com/), CA, USA; Mango

Materials (http://mangomaterials.com/), CA, USA) are trying to scale this process, but there are several issues that must be overcome. Similar to the methanol process, PHB is non- growth associated because methanotroph cells need to be produced first, then nutrient-deplete medium is added to induce PHB production. Then, expensive chemicals are needed to extract

PHB from biomass. The total production cost is high compared to petroleum based alternatives (i.e. poly-ethylene). Therefore, PHAs that can be used in biomedical applications

(i.e. drug delivery, tissue repair, medical devices) could have higher value than PHB (Strong et al., 2016).

Methanotroph lipids could also be used as a feedstock for biodiesel production (Fei et al., 2014). In fact, a preliminary economic analysis indicated that the cost of diesel could be as low as $0.7/gal at low natural gas prices ($100/ton), high methanotroph biomass yields (1 g DCW/g CH4), high lipid contents (50 g lipids/g DCW), and high extraction/hydrotreating yields (0.95 g/g) (Fei et al., 2014). However, the process is analogous to PHB and methanol

27 because biomass must be generated first, then lipids are produced in a nutrient-deplete medium. Additionally, methanotrophs produce phosphorus-based membrane lipids that are difficult and costly to extract (Fei et al., 2014; Strong et al., 2015). Membrane lipids may be better utilized as high value human health supplements (Strong et al., 2015).

Other high value products that can be produced from methanotrophs include ectoine

(cosmetic chemical that can be used as moisturizer), exopolysaccharides (used to control rheological properties of fluids), vitamins, and lactate (Henard et al., 2016; Strong et al.,

2016, 2015). Methanotrophs can also be used for epoxidation of propylene (Xin et al., 2003) and ethylene (Xin et al., 2017). Methanotrophic biofilters and biotrickling filters have also been explored as low cost technologies to oxidize dilute CH4 emissions (<2% CH4) from landfills (Estrada et al., 2014b; Yoon et al., 2009).

There are several methanotroph-based products and services that have potential for commercialization. However, the major barriers are the relatively low growth rates (0.1-0.4 h-

1) due to low MMO activities (2.5-21.8 mmol/g DCW/h), low carbon and energy efficiency, and the low solubility of CH4 and O2 in methanotroph growth medium (Lawton and

Rosenzweig, 2016; Strong et al., 2016). Growth rate and carbon/energy efficiency issues will most likely be solved via genetic modification of methanotrophs (Kalyuzhnaya et al., 2015;

Lawton and Rosenzweig, 2016). Advances in reactor design to improve gas-liquid mass transfer (Section 2.5) and techno-economic analysis (Section 2.6) could help identify and solve problems that hinder the commercial viability of methanotroph-based bioprocesses

(Strong et al., 2016).

2.5. Bioreactors for Methanotroph Cultivation

2.5.1. Gas-to-liquid transfer

Gas-liquid mass transfer can be described with two-film theory (Figure 2.4) (Cooper and Alley, 2011; Kraakman et al., 2011; Metcalf & Eddy Inc., 2003). The theory assumes that two laminar films (one gas (δG), one liquid (δL)) provide the primary resistance to transfer of gaseous molecules between a bulk gas and a bulk liquid phase that are uniform and well-mixed (Cooper and Alley, 2011; Metcalf & Eddy Inc., 2003) (Figure 2.4).

Assuming that the driving force for mass transfer is the gradient between the bulk (CB,G, CL,G) and interface (Ci,G, Ci,L) concentrations, mass flux for gas absorption at steady state can be described by Eq. 2.1 (Logan, 2012; Metcalf & Eddy Inc., 2003):

r=kG*(CB,G-Ci,G)=kL*(Ci,L-CB,L) (Eq. 2.1)

2 where r is the rate of mass transfer (mol/l /t), kG (DG/δG) and kL (DL/δL) are gas and liquid

2 film transfer coefficients (l/t), DG and DL are gas and liquid phase diffusivities (l /t), δG and

δL are the gas and liquid film thickness (l), CB,G and CL,G are the concentrations of the gas in

3 the bulk gas and bulk liquid phases (mol/l ), and Ci,G and Ci,L are interface concentrations

(mol/l3). When these assumptions are applicable, reducing the thickness of the film can enhance interphase mass transport (Metcalf & Eddy Inc., 2003).

Normally, film transfer coefficients cannot be measured and they do not always encompass all the factors and complexities involved with bioreactor operations, so empirical

“overall” coefficients (KG, KL) are often used (Kadic and Heindel, 2014; Metcalf & Eddy

Inc., 2003). In the case of relatively dilute, sparingly soluble gases (i.e. CH4, O2, CO2 in

H2O), the equilibrium concentration of the gas at the interface can be determined using

Henry’s Law (Ci,G/H=Ci,L) (Kraakman et al., 2011; Metcalf & Eddy Inc., 2003). Therefore,

Eq. 2.1 can be adapted to Eq. 2.2:

C r=K *(C -C )=K * ( i,G -C ) (Eq. 2.2) G B,G i,G L H B,L where H is the Henry’s Law coefficient (unitless). When Eq. 2.1 and Eq. 2.2 are combined, the following relationships can be obtained (Garcia-Ochoa and Gomez, 2009) (Eq. 2.3):

1 1 1 1 1 H = + ; = + (Eq. 2.3) KL kL H*kG KG kG kL

In the case for sparingly soluble gases such as CH4, O2, and CO2 in water, Henry’s constant is large, and the primary resistance to mass transfer is from the liquid film. In that case, the volumetric rate of transfer of a gas from the bulk gas phase to the bulk liquid phase (R, mol/l3/t) can be described by Eq. 2.4:

C R=K a ( i,G -C ) (Eq. 2.4) L H B,L

2 3 where a is the interfacial area for mass transfer per unit volume (l /l ). KLa can be determined with experiments, and its value is dependent on several factors (mixing equipment, gas/liquid velocities, reactor packing, etc.) (Kim and Deshusses, 2008a) (Section 2.5.2). Based on Eq.

2.4, mass transfer can be enhanced by increasing the interfacial area. In biological systems, suspended (i.e. cell membrane) and/or attached cells (i.e. biofilm) can also provide resistance to mass transfer. In those cases, an overall mass transfer coefficient ( 1 = 1 + 1 + 1 ) is Kov KL KG KB commonly used (KB=biomass resistance coefficient (l/t)) (Kraakman et al., 2011; Yang and

Ge, 2016). Furthermore, an effectiveness factor (EF) can be multiplied to the abiotic mass transfer coefficient to describe the impact that gas-consuming cells have on mass transfer

(Garcia-Ochoa and Gomez, 2009).

2.5.2. Bioreactors for enhanced gas-liquid mass transfer

The most common type of bioreactor is the stirred tank reactor (STR, CSTR) (Kadic and Heindel, 2014). STRs are cylindrical reactors with low aspect ratios (H/D<4) and high liquid holdups (liquid volume/reactor volume, 0.7-0.8) (Kadic and Heindel, 2014). Gas is supplied in the headspace or through spargers in the bottom of the STR. Mass transfer is enhanced via mechanical agitation using impellers/stirrers and/or fine bubble spargers

(Garcia-Ochoa and Gomez, 2009; Kadic and Heindel, 2014). KLa in STRs is a function of the stirrer speed, superficial gas velocity, sparger design (i.e. bubble size), reactor geometry, and fluid properties (Garcia-Ochoa and Gomez, 2009; Shuler and Kargi, 2002). However, STRs have high power demands, and improper impeller designs can cause mixing dead zones. In fact, STRs have fallen out of favor for gas fermentations because competing reactors (bubble column/air lift, membrane, and trickle bed) have much lower energy demands (Liew et al.,

2016).

Bubble columns are cylindrical reactors with high aspect ratio (H/D>5) and high liquid holdup (0.7-0.8) that rely on the injection of compressed gas to provide turbulence and enhance gas-liquid mass transfer (Garcia-Ochoa and Gomez, 2009; Kadic and Heindel,

2014). KLa in bubble columns is a function of the superficial gas velocity, sparger characteristics, and fluid properties (Garcia-Ochoa and Gomez, 2009). Although they have lower power demands than STRs, the hydrodynamics of bubble columns is complex and

31 back-mixing is often an issue. Baffles can be added to better control flow (i.e. airlift reactors) and micro-spargers can be used to increase the interfacial surface area of bubbles (Kadic and

Heindel, 2014). The U-loop fermenter, which was developed for methanotroph-based SCP production, combines the advantages of STRs and bubble columns. Gas and liquid are injected into a vertical U-shaped pipe that is fitted with static mixers. The combination of mechanical agitation and gas turbulence increases mass transfer (Petersen et al., 2016). In fact, Petersen et al. (2016) showed that the U-loop design provided higher mass transfer coefficients than STRs or other tubular loop reactors at modest volumetric liquid and gas flow rates.

Membrane bioreactors (MBR) are an attractive technology for methanotroph cultivation (Shen et al., 2014; Yang and Ge, 2016). In a MBR, the gas and liquid phases are separated by macroporous membranes (Pen et al., 2014). Gases are supplied to the membrane interior, then diffuse to the exterior of the membrane where attached (biofilm) and/or suspended cells are located. A liquid phase that contains nutrients is supplied on the biofilm side (Syron and Casey, 2008). Membranes are designed with high specific surface area to improve the rate of gas-liquid mass transport. The major advantages of MBRs are high gas utilization efficiency, and good process safety because flammable gases (i.e. CH4, O2) can be added separately when multiple membranes are used (Pen et al., 2014). The disadvantages of

MBRs are membrane fouling, high membrane costs, and that mass transfer is often limited by diffusion of gases through the membrane (i.e. membrane design is critical) (Kadic and

Heindel, 2014; Syron and Casey, 2008). Still, membrane costs are reducing every year, some configurations were shown to have enhanced mass transfer properties compared to

STRs/TBRs, and MBRs have been applied for syngas fermentation and CH4 oxidation via

32 methanotrophs (Judd, 2008; Orgill et al., 2013; Pen et al., 2014; Rishell et al., 2004; Shen et al., 2014).

Another approach is the use of hydrophobic compounds (i.e. paraffin wax, silicone oil) that have higher CH4 affinity than does water (Cantera et al., 2016; Duan et al., 2011;

Kraakman et al., 2011; Myung et al., 2016). Although mass transfer rates can be vastly improved using hydrophobic compounds (Myung et al., 2016), separation of products from the fluid is difficult and costly. Additionally, methanotrophs need to be adapted to the medium beforehand (Cantera et al., 2016; Myung et al., 2016). Recently, Schulte et al. (2016) coated a bio-composite material (i.e. cellulose paper) with pre-grown, dewatered Clostridium ljungdahlii OTA1 cells for syngas fermentation (Schulte et al., 2016). They showed that CO absorption rates were improved by 2.5-fold and power demands were reduced by 97% as compared to suspended cell systems because the bio-composite materials had low thicknesses and high specific surface areas. Bio-composite coatings for CH4 biocatalysis should be investigated.

2.5.3. Trickle-bed reactors

Trickle bed reactors (TBRs) are three phase (gas, liquid, solid) reactors that use packing materials with high specific surface to increase the rate of gas-liquid mass transport

(Green and Perry, 2008; Ranade et al., 2011). Originally developed for biological wastewater treatment, TBRs also are used extensively for catalytic gas-liquid reactions in the chemical industry such as hydrogenation and hydrodesulfurization (Ranade et al., 2011). There is a growing interest in TBR technology for the production of biochemicals, such as ethanol via

33 syngas fermentation, and for various methanotrophic bioprocesses (Cáceres et al., 2016;

Estrada et al., 2014b; Kadic and Heindel, 2014; Yoon et al., 2009).

Trickle-bed bioreactors are cylindrical reactors with high aspect ratios (H/D>5) packed with an inert material that has high specific surface area (Kadic and Heindel, 2014).

Common packing materials include ceramic or plastic spheres, porous foams, and packings normally used in absorption/separation equipment (i.e. Raschig rings, Pall rings saddle rings)

(Estrada et al., 2014b; Kadic and Heindel, 2014; Kim and Deshusses, 2008a). Liquid with nutrients and/or cell inoculum are supplied at the top of TBRs, and as it flows downward, a thin film develops that has low resistance to gas-liquid mass transfer (Devarapalli et al.,

2016). Gases can be supplied co-current (same direction) or counter-current (opposite direction) to the liquid phase (Devarapalli et al., 2016). Gases that are transferred to the liquid phase are consumed by cells that are either attached to the inert solid material or suspended in the liquid phase (Devarapalli et al., 2016; Iliuta and Larachi, 2006). The presence of the solid packing material lowers the liquid holdup in TBRs (0.1-0.3 vol liq/vol reactor) compared to other bioreactor designs (Green and Perry, 2008; Ranade et al., 2011). However, TBRs have been shown to have enhanced mass transfer properties and much lower power requirements as compared to STRs (Orgill et al., 2013). The abiotic KLa for O2 in TBRs is mostly controlled by packing material properties (i.e. specific surface area, porosity), the liquid velocity, and fluid properties (Kim and Deshusses, 2008a; Orgill et al., 2013). The major disadvantages of TBRs are gas pressure drop caused by packing material, difficulty to control biofilm formation, liquid clogging, incomplete wetting, and complicated gas/liquid hydrodynamics (Deshusses and Cox, 2002; Devarapalli et al., 2016; Kadic and Heindel,

2014; Kraakman et al., 2011; Ranade et al., 2011).

The most common application of a methanotrophic TBR is the continuous biotrickling filter (Cantera et al., 2016; Estrada et al., 2014b; Yoon et al., 2009). The goal of methanotrophic biotrickling filters is to oxidize CH4 emissions (<2% CH4) that cannot be cost-effectively controlled using gas collection/flaring (Cáceres et al., 2016; Yoon et al.,

2009). Methanotrophic BTFs have very good CH4 removal efficiencies (90-100%) when packing materials with high specific surface areas, such as polyurethane foams or polyethylene rings are used (Cáceres et al., 2016; Estrada et al., 2014b; Yoon et al., 2009). A major downside to methanotrophic BTFs is that they are often mass transfer limited at the

3 low gas flow rates expected from dilute CH4 sources, such as old landfills (10-15 m /h) (Kim et al., 2013). Additionally, BTFs are strictly a treatment technology to reduce GHG emissions. Nonetheless, the design approaches for BTFs could be applied to develop TBRs for the conversion of CH4 to value-added products. In fact, Criddle et al (2012) have a patent application to use a biofilm reactor to grow methanotrophs for PHA production (Criddle et al., 2012), and their research group has applied fluidized bed reactors for PHB production

(Pfluger et al., 2011). However, TBRs have never been used to biologically convert biogas to methanol.

2.5.4. Modeling of trickle-bed bioreactors

Mathematical modeling can reduce the costs of experimentation, provide quantitative information on variables that are difficult to measure, and allow rapid evaluation of process parameter selection on TBR performance (Deshusses and Shareefdeen, 2005). In fact, modeling has been used to better understand and control hydrodynamics, mass transfer, heat transfer, and reactions in TBRs (Deshusses and Shareefdeen, 2005; Devinny and Ramesh,

2005; Ranade et al., 2011). The general approach to model TBRs is to develop and solve for component balances for the gas, liquid, and (sometimes) biomass phases (Devinny and

Ramesh, 2005; Iliuta and Larachi, 2006). For dilute species, the mass transport in a phase can be described by Eq. 2.5 and 2.6, respectively (COMSOL Multiphysics, 2017).

Ni=-Di∇ci+ci*u (Eq. 2.5)

∂c i +∇∙N =R (Eq. 2.6) ∂t i i

2 where Di is the diffusivity (l /t) of a chemical species in a phase (i), ∇ is the divergence, ci is

3 the concentration of a species in a phase (mol/l ), u is the velocity (l/t), and Ri is the reaction

3 term (mol/l /t). Ri can represent mass transport into/out of a phase (i.e. gas phase transfer to liquid phase), or can be used to describe chemical reactions (i.e. biological kinetics) (López et al., 2016; Z. Wang et al., 2016).

Reactor geometry is often simplified to one dimension, such as reactor length, and is discretized into finite elements. Parameter values (i.e. diffusivity, rate equations, mass transfer relations) and boundary conditions (i.e. inlet concentrations) are then used to solve the mass transport equations (Cáceres et al., 2016; Deshusses and Shareefdeen, 2005; López et al., 2016; Yoon et al., 2009). Equations are solved using numerical methods in software programs such as MATLAB and COMSOL Multiphysics. In COMSOL Multiphysics, Eq.

2.5 and 2.6 can also be adapted to account for “concentrated species”, in which parameters and variables can change based on differences in properties such as gas density, molecular weight, and temperature (COMSOL Multiphysics, 2015). There is a breadth of modeling research for biotrickling filters and chemical TBRs, but there are few studies that used gas- liquid transport/reaction kinetics to better understand gas fermentation processes (Chen et al.,

2015). Modeling of biogas to methanol conversion in TBRs could be a valuable tool to identify and evaluate the impacts of commercially relevant operational parameters on reactor performance.

2.6. Techno-economic Analysis

Techno-economic analysis (TEA) is a method that combines technical and economic aspects of a project to assess its feasibility for implementation (Swanson et al., 2010).

Technical feasibility includes an assessment of market availability, risk, operational requirements (labor, maintenance, material) and ease of installation (Shah et al., 2016).

Economic evaluation is the attempt to estimate monetary values, including capital and operational costs, revenues, and expected profitability (Shah et al., 2016). TEA can be broken down into four main steps: 1) design a theoretical schematic/framework of the process; 2) solve for material and energy balances; 3) estimate project costs based on parameter assumptions and material/energy balance results; and 4) analyze the sensitivity of the techno- economic model to process and cost assumptions (Baral and Shah, 2016a; Shah et al., 2016).

In general, TEA can be used to estimate bioenergy process costs to about ±20% accuracy

(Brown and Brown, 2014; Swanson et al., 2010).

2.6.1. Biogas

TEA has been used to compare emerging and existing biogas upgrading technologies.

For example, Scholz et al. (2013) compared the process costs of membrane permeation, pressurized water scrubbing, pressurized water scrubbing/membrane hybrid process, amine absorption, and an amine absorption/membrane hybrid process. Their results indicated that

37 the addition of gas permeation membranes reduced the costs of biogas upgrading. Similarly,

Deng and Hägg (2010) showed that two-stage membrane processes with recycle had the lowest costs compared to single-stage/no-recycle. Rajendran et al. (2014) evaluated the economics of purified biogas for vehicle fuel under different cleaning scenarios (PWS, amine absorption) using the organic fraction of municipal solid waste (OFMSW) as an AD feedstock (biogas production rate from 9,600-21,000 m3/d). The most profitable modeled case was when biogas from a WWTP was combined with AD biogas (total=21,000 m3 biogas/d) and 30% of the biogas was upgraded via PWS and 70% was upgraded through amine absorption (Rajendran et al., 2014). Based on this modeling result, larger scales of operation will likely enhance profitability of biogas upgrading.

Recently, Wu et al. (2016) suggested that biogas purification (PWS) for grid injection was the most energy efficient and profitable technology compared to CHP and solid oxide fuel cells (636 Nm3 of biogas/d). Rotunno et al. (2017) also noted that capital and operational costs of PWS for purified biogas grid injection (€0.54/Nm3) were much lower than for Bio-

CNG (€0.73/Nm3), primarily because compression was energy intensive. However, profitability may be improved for Bio-CNG because it is sold at a higher price than purified biogas (Table 2.2).

There are no studies that evaluated the costs of thermochemical or biological conversion of biogas to methanol. However, Okeke and Mani (2017) predicted that the minimum selling price for Fischer Tropsch (FT) drop-in fuels produced after biogas cleaning, biogas-to syngas conversion, and syngas to FT fuel conversion were $1.92/GGE ($2.06/gal)

(20,000 Nm3/h biogas production rate) to $5.29/GGE ($5.67/gal) (2,000 Nm3/h biogas production rate) (Okeke and Mani, 2017). Because biogas-to-FT fuel conversion has similar

38 process conditions (i.e. syngas production, syngas conversion), it is likely that the thermochemical process to convert biogas to methanol could be economically feasible at larger scales. Project costs for gasification-based biomass to methanol plants have been estimated between $0.2-1.0/kg methanol (M. Patel et al., 2016). However, that process is strongly influenced by scale and feedstock price, so some have suggested that waste biomass

(i.e. OFMSW) is currently the most appropriate feedstock (IRENA and IEA-ETSAP, 2013).

2.6.2. Methanotroph bioprocesses

There are very few TEA studies for methanotroph-based products. Criddle et al

(2014) conducted a TEA for the biological conversion of biogas to PHB. The cost of PHB production ($1-5/kg) was strongly influenced by scale of operation, microbial kinetics, and product yield. Further, the price of biogas/nutrients and capital costs were approximately

10% of the total project costs (Criddle et al., 2014). Scale of operation (100,000 MT/year) was also a significant factor in Levett et al. (2016)’s recent TEA of methanotrophic PHB production from natural gas. Those authors suggest that the generation of process heat by methanotrophs impacted costs, and thermophilic methanotrophs were preferred because less cooling agents were required (Levett et al., 2016). Fei et al. (2014) also conducted a preliminary TEA for the conversion of natural gas to methanotroph lipids for biodiesel production. Their results showed that feedstock price ($100-200/ton of CH4), methanotroph kinetics, and separation assumptions (i.e. lipid extraction) strongly influence the expected selling price of biodiesel ($0.7-10.8/gal) (Fei et al., 2014). Potentially, biogas could be used as a low/no-cost substrate for methanol production, which should lower costs.

Soni et al. (1998) compared the costs of two large scale systems for using methanotrophs for propylene oxide production. In their analysis, a granulated activated carbon-fluidized bed reactor (GAC-FBR) had much lower production costs (<$5/lb) than conventional stirred tank process (<$12/lb.), because the carbon bed continuously adsorbed the propylene oxide, which is toxic to methanotrophs. Therefore, continuous product separation could improve methanol production costs.

More TEAs are needed to evaluate the commercial viability and to identify bottlenecks of methanotrophic bioprocesses. Currently, there are no TEAs that have compared biological conversion processes to other competing biogas upgrading technologies, and none have evaluated the biological process to convert CH4 to methanol.

2.7. Concluding Remarks

Efficient management of organic waste and the development of renewable fuels that limit greenhouse gas emissions are critical aspects of a sustainable society. Both goals can be accomplished with widespread adoption of solid-state anaerobic digestion systems that can convert organic feedstocks to large quantities of methane-rich biogas. However, the high costs of biogas upgrading have contributed to the limited development and commercialization of anaerobic digestion systems. Methanotrophs, or methane-oxidizing bacteria, have great potential to upgrade biogenic methane sources to value added products, such as methanol. Yet, methanotrophic conversion could be limited by the low solubility and rate of mass transport of gaseous substrates (methane, oxygen) in liquid growth medium.

Thus, bioreactors designed to improve mass transfer, such as trickle-bed bioreactors are needed. Mathematical modeling of gas-liquid bioreactors offers a low-cost option to better

40 understand the process parameters that will influence performance and scale-up. Techno- economic analysis is needed to identify process bottlenecks and areas of research that could result in cost-effective, commercially viable methanotroph-based bioprocesses.

Table 2.1: Comparison of biogas cleaning technologies

Energy Capital Operating CH4 Demands Costs Costs Method purity 3 3 (kWh/ Advantages Disadvantages ($/Nm ($/Nm 3 (%) a a,b Nm biogas/h) CH4) biogas) Removes Pressurized CO , H S and 2000- 2 2 High water Water 93-99 0.18 0.2-0.3 other water 7500 demand Scrubbing soluble impurities Removes Pressurized H S, NH 2000- CO , N , and 2 3 Swing 83-99 0.34 0.2-0.3 2 2 removal needed 5500 O at high Adsorption 2 prior to PSA efficiency Low CH 0.1-0.2 4 High heat losses, Amine (ext. heat demands, waste 95-99 2200-4000 0.23-0.38c Efficient H S Absorption demand= 2 chemicals and CO 0.5 kWh) 2 produced removal

Simple Membranes Membrane 2500- 80-99 0.16-0.3c 0.2-0.3 operation, expensive, low Permeation 7500 low op. costs CH4 purity

Low CH4 Organic losses, Waste chemicals 2000- Solvent 95-99 N/A 0.2-0.3 Efficient H S produced, heat 6000 2 Scrubbing and/or CO2 demands removal

CO High energy see Table 2 Cryogenic 91-99 0.59 0.8-1.5 produced as demands, high 2.2-LNG byproduct capital costs

Data adapted from Bauer et al. (2013), Yang et al. (2014), Murray et al. (2014), Muñoz et al. (2015), Sun et al. (2015), Yang and Ge (2016), USEPA (2015). a. cost data dependent on scale (100-8500 Nm3/h biogas capacity) (Bauer et al., 2013; USEPA, 2015; Yang et al., 2014; Yang and Ge, 2016) b. 3 operating costs based on Nm of CH4 produced c. high range of cost includes H2S removal step (Yang et al., 2014).

Table 2.2: Comparison of biogas use methods

Capital Operating Product costs costs Method Product Selling Advantages Disadvantages ($/unit ($/unit Priceb prod) prod)

Heat/ $1,400- $130- $0.07/ Low cleaning Low energy Combustion Electricity 2800/kWa 230/kWa kWh requirements efficiency

Stringent gas Convenient; Pipeline- $100-500/ $0.2-0.4/ $0.1-0.2/ cleaning Purified-CH infrastructure 4 CH Nm3 CH /d Nm3 CH Nm3 requirements; 4 4 4 available low selling price

Transportation fuel with High power $100-500/ $0.3-3.25/ $2.05/ Bio-CNG CNG fewer life requirement for GGE/d GGE GGE cycle GHGs gas compression than gasoline

Transportation $600-1800/ $0.1-1.0/ $2.41/ High capital and Bio-LNG LNG fuel; high GGE/d GGE GGE operational costs energy density

High carbon $100-200/ conversion High capital Thermo- $0.2c/ $0.4/kg methanol kg efficiency; costs; high chemical kg MeOH MeOH MeOH/d high energy demands productivity Low productivity; Low energy product $0.4/kg demands; Biological methanol unknown unknown inhibition; MeOH renewable requires biocatalyst. exogenous electron donor Data obtained from Yang and Ge (2016), USEPA (2015), Blug et al. (2014) a. combusted in engines for electricity production. b. prices from US Energy Information Agency (USEIA) and US Department of Energy (USDOE) (USDOE, 2016; USEIA, 2017a, 2017b). c. costs based on multi-step natural gas-to-syngas-to methanol conversion (Blug et al., 2014).

Table 2.3: Summary of conditions and performance for biological conversion of CH4 to methanol (adapted from Ge et al. (2014))

Cell Methanol Methanol Conversion T CH4/ Strain Medium density yield Productivity efficiencya Reference (°C) air (g/L) (g/L) (g/L/d) (%) NMS/ Unknown N/A 1:0 0.3 0.5-1.0 0.5-1.0 ND Corder et al. (1986) vitamins

Ms. trichosporium 80 mM phosphate; 35 1:1 4 <0.01 <0.05 N/A Mehta et al. (1987) OB3b 5 mM MgCl2

100 mM Ms. trichosporium phosphate; 35 1:1c 2 0.34 4.1 27 Mehta et al. (1991)

44 b OB3b 5 mM MgCl2;

40 mM formate

0.07 mM Ms. trichosporium cyclopropanol; 30 1.1:1.0c 0.36d 0.26-0.29 0.11 71 Sugimori et al. (1995) OB3b 14 mM formate; 13 mM phosphate

12.9 mM phosphate; Ms. trichosporium Takeguchi et al. 25 1:1.6 67 nM 0.035 0.17 0.04 61 OB3b (1997) cyclopropanol; 14.3 mM formate 12.9 mM Ms. trichosporium phosphate; 30 1:1.6 N/A 0.192 0.05 N/A Furuto et al. (1999) OB3b cyclopropanol; 14.3 mM formate

Continued

Table 2.3: Continued 12.9 mM Ms. trichosporium phosphate; 25 1:4 0.6 0.25 0.16 ND Lee et al. (2004) OB3b 200 mM NaCl; 20 mM formate

Ms. trichosporium 20 mM phosphate; 32 1:4:1.1e 3.0 0.0006 0.0001 N/D Xin et al. (2004) IMV 3011 5 mM MgCl2

45 Ms. trichosporium 20 mM phosphate; 30 1:1f 3.0 0.0004 0.0004 N/A Xin et al. (2007)

IMV 3011 5 mM MgCl2

12.0 mM phosphate; Ms. trichosporium 25 1:1 100 mM NaCl; 0.6 0.44 0.66 ND Kim et al. (2010) OB3b 1 mM EDTA; 20 mM formate 400 mM

Ms. trichosporium phosphate; h g g 30 1:1 17.3 0.95 -1.12 0.68 >60 Duan et al. (2011) OB3b 10 mM MgCl2; 20 mM formate Consortium: NMS/40 mM Ms. sporium phosphate; NCIMB 11126; 4:6 or NMS/100 mM Ms. trichosporium 30 N/D 0.03-0.22 N/D 43-80 Han et al. (2013) 1:9i NaCl OB3b; NMS/40 mM Mc. capsulatus NH Cl Bath 4 Continued

Table 2.3: Continued 12.9 mM phosphate; Ms. trichosporium 20-30 1:1 100 mM NaCl; 0.2 0.12 0.5-0.6 ND Pen et al. (2014) OB3bh 1.0 mM EDTA; 20 mM formate 40 mM phosphate; Ms. sporium 100 mM NaCl; 35 4:6i 0.02 0.2 0.1-0.2 54-90 Yoo et al. (2015) KCTC 22312 40 mM NH4Cl; 50 μM EDTA

46 100 mM

Ms. trichosporium phosphate; 30 1:2.3 ND 0.393 1.2 74 Hwang et al. (2015) OB3b 0.5 mM EDTA; 40 mM formate 100 mM Ms. sporium j phosphate; k S.K.S. Patel et al. b 30 1:1 13-34 0.2 0.2 ND 17706 20 mM MgCl2; (2016c) 40 mM formate 100 mM Mcl. tundrae 30 1:1 phosphate; 18 0.17 0.15 ND Mardina et al. (2016) DSMZ 15673 50 mM formate 100 mM Mcy. bryophila phosphate; S.K.S. Patel et al. 30 1:1 9 0.15 0.15 ND DSM 21852 50 mM MgCl2; (2016b) 100 mM formate

Continued

Table 2.3: Continued 100 mM phosphate; Ms. sporium S.K.S. Patel et al. 30 1:9l 20 mM MgCl ; 3 0.19 0.1 43 17706 2 (2016a) 10 μM Fe (II); 5 μM Cu (II);

NMS/5 μM CuCl ; Mcd sp. SAD2 37 1:2m 2 ND 0.27 0.20-0.25 30-34 W. Zhang et al. (2016) 100 mM formate

NMS/5 μM CuCl ; Consortium 47 1:1m 2 0.23 0.3 0.05-0.07 47 Su et al. (2017) 100 mM formate

Ms=Methylosinus; Mc=Methylococcus; Mcl=Methylocella; Mcy=Methylocystis; Mcd=Methylocaldum a. conversion efficiency=moles of methanol produced/ moles of CH4 consumed b. immobilized cells on DEA cellulose (Mehta et al., 1991) or Chitosan (S.K.S. Patel et al., 2016c) c. mixture of CH4 and O2c d. wet cells e. CH4:CO2:O2:N2 f. air: CO2 g. with 5% paraffin h. in membrane aerated reactor. i. mixture of artificial biogas and air j. CH4: air ratio using synthetic gas containing CH4; CO2; H2 at 6:3:1 ratio. k. mg DCW/g Chitosan support l. mixture containing raw biogas from municipal wastewater treatment plant and 10% H2 m. biogas to air ratio. Contained 520 ppm H2S.

Heat Steam Steam Water Recovery system

Steam Flue gas

Natural Steam Heat Purification Raw Gas Reforming syngas Recovery

Makeup Purge gas syngas

Methanol Compression synthesis

Crude Distillation Methanol methanol

Figure 2.1: Thermochemical conversion of natural gas to methanol (adapted from Riaz et al.

(2013))

Figure 2.2: Pathway for methane oxidation in aerobic methanotrophs (adapted from Fei et al.

(2014)).

-1 -1 -1 -1 2 e 2 e 2 e 2 e Biosynthesis CO2

CH 4 MMO CH3OH MDH HCHO FaDH HCOOH FDH

O 2 H2O MDH EXOGENOUS Formate inhibitor SUPPLY

Figure 2.3: Strategy for methanol production using aerobic methanotrophs (adapted from Ge

et al. (2014))

Interface

CB, G

Ci, G

Bulk Gas Bulk Liquid

Ci, L

CB, L

δ δ G L

Figure 2.4: Schematic of two film theory (adapted from Cooper and Alley (2011))

Chapter 3: Effect of Limited Air Exposure and Comparative Performance Between Thermophilic and Mesophilic Solid-State Anaerobic Digestion of Switchgrass

Johnathon P. Sheets, Xumeng Ge, Yebo Li*

Department of Food, Agricultural and Biological Engineering, The Ohio State

University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH, 44691-4096, USA

Switchgrass is an attractive feedstock for biogas production via anaerobic digestion

(AD). Many studies have used switchgrass for liquid anaerobic digestion (L-AD), but few have used switchgrass for solid-state anaerobic digestion (SS-AD). Limited air exposure to the reactor headspace has been adopted in commercial scale anaerobic digesters for different applications. However, little research has examined the effect of limited air exposure on biogas production during SS-AD. In this study, the effects of air exposure and total solids

(TS) content on SS-AD performance were evaluated under mesophilic (36±1°C) and thermophilic (55±0.3°C) conditions. Limited air exposure did not significantly influence the methane yield during SS-AD. Thermophilic SS-AD had greater methane yields (102–145 L

-1 -1 CH4 kg VSadded ) than mesophilic SS-AD (88–113 L CH4 kg VSadded ). Both mesophilic SS-

AD (73–136 GJ) and thermophilic SS-AD (2–95 GJ) produced positive net energy based on a theoretical ‘garage-type’ SS-AD digester operating in a temperate climate.

3.1. Introduction

The United States Department of Energy (U.S. DOE) considers switchgrass

(Panicum virgatum L.) as a model lignocellulosic energy crop, due to its high productivity, efficient water and nutrient use, and adaptability to marginal lands (Keshwani and Cheng,

2009). Anaerobic digestion (AD) is a robust process that is able to convert complex organic material such as energy crops into biogas composed of 60–70% methane (Barbanti et al.,

2014). Biogas is a flexible renewable fuel that can be upgraded into transportation fuels or used directly to generate electricity and heat (Li et al., 2011). In fact, biogas from AD has recently been included in the United States Renewable Fuels Standard (USEPA, 2014), and switchgrass has been tested as a feedstock in liquid-AD (L-AD) systems (Frigon et al., 2012;

Massé et al., 2010), which use low total solids (TS) contents of less than 15% (Xu et al.,

2014). However, floating and stratification of fibrous materials has made L-AD of lignocellulosic biomass difficult to scale up (Frigon and Guiot, 2010). In contrast, solid-state anaerobic digestion (SS-AD) systems operate at TS contents greater than 15%. Compared to

L-AD, SS-AD has higher volumetric productivities (Li et al., 2011) and generates a digestate that is easier to transport due to low moisture (Xu et al., 2014). These advantages make SS-

AD an intriguing option for bioenergy production from lignocellulosic feedstocks, such as switchgrass. However, research on SS-AD of switchgrass is limited (Ahn et al., 2010; Brown et al., 2012; El-Mashad, 2013). Prior to the scale up of this promising approach, it is essential to evaluate the performance of SS-AD of switchgrass under different environmental conditions, such as limited air exposure and operating temperature.

A common practice in L-AD systems is to supply oxygen (O2) or air to the digester headspace during operation. Limited aeration may lead to increased rates of hydrolysis and 53

fermentation, likely because many fermentative AD microbes are facultative anaerobes

(Botheju and Bakke, 2011). In fact, several studies showed that limited air addition did not inhibit the strictly anaerobic methanogens during L-AD, and actually enhanced methane yield

(Díaz et al., 2010; Lim and Wang, 2013). In addition, limited aeration is also utilized to remove hydrogen sulfide (H2S) in the biogas prior to upgrading (Díaz et al., 2010).

Therefore, limited air exposure could be a helpful process to improve both biogas production and quality in SS-AD. However, there is little research available on the effect of air exposure on biogas production during SS-AD (Charles et al., 2009).

SS-AD can operate under either mesophilic (37°C) or thermophilic (55°C) conditions. Mesophilic AD is better established, and often more stable and reliable than thermophilic AD (Labatut et al., 2014). However, thermophilic conditions have been shown to accelerate the conversion of organic material into biogas during SS-AD (Li et al., 2011).

Thermophilic temperatures may result in inhibitory levels of volatile fatty acids (VFAs) due to enhanced activity of fermentative microbes (Ahn et al., 2010; Labatut et al., 2014; Shi et al., 2013), which can be addressed by controlling the feedstock to inoculum (F/I) ratio and carbon to nitrogen (C/N) ratio, and providing proper nutrients and pH buffering (Lin et al.,

2014; Shi et al., 2014). One major concern about thermophilic SS-AD is the high energy input required to maintain the thermophilic temperature, which may vary significantly for different seasons and can offset the high biogas production rates (Li et al., 2011). To the best knowledge of the authors, there have been no reports that evaluated the performance between mesophilic and thermophilic SS-AD of switchgrass based on energy inputs and outputs while operating at elevated TS contents (>20%).

To address these research gaps, the objectives of this study were to: (1) evaluate the effect of limited air exposure on biogas production during SS-AD; (2) compare performance of mesophilic and thermophilic SS-AD of switchgrass at elevated TS contents; and (3) determine the net energy production during mesophilic and thermophilic SS-AD of switchgrass during long-term operation.

3.2. Methods

3.2.1. Feedstock and inoculum

Switchgrass (Panicum virgatum L., cultivar: Cave-in-Rock) was collected in October

2009 from a farm located at the Ohio Agricultural Research and Development Center

(OARDC) in Jackson, Ohio. All experiments were done at the OARDC in Wooster, Ohio.

Upon receipt, switchgrass was oven dried at 40°C in a convection oven (Precision Thelco

Model 18, Waltham, MA, USA) to less than 10% moisture, ground with a hammer mill

(Mackisik, Parker Ford, PA, USA) to pass through a 5-mm screen, and stored in air-tight containers prior to use. Switchgrass was composed of 91.3±0.2% TS, 96.9±0.0% volatile solids (VS, based on TS), 3.1±0.0% ash (based on TS), 43.7±0.7% total carbon (TC), and

0.6±0.1% total nitrogen (TN). The extractives, cellulose, hemicellulose, and lignin contents were 12.1±1.2%, 31.0±1.0%, 19.5±0.6%, and 19.3±0.4%, respectively (all based on TS of sample).

Raw effluent (TS=7.7%) was collected from a commercial scale, mesophilic liquid anaerobic digester (KB Compost Services, Akron, OH, USA) which uses municipal sewage sludge as a feedstock. Raw effluent was dewatered by centrifugation to increase the TS from

7.7% to 17.8%. Raw and centrifuged effluents were stored in air-tight buckets at 4°C prior to 55

use. Proportions of raw and centrifuged effluent were activated anaerobically for one week at

36±1 and 55±0.3°C prior to inoculation in mesophilic and thermophilic SS-AD reactors, respectively.

3.2.2. Solid-state anaerobic digestion

The effects of TS, temperature, and air exposure on the performance of SS-AD were evaluated using a three factor-two level (23) experimental design. Two levels of TS (20%,

30%), temperature (36±1, 55±0.3°C), and air exposure (no exposure, limited exposure) were used for the SS-AD experiments. Three replicate reactors were designed for each treatment combination (24 total reactors). The raw L-AD effluent, centrifuged L-AD effluent, and switchgrass were thoroughly mixed to the desired TS at a feedstock to inoculum (F/I) ratio of

3 (based on VS). For reactors at 20% TS, switchgrass, raw effluent, and centrifuged effluent represented 64%, 31%, and 8% of the reactor content (dry basis), respectively. For reactors at

30% TS, switchgrass, raw effluent, and centrifuged effluent represented 64%, 8%, and 28% of the reactor content (dry basis), respectively. Pre-mixing was conducted under aerobic conditions. SS-AD was carried out in 1-L glass reactors, and each was loaded with 400 g

(30% TS) to 550 g (20% TS) of mixed materials to reach an initial working volume of 850 mL. After loading, half of the reactors were purged with pure nitrogen (N2) gas for 1 min to induce oxygen depleted conditions (initial O2 content < 0.3%), then sealed with a rubber stopper. The remaining reactors were simply sealed with a rubber stopper after mixed materials were loaded. All reactors were then placed in either a 36±1°C or 55±0.3°C incubator for mesophilic and thermophilic SS-AD, respectively. The full digestion period was 70 days. On days 10, 20 and 30, 100 mL of headspace gas was displaced by ambient air

using a plastic syringe. This was only conducted for unpurged reactors. Biogas was collected in 5-L Tedlar gas bags (CEL Scientific, Santa Fe Springs, CA) attached to a single outlet on each reactor. For the first 35 days, biogas composition and volume were measured every 2–4 days. From day 35 to day 55, biogas composition and volume were measured once per week.

As controls, reactors loaded only with centrifuged L-AD effluent were run in parallel. One of the N2 purged reactors under thermophilic conditions (TS = 20%) had seal failure on day 15, and was not included in data analysis.

3.2.3. Analytical methods

Compositional analysis of switchgrass, raw L-AD effluent, centrifuged L-AD effluent, mixed materials before SS-AD, and digestate after SS-AD was conducted. The TS,

VS, pH, and alkalinity were measured according to Standard Methods for the Examination of

Water and Wastewater (APHA, 2005). Samples for volatile fatty acid (VFA) measurement were prepared using an adapted version of the methods described by Shi et al. (2013), which consisted of suspending 5 g of L-AD effluent or digestate in 5 mL of deionized water, thoroughly mixing it, and separating the solids by centrifugation (10,000 rpm for 5 min). The supernatant was acidified to a pH of 2–3 by addition of hydrochloric acid, and then filtered via a syringe filter (0.2 μm). VFAs (acetic, propionic, isobutyric, butyric, isovaleric, valeric acids) were measured using a gas chromatograph (GC) (Shimadzu, 2010 PLUS, Columbia,

MD, USA) equipped with a 30 m × 0.32 mm × 0.5 μm Stabilwax polar phase column and flame ionization detector. Total carbon (TC) and total nitrogen (TN) were measured with an elemental analyzer (Vario Max CNS, Elementar Americas, Mt. Laurel, NJ, USA) in order to calculate the C/N ratio. Total ammonia nitrogen (TAN), composed of free ammonia (NH3)

+ and ammonium (NH4 ), was measured by a modified distillation and titration method (ISO

5664, 1984) that used 4% boric acid and a Kjeldahl Distillation Unit B-324 (Buchi,

Labortechnik, AG, Switzerland). Extractives of raw materials and SS-AD digestate were measured according to the NREL Analytical Procedure described in Sluiter et al. (2005).

Structural carbohydrates (cellulose, hemicellulose, lignin) were analyzed according to the

NREL Analytical Procedure described in Sluiter et al. (2008). Extractives-free biomass was hydrolyzed into monomers after a two-step acid hydrolysis, and the concentrations of these monomers were determined by high performance liquid chromatography (HPLC) (Shimadzu

LC-20AB, Columbia, MD, USA) (Sluiter et al., 2008). Acid soluble lignin was measured by

UV–Vis spectroscopy, and acid insoluble lignin was determined by gravimetric analysis

(Sluiter et al., 2008).

Biogas in Tedlar bags was adapted to ambient pressure and ~25°C before measurement of composition and volume. The composition of biogas (CO2, CH4, N2, and O2) was analyzed by a GC (HP 6890, Agilent Technologies, Wilmington, DE, USA) equipped with an alumina/KCl deactivation column (30 m× 0.53 mm × 10 mm) and a thermal conductivity detector. Helium was used as a carrier gas at a flow rate of 5.2 mL min-1.

Temperatures of the injector, column, and detector were set at 150, 40, and 200°C, respectively. The volume of biogas was measured with a drum-type gas meter (Ritter, TG 5,

Bochum, Germany). Immediately following the 100-mL air displacement on days 10, 20, and

30, headspace gas was also subjected to compositional analysis by GC. Performance of SS-

-1 -1 AD was evaluated using the daily CH4 production (L kg VSadded d ), cumulative CH4 yield

-1 (L kg VSadded ), and volumetric CH4 productivity (Lmethane/Lwork). Cumulative CH4 yield was calculated as the total CH4 production over the digestion period minus that produced by the

inoculum. Volumetric CH4 productivity was expressed as Vmethane/Vwork, where Vmethane was the volume of methane produced and Vwork was the working volume of the reactor (Brown et al., 2012).

3.2.4. Energy analysis

Net energy analysis was used to determine whether the useful energy in the biogas could offset the environmental heat losses in a pilot-scale batch SS-AD digester operating outdoors in a temperate U.S. climate in six successive 35-day digestion periods over seven months. The daily net energy (Eq. 3.1) was calculated for each reactor configuration

(temperature, TS) evaluated in this study.

J Daily Net Energy ( ) =Biogas energy-Heat required to maintain temperature (Eq. 3.1) d

Biogas energy was calculated by multiplying the measured daily CH4 yields by the

-3 lower heating value of CH4 at 25°C and 101kPa (33 MJ m ) and an assumed combined heat and power unit total efficiency of 0.6 (USEPA, 2013). Heat required to maintain the designated temperature was equal to heat losses minus heat generated by microbial activity.

Heat generated by microbial activity was determined using Eq. 3.2 (Lindorfer et al., 2006):

Heat generated by microbial activity (J/d)=ME*∆TSH*4200 (Eq. 3.2)

where ME is the mass of effluent in the digester (kg), ΔTSH is the daily temperature increase in energy crop digesters due to microbial self-heating measured by Lindorfer et al. (2006)

(used 0.15°C d-1), and 4200 is the specific heat of water (J kg-1 °C-1). Heat losses were calculated using Eq. 3.3:

Heat losses (J/d)=U*A*∆T (Eq. 3.3) where U is the coefficient of heat transfer (J m-2 d-1 °C-1); A is the cross-sectional area through which heat loss is occurring (m2), and ΔT is the temperature drop across the surface

(°C). Radiative heat transfer was not included. Energy required for initial material heating was included in the cumulative net energy production on the days when the digester contents were removed and new switchgrass and inoculum were introduced to the digester. Energy required for material heating was calculated by multiplying the specific heat of water (4200 J kg-1 °C-1) and the initial moisture content of the digester contents. It was assumed that feedstock and inoculum are premixed at 20°C prior to loading into the digester.

The common “garage-type” SS-AD digester was used for this theoretical evaluation.

The digester was made of 300-mm thick concrete and had dimensions of 10 m × 5 m × 5 m

(L×W×H). The walls were all insulated and floors were in contact with dry earth (Metcalf &

Eddy Inc., 2003). A floating cover with a 25-mm insulating board installed under roofing was used for the ceiling. Heat transfer coefficients for digester construction materials were obtained from Metcalf and Eddy (2003).

The switchgrass was assumed to be harvested in June 2013 and was dried and stored until April 2014. The theoretical scaled up SS-AD digester was assumed to be operated in successive 35-day digestion periods during the months of April-November. After each 35- day digestion period, digester contents were removed, new switchgrass and inoculum were heated to the required temperature (mesophilic (37°C) or thermophilic (55°C)) over two days and digested for another 35 days. The selection of 35-day digestion periods was due to observation of rapid decline in CH4 productivity and net energy production after day 35.

Since the switchgrass was collected from Jackson, Ohio, and it was assumed that the 60

theoretical scaled up digester would be operating at the location where the switchgrass was grown, local air and soil temperature data (2014) from the website of the OARDC weather station in Jackson, Ohio (39.0519°, -82.6367°) were used to determine daily heat losses for the seven months of operation.

3.2.5. Statistical analysis

Statistical significance was determined by analysis of variance (ANOVA, a = 0.05) or

Tukey’s Honestly Significant Difference (HSD) test using JMP Statistical Software from

SAS Institute Inc. (Version 10.0.2, Cary, NC, USA). Experimental data were presented as average values ± standard deviations.

3.3. Results and Discussion

3.3.1. Composition of switchgrass and L-AD effluent

Switchgrass had high TS (91.3%), VS (96.9% of TS), and C/N ratio (79.4). High TS content, high F/I ratio, and high C/N ratio may cause hydrolytic inhibition, rapid accumulation of VFAs, and/or nitrogen limitation during SS-AD (Abbassi-Guendouz et al.,

2012). In fact, Ahn et al. (2010) observed rapid accumulation of VFAs (12 g L-1), low biogas production (2.5 L over 60 days), and low CH4 content in the biogas (max of 24.5% CH4) at a switchgrass to inoculum (dairy manure and inoculum) ratio of 4.9 (based on VS), C/N ratio of 39, and TS of 15%. In the present study, switchgrass also had substantial lignin content

(19.3%), which has been shown to be recalcitrant to cellulolytic degradation (Meng and

Ragauskas, 2014). Despite these compositional limitations, a switchgrass to inoculum (L-AD effluent) ratio (F/I) of 3 provided stable initial conditions for SS-AD. Stability was ensured

-1 by the alkalinity (~14 g kg as CaCO3) provided by the L-AD effluent, which protected reactors against acidification through pH buffering (Shi et al., 2014). Also, L-AD effluent had a low C/N ratio (Table 3.1), which provided the SS-AD reactor mixtures with balanced initial C/N ratios from 15 (TS = 20%) to 20 (TS = 30%). L-AD effluent activated at

55±0.3°C had significantly higher VFAs because higher temperatures enhance the growth kinetics of acidogenic microbes in AD (Labatut et al., 2014). This caused initial VFAs in thermophilic reactors (2.6-3.8 g kg-1) to be 2-3 times higher than the initial VFAs for mesophilic reactors (1.3-1.4 g kg-1) (Table 3.2).

3.3.2. Effect of air exposure on SS-AD performance

Immediately following air exposure on days 10, 20 and 30, there was elevated N2 content (19-28%) and O2 content (5-8%) in the reactor headspace. This briefly diluted the

CH4, which contributed to lower daily CH4 production on the days following the air exposure

(Figure 3.1c). However, methanogenic O2 inhibition was not observed, as CH4 content quickly recovered (Figure 3.1c) to levels similar to reactors pre-purged with N2 (Figure 3.1a).

Overall, 70-day cumulative CH4 and CO2 yields were not significantly affected by air exposure (ANOVA, p>0.05). This was probably due to formation of diffusion barriers between the “sticky” contents of SS-AD and the gas phase with O2 introduced via air displacement (Botheju and Bakke, 2011). It is most likely that air displacement simply diluted the CH4 in biogas and the O2 introduced could not diffuse into reactor materials to cause inhibition even though the measured content of O2 after air addition in this study (2.2-

3.6 mM) was above the inhibitory oxygen content (30 nM) for methanogens in anaerobic digesters reported by Scott et al. (1983). These results are significant because SS-AD

performance may not be impacted by low air exposure levels. Further studies could be conducted to evaluate SS-AD performance at higher air exposure levels. Presence of H2S content in the biogas was not detected, likely due to the limited sulfur content of switchgrass.

Because reactors purged with N2 and reactors with limited air exposure had similar CH4

(Figure 3.1a and c) and CO2 yields (Figures 3.1b and d), reactors with limited air exposure were not included in the net energy analysis and analysis of organic component degradation.

3.3.3. Effect of temperature and TS on SS-AD performance

Thermophilic conditions improved SS-AD performance. At 20% TS, thermophilic reactors had daily biogas production peaks on day 2 (12.7 L kg-1 VS d-1) and day 12 (17.6 L kg-1 VS d-1), while mesophilic reactors had 23% and 48% lower peaks on day 2 and day 8, respectively. The first biogas peak on day 2 was primarily composed of CO2, and peak daily

CO2 production was nearly two times greater under thermophilic conditions (Figure 3.1b).

The second biogas peaks were due primarily to CH4 production, and thermophilic reactors had nearly three times higher peak daily CH4 production than mesophilic reactors (Figure

3.1a). Initial peak CO2 production was likely due to enhanced hydrolysis and acidogenesis, which also caused VFA accumulation and imbalance between acidogenesis and methanogenesis (Shi et al., 2013). This imbalance also likely contributed to a longer lag phase for cumulative CH4 production (Figure 3.2). Additionally, the imbalance could also have been heightened by higher initial VFAs in thermophilic reactors (Table 3.2). However, alkalinity increased and pH remained fairly stable before and after the digestion period

(Table 3.2), which indicated that buffering was sufficient. Furthermore, methanogenic

activity was maintained under all operating conditions, as indicated by the CH4 content in the biogas, which was stable (55-60%) throughout the digestion period (Figure 3.1a).

Enhanced hydrolysis was observed for reactors under thermophilic conditions. In fact, at both TS contents, reactors under thermophilic conditions had significantly higher

(p<0.05) holocellulose (cellulose+hemicellulose) degradation than reactors under mesophilic conditions (Table 3.3). Aside from a more active population of cellulolytic and xylanolytic microbes (Shi et al., 2013) and enhanced reaction kinetics (Labatut et al., 2014), increased temperatures could have improved the mass transfer of hydrolysis products to AD microflora

(Ribeiro et al., 2006). The decline in performance under mesophilic conditions and 30% TS was very likely due to hydrolytic inhibition caused by mass diffusion limitations (Xu et al.,

2014). Mass diffusion limitations could have limited the amount of hydrolytic products available to fermentative microbes, reducing the fermentative products (H2, acetic acid, CO2) available for methanogenic conversion. This claim is supported because there was reduced

CH4 production but limited evidence of acidification or ammonia inhibition (Table 3.2), indicating hydrolysis products were unavailable to fermentative microbes (Abbassi-

Guendouz et al., 2012).

There was a significant positive correlation between holocellulose degradation and

2 35-day cumulative CH4 yield (p<0.05, R =0.76), which indicates that the degradation of cellulose and hemicellulose in switchgrass positively contributed to CH4 production.

However, there was similar holocellulose degradation in mesophilic reactors at 20% TS and

30% TS, despite higher CH4 production and VS degradation at 20% TS. Furthermore, there was better correlation between VS reduction and 35-day cumulative CH4 yield (p<0.05,

R2=0.90). These discrepancies indicate that other organic components, such as proteins and

extractives, also contributed to the total CH4 production. Before digestion, only ~3% of cellulose and ~7% of hemicellulose in the reactor contents came from the inocula, indicating most of the degraded cellulose and hemicellulose should have come from switchgrass.

3.3.4. Performance perspectives and net energy analysis

Thermophilic conditions were most favorable for the total production of CH4 because at equivalent TS contents, thermophilic reactors had ~20-30% higher cumulative CH4 yields than mesophilic reactors (Figure 3.2). The 70-day cumulative CH4 yields for thermophilic

-1 SS-AD with 20% TS (145 L CH4 kg VS) in the current study were similar to the 40-day

-1 yields reported at 16% TS, F/I ratio of 2, and 50°C (167 L CH4 kg VS) (El-Mashad, 2013).

-1 The 70-day cumulative CH4 yields from mesophilic reactors at 20% TS (113 L CH4 kg VS) were 41-63% lower than those reported in batch mesophilic L-AD of ensiled switchgrass

-1 (191-309 L CH4 kg VS) (Massé et al., 2010) but similar to mesophilic SS-AD of

-1 switchgrass at 18% TS and F/I ratio of 3 (117 L CH4 kg VS) (Brown et al., 2012). The lower CH4 yields of this study compared to Massé et al. (2010) can be ascribed to higher TS, a higher F/I ratio, and no pretreatment of switchgrass prior to SS-AD. CH4 production was very slow after day 35. In fact, about 70-78% (mesophilic) to 85-86% (thermophilic) of the cumulative CH4 yield at 70 days was produced by day 35 (Figure 3.2). These results indicate that a shorter digestion period could be more advantageous for the SS-AD conditions tested.

One main advantage of operating SS-AD at increased TS content is the ability to provide more feedstock per unit volume. However, an increased proportion of low bulk density switchgrass reduced the bulk density of the reactor content from 0.70 kg L-1 to 0.45 kg L-1 when TS content was increased from 20% to 30%. Thus, volumetric methane

productivity was lower for reactors at 30% TS (Figure 3.3). Still, there was significantly higher (p<0.05) volumetric CH4 production from thermophilic reactors than mesophilic reactors. In fact, there was no significant difference (p>0.05) between 35-day CH4 productivity from thermophilic reactors and 70-day CH4 productivity from mesophilic reactors. Therefore, an equivalent amount of CH4 could be produced in thermophilic SS-AD in a shorter digestion period than mesophilic SS-AD. Besides, thermophilic digestion provides greater destruction of pathogens, improving safety of downstream digestate utilization (Labatut et al., 2014).

Table 3.4 shows the assumptions used in net energy analysis. At equivalent F/I ratios, reactor contents at 30% TS had ~50% higher VS contents than reactors at 20% TS. However, the low bulk density of switchgrass limited the advantage of operating the “garage type” SS-

AD digester at 30% TS, because less total material weight could be loaded. Therefore, reactors at 20% TS and 30% TS had nearly equivalent initial VS loading (~23,000 kg VS). In this analysis, the advantage for operation at 30% TS was reduced initial heating of reactor contents, due to lower moisture content. In fact, under both operating temperatures, reactors at 30% TS had 44% lower initial heating requirements.

Net energy analysis showed that all reactor conditions produced positive cumulative net energy. Although initial heating of reactor contents was reduced, there was little incentive to operate at higher TS, because of lower CH4 production. In fact, thermophilic operation at

30% TS consumed more energy than what was produced by SS-AD in the first 35-day digestion period (Figure 3.4b). Daily net energy production was fairly stable throughout the seven-month period (Figure 3.4a), due to similar ambient temperatures (Table 3.4).

Operation under thermophilic conditions produced over two times higher peak daily net energy than when operated under mesophilic conditions (Figure 3.4a). However, mesophilic conditions at 20% TS had 43% higher peak cumulative net energy production than thermophilic conditions at 20% TS because of reduced initial heating requirements after inoculation (Figure 3.4b). These results indicate mesophilic SS-AD is more suitable for bioenergy production in a temperate climate. However, there are limitations to the net energy analysis that should be addressed in future study. These include the effect of microbial energy production under thermophilic conditions, optimization of other reactor parameters

(F/I ratio, C/N ratio), and effects of harvest time on switchgrass digestibility (Mitchell and

Schmer, 2012). Springtime harvesting of switchgrass has the advantage of nutrient translocation into the soil and reduced fertilizer costs. However, lower biomass yields are expected during spring (Mitchell and Schmer, 2012). Therefore, integration of energy required for switchgrass cultivation and transportation would improve the energy analysis for

SS-AD systems. Design of SS-AD reactors with low surface area to volume ratios and better insulation could reduce heat losses to the environment. Furthermore, chemical or biological pretreatment of switchgrass could also improve biogas production in SS-AD.

3.4. Conclusions

Minimal air exposure to the digester headspace did not significantly affect reactor performance during the SS-AD of switchgrass. CH4 yields and organic component degradation were improved during thermophilic SS-AD compared to mesophilic SS-AD, with the highest yields obtained at lower total solids content. The recommended digestion period for SS-AD of switchgrass is 25–35 days, based on the CH4 productivity and net

energy production. SS-AD of switchgrass under mesophilic SS-AD and thermophilic SSAD produced positive cumulative net energy.

Table 3.1: Composition of L-AD effluent

Raw Centrifuged Raw Centrifuged Component Effluent effluent effluent effluent (mesophilic) (mesophilic) (thermophilic) (thermophilic) TS (%) 7.7±0.0b 17.9±0.5c 7.7±0.0b 17.8±0.4c VS (%)a 58.6±0.1b 57.0±1.0c 58.6±0.1b 54.3±0.3c Ash content (%)a 41.4±0.1b 43.1±1.0c 41.4±0.1b 45.7±0.3c TC (%) 2.9±0.0c 6.3±0.2c 3.2±0.1c 6.4±0.1c TN (%) 0.6±0.1c 0.8±0.1c 0.6±0.0c 0.7±0.0c C/N ratio 5.1±0.5c 7.9±0.3c 5.7±0.4c 8.7±0.1c pH 8.5±0.0c 8.7±0.0c 8.4±0.0c 8.6±0.0c

alkalinity c c c c -1 14.0±0.8 14.3±0.5 14.9±0.7 13.1±0.2 (g kg as CaCO3) VFAs (g kg-1) 1.0±0.0c 1.2±0.0c 3.5±0.1 c 6.1±0.3c TAN (g kg-1) 4.0±0.1c 4.6±0.3c 4.4±0.2c 5.2±0.2c Extractives (%)a 14.0±0.2b 14.0±1.1b 14.0±0.2b 14.0±1.1b Cellulose (%)a 1.5±0.2b 1.5±0.1b 1.5±0.2b 1.5±0.1b Hemicellulose (%)a 2.8±0.6b 2.4±0.3b 2.8±0.6b 2.4±0.3b Lignin (%)a NA NA NA NA values presented as average ± standard deviation a. based on TS of sample; other components based on total weight of sample; NA=not analyzed b. characteristics of effluent prior to activation c. characteristics of effluent after 7 days of activation at 36±1°C (mesophilic) or 55±0.3°C (thermophilic).

Table 3.2: Reactor characteristics before and after 70 days of SS-AD

Total ammonia TS Alkalinity Total VFAs Temperature pH -1 nitrogen -1 (g kg as CaCO3) (g kg ) (°C) (g kg-1) (%) initial final initial final initiala final initial final Mesophilic 20 8.3±0.0 8.0±0.1 11.6±0.1 13.9±0.3 3.4 4.4±0.0 1.3±0.1 0.1±0.0 (36±1°C) 30 8.0±0.1 8.5±0.1 11.3±0.6 13.1±0.5 3.4 3.9±0.0 1.4±0.4 0.0±0.0 Thermophilic 20 8.4±0.1 8.2±0.0 11.5±0.1 12.5±0.0 3.8 4.3±0.0 2.6±0.1 1.7±0.3 (55±0.3°C) 30 8.2±0.3 8.4±0.1 10.8±0.8 12.5±0.6 3.9 4.2±0.0 3.8±0.5 3.2±0.7 a. calculated from feedstock composition

Table 3.3: Cellulose, hemicellulose and VS removal after 70 days of SS-AD

Cellulose Hemicellulose VS removal Temperature TS removal removal (°C) (%) (%) (%) (%) Mesophilic 20 32.5±1.9 34.8±2.6 25.2±2.2 (36±1°C) 30 30.4±3.6 33.3±4.2 21.5±1.0 Thermophilic 20 52.7±6.8 58.4±2.7 30.2±3.2 (55±0.3°C) 30 37.6±1.7 43.7±2.4 25.2±0.5

Table 3.4: Assumptions used in net energy analysis

Assumption Value Unit Digestion time Digestion period 35 days period-1 Number of digestion periods 6 periods Total time of digester operation 220 days

Digester design Length 10 m Width 5 m Height 5 m Working volume 80 % of total volume Walla thickness 300d mm Floorb thickness 300d mm Floating coverc 25d mm d -2 -1 -1 Uwall 0.8 J m s °C d -2 -1 -1 Ufloor 1.7 J m s °C d -2 -1 -1 Ucover 1.0 J m s °C

Input material properties Bulk Density (TS=20%) 0.70e kg FW L-1 Bulk Density (TS=30%) 0.45e kg FW L-1 Moisture content (TS=20%) 800e g kg-1 FW Moisture content (TS=30%) 700e g kg-1 FW Temperature 20 °C

Climate conditions (Averages) Outside air temperature, Apr-May 13.0f °C Soil temperature, 2”, Apr-May 11.9f °C Outside air temperature, May-Jun 17.3f °C Soil temperature, 2”, May-Jun 17.6f °C Outside air temperature, Jun-Jul 22.2f °C Soil temperature, 2”, Jun-Jul 24.1f °C Outside air temperature, Jul-Aug 21.7f °C Soil temperature, 2”, Jul-Aug 23.7f °C Outside air temperature, Aug-Sept 22.2f °C Soil temperature, 2”, Aug-Sept 23.6f °C Outside air temperature, Sept-Oct 18.5f °C Soil temperature, 2”, Sept-Oct 20.8f °C Outside air temperature, Oct-Nov 15.3f °C Soil temperature, 2”, Oct-Nov 14.9f °C a. concrete with insulation b. concrete in contact with dry earth c. insulating board installed on roof under floating cover d. value obtained from Metcalf and Eddy (2003). e. measured value in this study f. OARDC Weather Station, Jackson, OH FW=fresh weight; DW=dry weight

20 70 (a) 18

) 60 1 - 16

VS VS d 50

1 14 - Mesophilic, TS=20% 12 Mesophilic, TS=30% 40 Thermophilic, TS=20% 10 Thermophilic, TS=30%

30 Content(%)

8 4 Production(L kg 6 CH

4 20 4 10

2 Daily CH 0 0 0 10 20 30 40 50 60 70 Day

9 (b)

) 1

- 8 VS VS d

1 7 - Mesophilic, TS=20% Mesophilic, TS=30% 6 Thermophilic, TS=20% 5 Thermophilic, TS=30%

Production(L kg 3 2 2

1 Daily CO 0 0 10 20 30 40 50 60 70 Day Continued

Figure 3.1: Effects of reactor conditions on SS-AD performance for N2 purged reactors (a,b)

and reactors with limited air exposure (c,d). Asterisks show days when 100 mL of air was

displaced into the headspace. 73

Figure 3.1: Continued

20 70 (c) 18 * * *

) 60 1 - 16

VS VS d 50

1 14 - Mesophilic, TS=20% 12 Mesophilic, TS=30% 40 Thermophilic, TS=20% 10 Thermophilic, TS=30%

30 Content(%)

8 4 Production(L kg 6 CH

4 20 4 10

2 Daily CH 0 0 0 10 20 30 40 50 60 70 Day

10 (d)

9 * * *

) 1

- 8 Mesophilic, TS=20% Mesophilic, TS=30%

VS VS d 7 Thermophilic, TS=20% 1 - Thermophilic, TS=30% 6

Production(L kg 3 2 2

1 Daily CO 0 0 10 20 30 40 50 60 70 Day

160 Mesophilic, TS=20% 140 Mesophilic, TS=30% Thermophilic, TS=20% Thermophilic, TS=30%

120

VS)

1 -

100 yield(L kg

4 80

Cumulative Cumulative CH 40

0 0 10 20 30 40 50 60 70 Day

Figure 3.2: Cumulative CH4 yield for N2 purged reactors

20 Mesophilic, 35 days 18 Mesophilic, 70 days Thermophilic, 35 days

) 16 Thermophilic, 70 days work

/L 14

methane 12

productivity (L 6 4

CH 4

0 20% 30% TS content

Figure 3.3: Comparison of CH4 productivity for N2 purged reactors at 35 days and 70 days

5.0 Mesophilic, TS=20% (a) Mesophilic, TS=30% Thermophilic, TS=20% Thermophilic, TS=30%

4.0

) 1 - 3.0

2.0

1.0

DailyEnergy Net (GJ d 0.0

-1.0 0 25 50 75 100 125 150 175 200 225 Day

160 Mesophilic, TS=20% (b) Mesophilic, TS=30% 140 Thermophilic, TS=20% Thermophilic, TS=30% 120

100

-20 Accumulative Accumulative Net Energy (GJ) -40 0 25 50 75 100 125 150 175 200 225 Day

Figure 3.4: Daily (a) and cumulative (b) net energy production. Note: Initial energy for

heating digester contents is included in (b).

Chapter 4: Biological Conversion of Biogas to Methanol using Methanotrophs Isolated from Solid-State Anaerobic Digestate

Johnathon P. Sheetsa, Xumeng Gea, Yueh-Fen Lib, Zhongtang Yub, Yebo Lia

a. Department of Food, Agricultural and Biological Engineering, The Ohio State

University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH, 44691-4096, USA b. Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA

The aim of this work was to isolate methanotrophs (methane oxidizing bacteria) that can directly convert biogas produced at a commercial anaerobic digestion (AD) facility to methanol. A methanotrophic bacterium was isolated from solid-state anaerobic digestate. The isolate had characteristics comparable to obligate methanotrophs from the genus

Methylocaldum. This newly isolated methanotroph grew on biogas or purified CH4 and successfully converted biogas from AD to methanol. Methanol production was achieved using several methanol dehydrogenase (MDH) inhibitors and formate as an electron donor.

The isolate also produced methanol using phosphate with no electron donor or using formate with no MDH inhibitor. The maximum methanol concentration (0.43±0.00 g L-1) and 48-h

CH4 to methanol conversion (25.5±1.8 %) were achieved using biogas as substrate and a growth medium containing 50 mM phosphate and 80 mM formate.

4.1. Introduction

Biogas produced during anaerobic biodegradation of organic wastes contains methane (CH4) as the major component (30-70%) and it is an emerging renewable energy source. In the United States alone, it is estimated that over 650 billion ft3 of biogas could be captured yearly from landfills and anaerobic digestion (AD) systems (USDA et al., 2014).

This large source of energy could be used to power more than 3 million homes or generate enough compressed natural gas (CNG) to replace 2.5 billion gallons of gasoline (USDA et al., 2014; USDOE, 2014). However, biogas is difficult to store and transport because it is a gas at ambient conditions (Ge et al., 2014). This issue could be addressed by converting biogas to methanol, a valuable liquid chemical that can be used directly as fuel or further converted to other products such as olefins and gasoline. CH4 can be thermo-chemically converted to methanol, but the process uses expensive metal catalysts and operates at high temperatures (200-900°C) and pressures (5-20 MPa) (Riaz et al., 2013). Besides, biogas contains carbon dioxide (CO2, 30-70%) and trace impurities such as hydrogen sulfide (H2S,

0-2000 pm) (Yang et al., 2014), while thermochemical technologies require a CH4 source that is free of impurities (Riaz et al., 2013). Although biogas can be cleaned and upgraded to purified biogas (>97% CH4), the purification processes are usually expensive (Yang et al.,

2014). Biological conversion of biogas to methanol is an emerging, attractive approach as it may not require biogas purification and uses ambient conditions, reducing operational requirements and energy demands (Ge et al., 2014).

Methanotrophs are aerobic bacteria that can convert CH4 to methanol using the enzyme methane monooxygenase (MMO) (Kalyuzhnaya et al., 2015). Under normal conditions, methanotrophs further oxidize methanol to CO2 via methanol dehydrogenase 79

(MDH) and other enzymes in a sequential manner, generating both ATP and reducing power that can be used for other metabolic reactions (Kalyuzhnaya et al., 2015). While MDH inhibitors, such as phosphate and NaCl, have been used to increase methanol production by methanotrophs (Duan et al., 2011; Mehta et al., 1991), inhibition of methanol oxidation by

MDH inhibitors decreases production of ATP and reducing power; thus, another organic substrate, such as formate, is needed to maintain the metabolic activity of methanotrophs.

Recently, electrochemical production of formic acid from CO2 and H2O has been achieved, which provides an opportunity to effectively reduce the cost of formate as an electron donor

(Li et al., 2012; Reda et al., 2008). However, improvements in several aspects are still needed for biological conversion of biogas to methanol to reach industrial scale. Currently, pure CH4 is routinely used for growing methanotrophs. No studies have used methanotrophs to convert biogas from a commercial AD facility to methanol.

Isolation of methanotrophs that can directly use biogas will improve the feasibility of biological conversion of CH4 to methanol. Methanotrophs have been isolated from a variety of sources such as soil, natural gas fields, and waste treatment facilities (Ge et al., 2014), and a few studies have isolated methanotrophs to produce methanol (Han et al., 2013).

Methanotrophs have also been detected and isolated from commercial liquid AD (L-AD) systems that operate at total solids (TS) contents less than 15% (Corder et al., 1986; Rastogi et al., 2009). This indicates that methanotrophs can survive long-term anoxia and may use biogas as their sole source of carbon and energy (Ge et al., 2014). Compared to L-AD, solid- state AD (SS-AD) operates at TS contents greater than 15% (Sheets et al., 2015a), and has higher porosity than L-AD, which may provide higher O2 levels that support the growth of methanotrophs (Ahn et al., 2010). Potentially, methanotrophs isolated from SS-AD could be

used to directly convert biogas to methanol. To date, no methanotrophs have been isolated from SS-AD or used to convert biogas from a commercial AD facility to methanol.

Additionally, no studies have examined the effects of MDH inhibitors and formate on the biological conversion of biogas to methanol by methanotrophs. To address these research gaps, this study aimed to: 1) isolate and characterize methanotrophs from an SS-AD system;

2) compare biogas and purified CH4 as substrates for a methanotrophic isolate; and 3) determine the effects of MDH inhibitors and formate on biological conversion of biogas to methanol.

4.2. Materials and Methods

4.2.1. Isolation of methanotrophs

Eight digestate samples (5 g each) were collected from SS-AD reactors described in

Sheets et al. (2015a). The 1-L SS-AD reactors were fed switchgrass (F) and inoculated with digested wastewater sludge (I) at an F/I ratio of 3. The SS-AD reactors were controlled at either 20% or 30% TS and were incubated under mesophilic (36±1°C) conditions for 70

-1 days. The 70-day CH4 yield of the SS-AD reactors varied from 88 L kg volatile solids

-1 (VSadded) (30% TS) to 113 L kg VSadded (20% TS) (Sheets et al., 2015a). The isolation procedure was a modified version of the protocols described in Bowman (2006) and Dedysh and Dunfield (2011). The SS-AD samples were inoculated into a nitrate mineral salts (NMS) medium (Bowman, 2006) in 250-mL flasks (45 ml NMS per flask). The NMS medium

-1 -1 -1 contained MgSO4·7H2O (1.0 g L ), KNO3 (1.0 g L ), KH2PO4 (0.272 g L ), Na2HPO4

-1 -1 (0.284 g L ), CaCl2·2H2O (0.134 g L ), chelated Fe solution (0.2% (v/v)), and a trace element solution (0.05% (v/v)). The chelated Fe solution contained ferric (III) ammonium 81

citrate (1.0 g L-1), EDTA (2.0 g L-1), and concentrated HCl (0.3% (v/v)) in deionized (DI)

-1 -1 water. The trace element solution contained EDTA (500 mg L ), FeSO4·7H2O (200 mg L ),

-1 -1 -1 ZnSO4·7H2O (10 mg L ), MnCl2·4H2O (3.0 mg L ), H3BO3 (30 mg L ), CoCl2·6H2O (20

-1 -1 -1 - mg L ), CaCl2·2H2O (1.0 mg L ), NiCl2·6H2O (2.0 mg L ), and Na2MoO4·2H2O (3.0 mg L

1) in DI water. The initial pH of the NMS medium was 6.6-6.8. Each of the flasks was sealed with a rubber stopper and its headspace was filled with a filtered (0.2 µm) mixture of purified

CH4 (99% purity) and air at a CH4: air ratio of 1:4 (v/v). The flasks were incubated at 37°C with continuous shaking at 200 rpm. After three days, 5-10 ml sample of each enriched culture was transferred into a new flask that contained 40-45 mL of fresh NMS medium and a similar mixture of CH4 and air. This process was repeated every 3 days. After 25 days, the enriched culture from each flask was spread on an NMS agar plate. The plates were then incubated at 37°C in an anaerobic jar, with a headspace of CH4 and air at a ratio of 1:4 (v/v), for one to two weeks. Individual colonies were picked and streaked on fresh NMS agar plates until a single colony morphology (based on color, size, and shape) were observed. Fifteen isolates were obtained and each was transferred to 1 mL of NMS medium in a 15 mL sealed vial with a headspace containing filtered (0.2 μm) CH4 and air at a ratio of 1:4. The vials were incubated at 37°C with continuous shaking at 300 rpm until there was an observable increase in culture turbidity. Cell morphology was examined by light microscopy. Cultures were considered to be pure if they did not grow in NMS amended with 0.05% glucose

(Dedysh and Dunfield, 2011).

For each methanotrophic isolate, the full-length 16S rRNA gene was amplified and sequenced at the Plant-Microbe Genomics Facility at The Ohio State University, Columbus,

Ohio, USA. The 16S rRNA gene sequences were analyzed and taxonomically classified

using RDP Classifier (Wang et al., 2007) and BLAST against the NCBI RefSeq database.

The 16S rRNA gene sequence of all 15 isolates shared >97% sequence similarity with strains of Methylocaldum, a genus containing obligate methanotrophs. One of the fastest growing isolates, henceforth referred to as Methylocaldum sp. 14B, was selected for further experiments. Strain 14B was isolated from the digestate of a mesophilic (37°C) SS-AD reactor that was operated at 30% TS content and produced biogas composed of 50-60% (v/v)

CH4 and 1-3% (v/v) O2 (Sheets et al., 2015a).

4.2.2. Cultivation with different chemical and physical inputs

To test the ability of strain 14B to utilize various carbon sources, it was aerobically grown in 1 mL of liquid NMS medium supplemented with 0.1% (w/v) of methanol, formate, acetate, xylose, glucose, or citrate. Cultivation was conducted in 15 mL vials at 37°C and 200 rpm. Growth was determined by the change in optical density (OD) at 600 nm and cell morphology was examined by light microscopy.

The effects of copper (Cu2+), NaCl, nitrogen source, pH, and temperature on cell growth using CH4 as the sole carbon source were evaluated by culturing strain 14B in 20 mL of NMS medium in 125 mL flasks with CH4 and air (at 1:4 ratio, v/v) in the headspace.

Copper was tested because it was shown to influence cell growth, especially for those methanotrophs that possess the particulate methane monooxygenase (pMMO) (Kalyuzhnaya

2+ et al., 2015). The Cu concentrations assessed were 0, 1.0, 5.0, and 10 µM of CuCl2 supplemented in NMS. Liquid NMS medium containing 1 µM CuCl2 was amended with 0, 2,

5, 10, or 20 g L-1 NaCl to determine the effects of different salt concentrations. Growth on ammonium was examined by replacing the KNO3 in NMS medium (containing 1 µM CuCl2)

-1 with 1.0 g L ammonium sulfate ((NH4)2SO4). The optimal growth pH was evaluated using

2+ NMS medium containing 1 µM CuCl2 at pH 5.0-7.6. For tests to evaluate Cu , NaCl, nitrogen source, and pH, the cultures were incubated at 37°C with continuous shaking at 200 rpm in a walk-in incubator. To evaluate the effect of temperature, strain 14B was cultured in

- NMS medium (NO3 as nitrogen source, 1 µM CuCl2, no NaCl, initial pH of 6.6-6.8) and incubated at 37, 42 or 47°C with continuous shaking at 200 rpm. Cell growth was determined by measurement of the optical density (OD600nm), and growth rate was calculated using the

OD values. For each of the above tests, at least two replicates were performed. Gases were filtered (0.2µm) prior to use.

4.2.3. Cultivation on different methane sources

Biogas was collected from quasar energy group’s commercial scale digester

(Wooster, OH, USA) that treats food waste. According to gas chromatography (GC) analysis, the biogas was composed of 69.8% CH4, 28.8% CO2, 1.0% N2, and 0.4% O2. The biogas had less than 50 ppm of H2S as measured using the Dräger Short Term Detector Tubes (Fisher

Scientific, Hampton, NH, USA), since the AD facility implements control technologies to reduce H2S content in the biogas (quasar energy group, 2015). Purified CH4 (99% purity) was purchased from Praxair® (Danbury, CT, USA). Kinetic parameters for substrate consumption and cell growth were determined by cultivating strain 14B with either purified CH4 (99% purity) or the biogas as the carbon source. Strain 14B was cultured at 37°C and 200 rpm in 50 mL of NMS medium (w/ 1 µM CuCl2) in 250-mL flasks with a 500-mL Tedlar gas bag connected to each flask. The headspace and Tedlar gas bag contained CH4 and air at a 1:4 ratio (v/v) or biogas and air at a 1:2.5 ratio (v/v). These two gas ratios provided a comparable

initial headspace concentration of ~20% CH4 (v/v) and 16-18% O2 (v/v) for aerobic cultivation (Bowman, 2006; Dedysh and Dunfield, 2011). Cell growth (OD600nm) and gas composition (CH4, O2, N2, CO2) were measured (via GC) periodically until the stationary phase of growth was reached. Three replicates were performed.

4.2.4. Methanol formation

To screen for effective MDH inhibitors, strain 14B cells grown to the exponential/early stationary phase were harvested by centrifugation (10,000 rpm, 15 min) and then resuspended in 4.1 mL of NMS medium (w/ 1 µM CuCl2) containing either 0 mM or 80 mM sodium formate and varied concentrations of MDH inhibitors. The initial OD600nm of the cell suspension was 0.89±0.02. EDTA (0.5, 5.0 mM), phosphate buffer (100, 200 mM as KH2PO4/ Na2HPO4), NaCl (100, 200 mM), and MgCl2 (20, 40 mM) were tested as potential MDH inhibitors (Ge et al., 2014). Before incubation, a 1-mL sample of the cell suspension was removed and filtered (0.2 µm) and the filtrate was analyzed by GC to test for the initial presence of methanol. The remaining 3.1 mL of the cell suspension was transferred to 40-mL glass vials, which were each sealed with a rubber stopper. Six mL of purified CH4

(99% purity) was added to each vial to reach a CH4: air ratio of 1:5.15 in the headspace. All the vials were then incubated at 37°C with continuous shaking at 200 rpm. After 6 hours, the cell suspension was filtered and the filtrate was subjected to GC analysis for methanol concentration. Two replicates were performed for each vial. The volume of CH4 (6 mL) was selected to provide similar quantities of CH4 (0.25 mmol) and O2 (0.27 mmol) to the reactor headspace, while 80 mM formate was used to provide an essential amount of formate (0.25 mmol) corresponding to the CH4 and O2 levels in the headspace.

The effects of formate and phosphate on biogas to methanol conversion were evaluated using a 4 x 4 factorial design to test four levels of phosphate (0, 50, 100, and 200 mM as KH2PO4/ Na2HPO4) and four levels of sodium formate (0, 40, 80, and 120 mM). The reactors were set up as described above (3.1 mL cell suspension and 40 mL reactor volume) except biogas was the source of CH4 (9 mL, biogas: air ratio of 1:3.1 in the headspace) and the initial OD600nm was 0.67±0.01. At least three replicates were performed for each condition except for those with 0 mM phosphate, which had two replications. Low formate concentrations (10-20 mM) were not used because a preliminary experiment showed that methanol production was inconsistent at these levels (data not shown).

The time course of methanol production was examined by cultivating strain 14B at

37°C and 200 rpm in 50 mL of NMS medium (containing 1 µM CuCl2 and 50 mM phosphate) with different sodium formate levels (40, 80, or 120 mM) in 250-mL flasks with a

500-mL Tedlar gas bag connected to each flask. Biogas was the CH4 source and the initial

OD600nm of the cell suspension was 0.75±0.02. The headspace and Tedlar gas bag contained biogas and air at a 1:2.5 ratio (v/v). Cell density (OD600nm), gas composition (CH4, O2, N2,

CO2), and methanol content were measured periodically. Three replicate reactors were set up for each condition. The gas composition data from one reactor (one replicate at 80 mM formate) were not included because of gas measurement error, and all the data from another reactor (one replicate at 120 mM formate) were not used due to culture seal failure.

4.2.5. Analytical methods

Samples of the cell suspension (1 mL) were collected for OD analysis. Optical density of the cell suspension was measured at 600 nm with an Eon® microplate

spectrophotometer (Biotek, Winooski, VT, USA). Each sample (200 µL) was measured at least three times. The specific growth rate (µ, h-1) was determined by

OD μ= [ln( t )] /t (Eq. 4.1) OD0

where OD0 is the OD600nm at the beginning of the exponential growth phase, ODt is the

OD600nm at the end of the exponential growth phase, and t is the time period of the exponential growth phase (h). The pH of cultures was measured using a pH probe.

A linear relationship was determined between the OD at 600 nm and bacterial biomass. The dry weight of cell suspension was determined using an adapted version of the methods described in Cai et al. (2013). Briefly, 50 mL samples of cell suspension of known

OD600nm were centrifuged at 10,000 rpm for 15 min. The supernatant was discarded and the cell pellet was resuspended in 25 mL of 0.5 M NH4HCO3 to remove residual salts. The samples were centrifuged again at 10,000 rpm for 15 min. After the supernatant was discarded, the cell pellet was resuspended in 3 mL of 0.5 M NH4HCO3 and transferred to a pre-ignited (550°C) porcelain crucible and dried in a Thelco Model 18 oven (Precision

Scientific, Chennai, India) at 105°C for 12 h. Ash weight was determined by heating the samples in an Isotemp muffle furnace (Fisher Scientific, IA, USA) at 550°C for four hours.

The cell yield (Yx/CH4) for batch cultivations was determined by dividing the ash free dry weight (AFDW) at the stationary phase by the amount of CH4 consumed during cultivation.

The composition of headspace gas (CO2, CH4, N2, and O2) was analyzed by GC according to methods described in Sheets et al. (2015a). The methanol content of filtrate samples was analyzed using a GC (Shimadzu, 2010PLUS, Columbia, MD, USA) equipped with a Stabilwax polar phase column (30 m × 0.32 mm × 0.5 µm) and flame ionization

detector. The temperatures of the injector and detector were both set at 250°C. The oven temperature was set at 50°C and gradually increased to 80°C at a rate of 5.0°C min-1. Helium was used as the carrier gas with a total flow rate of 24.8 mL min-1 and split ratio of 15. The concentration of methanol (g L-1) was determined using an external standard curve prepared using pure methanol. Methanol productivity (g L-1 d-1) was determined by dividing the

-1 methanol content (g L ) by the cultivation time. CH4 to methanol conversion (%) was determined by dividing the moles of methanol produced by the moles of CH4 consumed.

4.2.6. Statistical analysis

Statistical significance was determined with analysis of variance (ANOVA, α=0.05) or Tukey’s Honestly Significant Difference (HSD) test (α=0.05) using JMP Statistical

Software from SAS Institute Inc. (Version 10.0.2, Cary, NC, USA). Experimental data are presented as average values ± standard error.

4.3. Results and Discussion

4.3.1. Isolation and characterization of a methanotrophic strain from digestate

When the enrichment cultures were grown on agar plates with CH4 as the sole substrate, shiny, smooth, butyrous/cartilaginous light brown or brown colonies formed. Strain

14B was one of the fastest growing isolates, and it was further characterized. An increase in culture turbidity was observed within 3-5 days after a colony of strain 14B was inoculated into liquid NMS medium with 20% CH4 in the headspace. Slow growth was observed on

0.1% methanol, but no growth was detected on 0.1% (w/v) formate, acetate, xylose, glucose, or citrate. These results indicated that strain 14B was an obligate methylotroph. The V1-V3

region of its 16S rRNA gene shared 98%, 99%, and 98% sequence similarity with that of

Methylocaldum gracile, Methylocaldum tepidum, and Methylocaldum szegediense, respectively, all of which are obligate methanotrophs (Bodrossy et al., 1997). Strain 14B also had morphological and physiological characteristics comparable to those of Methylocaldum species, especially M. gracile and M. tepidum (Table 4.1) (Bodrossy et al., 1997; Eshinimaev et al., 2004). The type strain of M. gracile was isolated from activated sludge (Bodrossy et al., 1997; Eshinimaev et al., 2004; Ge et al., 2014), and strain 14B might also come from the inoculum of the SS-AD reactors. Additionally, strain 14B had a similar growth rate when

- -1 + -1 + either NO3 (0.048 h ) or NH4 (0.045 h ) was used as the nitrogen source. Because NH4 is

+ the primary component of total nitrogen in AD, strain 14B may have used NH4 as a nitrogen source in the SS-AD reactors (Shi et al., 2014).

The pH range for growth was 6.0 to 7.6 (no clear optimum), which was slightly lower than the pH of digestate samples (pH=8.0) (Sheets et al., 2015a). Strain 14B had stable growth within the mesophilic temperature range of 37-42°C, but did not grow at or above

47°C. NaCl concentration above 2 g L-1 significantly reduced the specific growth rate

(p<0.05) and no growth was observed above 10 g L-1 NaCl, which was comparable to the

Methylocaldum strains isolated by Eshinimaev et al. (2004). Similar to the Methylocaldum strains isolated by Bodrossy et al. (1997), strain 14B grew slowly in NMS medium without

2+ Cu supplement, but grew well at 1.0-10.0 µM CuCl2. As minimizing input costs is critical for scale-up of the process, 1.0 µM CuCl2 was used in remaining experiments. To the authors’ knowledge, this is the first methanotrophic strain isolated from SS-AD, which confirms that AD systems can be a source to isolate methanotrophs (Corder et al., 1986; Ge et al., 2014).

4.3.2. Comparison of growth on different methane sources

Strain 14B was able to grow using either biogas or purified CH4 as the primary carbon source (Figure 4.1), and no significant difference (Tukey HSD, p>0.05) between biogas and purified CH4 was noted for growth rate, CH4 consumption, or biomass yield of

-1 strain 14B (Table 4.2). Additionally, strain 14B had a growth rate (µ=0.06 h ) and CH4 conversion rate (0.68-0.71 mmol L-1 h-1) comparable to those of Methylosinus trichosporium

OB3b (µ=0.024-1.0 h-1, 0.8-0.9 mmol L-1 h-1), the most commonly studied methanotroph for

CH4 to methanol conversion (Duan et al., 2011; Ge et al., 2014; Mehta et al., 1991;

-1 Rostkowski et al., 2013). Lower cell yield (0.2 g cells g CH4) compared to M. trichosporium

-1 OB3b (0.5-0.7 g cells g CH4) indicated strain 14B oxidized CH4 primarily for energy generation rather than for biomass production (Kalyuzhnaya et al., 2015; Rostkowski et al.,

2013).

The main difference between biogas and purified CH4 is that biogas contains CO2 and

H2S. CO2 can lower culture pH and has also been shown to inhibit MDH activity (Xin et al.,

2004b). However, the pH of the cultures grown on biogas was close to neutral (6.0-6.8) because of sufficient buffering in the NMS medium. Lack of methanol accumulation during all growth experiments also suggested that MDH activity was not inhibited by the CO2 present in the biogas. Additionally, the H2S content of the biogas was less than 50 ppm, which was much lower than the levels inhibitory to methanotrophs (>100 ppm) (Cáceres et al., 2014). Overall, biogas from a commercial scale AD system was a suitable growth substrate for strain 14B. This indicates that the costly purification steps used in conventional biogas upgrading are not needed for a biological upgrading process that uses this methanotroph and biogas with low H2S content (Ge et al., 2014; Yang et al., 2014). Biogas 90

produced at other AD facilities may have higher H2S content and could negatively impact the growth of methanotrophs. Further studies could be conducted to determine the inhibitory levels of H2S on biological conversion of biogas to methanol (Cáceres et al., 2014).

4.3.3. Screening of methanol dehydrogenase inhibitors for methanol production

All the tested MDH inhibitors caused strain 14B to accumulate methanol (Table 4.3), indicating several different chemicals can be used for this purpose (Hwang et al., 2015).

Surprisingly, methanol was accumulated even without the addition of MDH inhibitors (i.e. 80 mM formate). In some cases, a small amount of methanol was also produced when only

MDH inhibitors were added, for example 200 mM phosphate (Table 4.3), suggesting not all of the methanol produced was further oxidized. Methanol production at different levels of formate and phosphate was further investigated with a full factorial experiment (Section

4.3.4).

4.3.4. Effects of formate and phosphate concentrations on methanol production

In the absence of formate, methanol production increased linearly (R2=0.94) with increasing phosphate concentration (Figure 4.2), indicating higher levels of phosphate caused a decrease in MDH activity (Duan et al., 2011). However, the maximum methanol content in those cultures was low (0.04-0.05 g L-1 at 200 mM phosphate). This is likely attributed to the limited supply of reducing equivalents for CH4 to methanol conversion by MMO (Ge et al.,

2014). When 40 mM formate were added, methanol productivity increased by 4- to 22-fold

(Figure 4.2), because formate dehydrogenase (FDH) oxidized the exogenous formate to produce electrons for methanol production (Mehta et al., 1991; Takeguchi et al., 1997).

Interestingly, high methanol productivities (0.5-1.0 g L-1 d-1) were obtained in reactors supplied with formate but not phosphate (Figure 4.2). This result indicated that electron generation by FDH was the limiting factor for methanol production by strain 14B and may have been caused by an already low MDH activity under those cultivation conditions.

Previously, Eshinimaev et al. (2004) showed that FDH in M. szegediense had much higher activity (85-211 nmol min-1 mg-1 protein) than MDH (2-3 nmol min-1 mg-1 protein) at 37°C.

Therefore, strain 14B may have rapidly generated electrons that were used to oxidize CH4 to methanol faster than MDH could convert methanol to formaldehyde. Additionally, Leak and

Dalton (1983) showed that MMO activity in Methylococcus capsulatus (Bath) was stimulated by formate (5 mM). Higher activities of MMO and FDH and low activity of MDH might have contributed to methanol accumulation. The results from this study suggest that high concentrations of formate and small amounts of MDH inhibitors can be used to promote methanol production by strain 14B. After plating the seed culture that was used for one replicate of the full factorial experiment on NMS solid medium with 0.05% glucose, a non- methanotrophic bacterial isolate was observed. However, methanol production was consistent among all replicates (Figure 4.2), indicating the process was resistant to contamination. No other non-methanotrophic bacteria were observed during any other experiments.

The maximum methanol productivity (1.09 g L-1 d-1) was obtained in reactors supplied with 50 mM phosphate (Figure 4.2). Currently, this is the third highest reported methanol productivity, behind that reported by Mehta et al. (1991) (4.1 g L-1 d-1) and Hwang et al. (2015) (1.18 g L-1 d-1) and is supported by a recent review by Ge et al. (2014). A kinetic study was conducted to further elucidate the effects of formate on biogas to methanol conversion (Section 4.3.5).

4.3.5. Effects of formate concentration on biogas to methanol conversion

Methanol production increased linearly (from 0 g L-1 to 0.23-0.27 g L-1) in the first 12 h at all formate concentrations (Figure 4.3). However, faster CH4 consumption and higher

CO2 production were observed in reactors with low formate concentration (40 mM). In those reactors, methanol production also ceased after 12 hours, and CO2 and cell biomass increased rapidly (Figure 4.3a). This suggests that reducing power had been depleted in those reactors and strain 14B had to use methanol as a source of energy and reducing power (Ge et al.,

2014). Consequently, the 48-h CH4 to methanol conversion ratio was 0.0±0.0% in reactors with 40 mM formate.

More than 40 mM formate were needed to maintain methanol production. For example, reactors with 80 mM and 120 mM formate produced methanol at a peak level

(0.34-0.43 g L-1) in 48-50 h, and then declined after 50 h (data not shown). This is a commonly observed phenomenon that has been attributed to mass transfer limitations and/or cell inhibition caused by methanol accumulation (Kim et al., 2010; Pen et al., 2014). As an example, Furuto et al. (1999) showed that methanol concentrations above 0.32 g L-1 could inhibit pMMO activity in M. trichosporium OB3b. Decreases in pMMO activity would explain the lower CH4 oxidation rates (Figure 4.3b and c) and the decline in cell density after methanol accumulated in reactors at 80 mM and 120 mM formate (Figure 4.3b and c).

Reduced methanol production at 120 mM formate was likely because the higher formate concentrations can inhibit the activity of the FDH enzyme (Yoch et al., 1990). Decreased

FDH activity would reduce the amount of electrons available for MMO, thereby lowering methanol production (Yoch et al., 1990). The 48-h CH4 to methanol conversion ratio was

23.3±0.7% in reactors with 120 mM formate. 93

Overall, maximum 48-h CH4 to methanol conversion ratio (25.5±1.8%) and methanol concentration (0.43±0.00 g L-1) were obtained in reactors with 80 mM formate

(Figure 4.3b). In general, these results are comparable to others (conversion=27-80%, methanol concentration=0.44-1.12 g L-1) (Corder et al., 1986; Duan et al., 2011; Ge et al.,

2014; Kim et al., 2010). Although Duan et al. (2011) achieved a methanol content of 1.12 g

-1 L , they used 5% paraffin to increase gas-liquid mass transfer of gaseous substrates (CH4,

-1 O2) and to maintain a very high cell density (>1.0 g L ) (Duan et al., 2011). It is reasonable to envisage that comparable or higher methanol productivities could be obtained with strain

14B using reactors that are designed to overcome mass transfer limitations to provide high cell densities (Pen et al., 2014). Currently, the high price of formate limits its use as an electron donor for biological conversion of biogas to methanol. Economic feasibility could be improved by genetically modifying methanotrophs to use carbon sources in wastewater (i.e. acetate) as electron donors. Furthermore, isolation of methanotrophs that can use renewable electricity as an electron source is an attractive area of future research (Ge et al., 2014).

4.4. Conclusion

A methanotrophic bacterial isolate with similar characteristics to Methylocaldum species was isolated from solid-state anaerobic digestate. The isolate successfully grew on biogas and purified CH4 as the carbon source. The methanotrophic isolate also converted biogas to methanol. Several methanol dehydrogenase (MDH) inhibitors increased methanol accumulation from biogas. Methanol production was also achieved by adding only phosphate to the cultivation medium, but was improved when formate was included. High levels of methanol could also be attained using formate and no MDH inhibitor.

Table 4.1: Comparison of growth characteristics of strain 14B to those of Methylocaldum

species

Strain 14B M. gracile M. tepidum M. szegediense Cell Coccus/ Thin rod/ Rod/ Rod/ morphology pleomorphic coccus pleomorphic Pleomorphic

Motility + + + +

Irregular, shiny, Irregular, shiny, Irregular, shiny, Irregular, shiny, smooth, smooth, Colony smooth, smooth, butyrous/cartilaginou pasty/cartilagino description butyrous/cartilagino mucoid/cartilaginous, s, light brown to us, brown to us, light brown brown to dark brown brown dark brown

Color in Light brown to brown Brown Brown Brown liquid

Growth on C2-C6 - - - - compounds

Growth on other C1 +a ND - - compounds

Cu2+ range 1.0-10 µMb ND 1 µMc 1 µMc

Temperature 37-42 20-47 30-47 37-62 range (°C)

pH range 6.0-7.6d ND ND ND

Nitrogen NO -, NH + NO -,e NO -,e NO -,e sources 3 4 3 3 3

NaCl range 0-2 g L-1 ND ND ND

Isolation Solid-state anaerobic Anaerobic effluent of Activated sludge Agricultural soil source digestate hot spring

Bodrossy et al. Bodrossy et al. Bodrossy et al. Reference This study (1997) (1997) (1997) a. slight increase in turbidity in 0.1% methanol b. no significant difference (Tukey HSD, p>0.05) in growth rate at each level. 1.0 µM selected as preferred level c. only concentration reported d. no growth below pH 5.5 e. only nitrogen source reported ND=not determined 95

Table 4.2: Effect of CH4 source on growth of strain 14B

CH4 source Growth rate Yield CH4 consumption -1 -1 -1 -1 (h ) (g cells g CH4) (mmol L h )

Pure CH4 0.062±0.00 0.19±0.02 0.68±0.03

Biogas 0.056±0.00 0.20±0.04 0.72±0.05

Table 4.3: Effects of MDH inhibitors on methanol production

MDH inhibitor MDH inhibitor Formate Methanol productivity concentration (mM) (g/L/d) (mM)

None 0 80 0.98±0.05 0 0 ND

EDTA 0.5 80 1.12±0.03 5.0 80 1.04±0.14 5.0 0 ND

Phosphate 100 80 1.17±0.04 200 80 0.95±0.13 200 0 0.28±0.00

NaCl 100 80 1.00±0.04 200 80 0.85±0.09 200 0 0.08a

MgCl2 20 80 0.98±0.13 40 80 1.04±0.01 40 0 ND a. detectable in only one sample Purified CH4 used as substrate

0.5 30 Cell Density Methane (a) 0.4 Oxygen 25 Carbon dioxide 20 0.3 15 0.2

10 Gas (%) Gas composition

0.1 5 Cell density (OD @ 600 nm) 600 @ density (OD Cell 0.0 0 0 20 40 60 80 100 Time (h)

0.5 Cell Density 30 Methane (b) Oxygen 25 0.4 Carbon dioxide 20 0.3 15 0.2

10 Gas (%) Gas composition

0.1 5 Cell density (OD @ 600 nm) 600 @ density (OD Cell 0.0 0 0 20 40 60 80 100 Time (h)

Figure 4.1: Growth, gas consumption and gas production using purified CH4 (a) and biogas

(b) as a substrate.

1.5 0 mM formate 40 mM formate 80 mM formate 120 mM formate

1.0

0.5 Methanol productivity (g/L/d) productivity Methanol

0.0 * 0 50 100 200 Phosphate (mM)

Figure 4.2: Effects of phosphate and formate concentrations on methanol production from

biogas. *=no methanol detected.

1.0 30 Cell Density Methanol (a) Methane 25 0.8 Oxygen Carbon dioxide 20 0.6 15 0.4 10

0.2 (%) Gas composition

5 Cell density and methanol (g/L) methanol density and Cell 0.0 0 0 10 20 30 40 50 Time (h)

1.0 30 Cell Density Methanol (b) Methane 25 0.8 Oxygen Carbon dioxide 20 0.6 15 0.4 10

0.2 (%) Gas composition

5 Cell density and methanol (g/L) methanol density and Cell 0.0 0 0 10 20 30 40 50 Time (h) Continued

Figure 4.3: Time course of methanol production at 40 mM formate (a), 80 mM formate (b),

and 120 mM formate (c).

100

Figure 4.3: Continued

1.0 30 Cell Density Methanol (c) Methane Oxygen 25 0.8 Carbon dioxide 20 0.6 15 0.4 10

0.2 (%) Gas composition

5 Cell density and methanol (g/L) methanol density and Cell 0.0 0 0 10 20 30 40 50 Time (h)

101

Chapter 5: Development and Evaluation of a Trickle Bed Bioreactor for Enhanced Mass Transfer and Methanol Production from Biogas

Johnathon P. Sheetsa, Kathryn Lawsona, Xumeng Gea,c, Lingling Wangb, Zhongtang Yub,

Yebo Lia,c

a. Department of Food, Agricultural and Biological Engineering, The Ohio State

University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH, 44691-4096, USA b. Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA

c. quasar energy group, 8600 E. Pleasant Valley Rd., Independence, OH, 44131

Biological conversion of the biogas produced by landfills and anaerobic digestion systems (60-70% methane (CH4), 30-40% carbon dioxide (CO2)) to methanol using methanotrophs (aerobic CH4-oxidizing bacteria) is an emerging approach to convert waste- derived biogas to liquid chemicals and fuels. The purpose of this work was to develop a trickle-bed reactor (TBR) to improve mass transfer of CH4 and oxygen (O2) to methanotroph growth media for enhanced CH4 oxidation and methanol production. Mass transport of O2 in a TBR packed with ceramic balls was nearly two-fold higher than an unpacked TBR. CH4 oxidation in the TBR (0.4-0.6 mmol/h) was about four times higher than that in shake flasks that used similar inoculum and headspace:volume and biogas:air ratios. Using optimal operating parameters (biogas:air=1:2.5, 12 mmol formate addition, 3.6 mmol phosphate), 102

methanol productivity (0.9 g/L/d) from the non-sterile TBR was among the highest reported in the literature. Operation under non-sterile conditions caused differences in the microbial community composition between experiments, and the most predominant methanotrophs appeared to be members of the genus in which the inoculum is classified (Methylocaldum sp.

14B).

5.1. Introduction

Methane (CH4) is a valuable energy source, but it is also a potent greenhouse gas that has ~25 times the 100-year global warming potential of carbon dioxide (CO2) (USEPA,

2016a, 2016d). In fact, nearly 11% of all of the greenhouse gases produced in the United

States each year are due to CH4 emissions from human activities (>700 million metric tons of

CO2 equivalent) (USEPA, 2016d). Two of the most important sources of those CH4 emissions are landfills (20%) and manure management sites (8%) (USEPA, 2016a, 2016d), where anaerobic microorganisms convert organic wastes to biogas (30-70% CH4, 30-70%

CO2, 0-2000 ppm hydrogen sulfide (H2S)) that is released directly to the atmosphere

(USEPA, 2016a). Promising opportunities to address this issue include the installation of biogas recovery systems at landfills and the diversion of organic wastes to engineered anaerobic digestion (AD) systems (USDA et al., 2014). In both cases, biogas can be captured and used as a source of renewable fuel, such as compressed natural gas (CNG), or can be converted to liquid chemicals (i.e. methanol) via thermochemical methods (Yang et al.,

3 -1 2014). However, many landfills produce biogas with flow rates (10-15 m h ) and CH4 contents (<30%) that are too low to implement cost-effective gas recovery systems (Estrada et al., 2014b; Kim et al., 2013; Yoon et al., 2009). Additionally, the processes to clean, store,

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transport, upgrade, and thermochemically convert biogas have high costs and energy demands (Yang et al., 2014). Furthermore, the low price (<$3 per million ft3, industrial price) of natural gas (>90% CH4) has made the use of biogas for renewable energy unattractive

(USEIA, 2017a). Therefore, mitigation of human-induced, waste-derived CH4 emissions requires development of flexible, low-cost technologies that can directly convert biogas to easily transportable fuels and chemicals.

Biological upgrading of biogas with methanotrophs (aerobic CH4 oxidizing bacteria) is an attractive approach to valorize waste-derived CH4, because methanotrophs grow at moderate temperatures and ambient pressures, can use CH4 at low concentrations (<20%), and can produce liquid chemicals such as methanol with high efficiency (Ge et al., 2014; Han et al., 2013; Wei et al., 2016). Methanotrophs convert CH4 and O2 to methanol using the methane monooxygenase (MMO) enzyme. Normally, methanol is further oxidized to formaldehyde via methanol dehydrogenase (MDH). Then, formaldehyde is either assimilated into biomass or eventually oxidized to CO2 and H2O by other enzymes to generate energy for metabolic reactions (Kalyuzhnaya et al., 2015). Thus, MDH inhibitors and external electron donors such as formate are needed to support methanol production by methanotrophs (Ge et al., 2014). Electrochemical catalysis and photocatalysis of CO2, direct hydrogenation of CO2, and selective oxidation of biomass are promising approaches to produce renewable and low- cost formate (Taheri and Berben, 2016; Yishai et al., 2016). There are also several studies that have used pure cultures of methanotrophs to convert clean CH4 (>99% CH4) to methanol. However, few have used reactor design (i.e. membrane bioreactor, continuous stirred tank reactor (CSTR)) to address the important issue that biological upgrading of CH4

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can be limited by the low solubility and mass transport of substrate gases (CH4, O2) in methanotroph growth medium (Duan et al., 2011; Kim et al., 2010; Pen et al., 2014).

Trickle bed reactors (TBRs) are an intriguing design for methanotroph cultivation, because they have limited power requirements, low capital costs compared to membrane bioreactors, and favorable mass transfer properties compared to CSTRs (Deshusses and Cox,

1999; Devarapalli et al., 2016; Kraakman et al., 2011). TBRs are cylindrical reactors packed with an inert material that has a high specific surface area (Ranade et al., 2011). Nutrient medium is circulated through the TBR to provide a thin liquid layer on the packing surface, and gases are pumped either co-current (with) or counter-current (against) to the liquid

(Devarapalli et al., 2016). The thin liquid film has a low resistance to mass transport, allowing gases to be rapidly transferred to the biocatalyst (Devarapalli et al., 2016). In biological TBRs, both immobilized cells on the packing surface and suspended cells in the liquid medium have been shown to contribute in gas conversion (Cáceres et al., 2016;

Devarapalli et al., 2016; Iliuta and Larachi, 2006; Yoon et al., 2009). TBRs have been designed for anaerobic fermentation of syngas (CO, H2) to ethanol (Devarapalli et al., 2016).

Additionally, the continuous methanotrophic biotrickling filter is an example of a TBR used for oxidation of dilute CH4 streams (0-2%) to CO2 (Cáceres et al., 2016; Yoon et al., 2009).

However, there are no published reports on the use of TBRs for biological conversion of biogas to methanol. Therefore, the objective of this study was to develop a TBR for CH4 conversion and methanol production from biogas. The TBR was inoculated with a mixed culture containing methanotrophs classified in the genus Methylocaldum, and was operated non-sterilely throughout the study. Several operating conditions were varied to test the

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performance and robustness of the TBR. Subsequently, the microbial community in the TBR at different operating phases was investigated.

5.2. Materials and Methods

5.2.1 TBR set-up

The trickle bed reactor (TBR) was made of rigid clear polyvinyl chloride (PVC)

(H=686 mm, ID=51 mm) with a rounded bottom and an airtight rubber cap (Figure 5.1). The

TBR was randomly packed with 4.81±0.39 mm KRYPTOKNIGHTTM ‘M’ Inert Ceramic

Balls (Koch Knight LLC, East Canton, OH, USA) onto a wire mesh disc (20×20 mesh, D=51 mm, McMaster Carr, Aurora, OH, USA) fitted approximately 76 mm above the reactor bottom. According to supplier documentation, the apparent free space, water absorption, apparent porosity, packing density, and specific gravity of the ceramic balls were reported at

40%, 1.0%, 2.0%, 1362 kg/m3 (85 lb/ft3), and 2.3 g/cm3 (144 lb/ft3), respectively (Koch

Knight LLC, 2016). The total packed bed height was 508 mm, which provided a 0.21 L headspace at the top (H=102 mm) and an approximately 0.16 L liquid holding reservoir at the bottom (H=76 mm). Gas and liquid were circulated in flexible PVC tubing (5.2 mm ID) using peristaltic pumps (MasterFlex L/S Easy Load II, Cole-Parmer, Chicago, USA). The liquid inlet was at the top of the reactor, and liquid was distributed through a 5.2 mm plastic orifice centered over the packed bed. The liquid outlet was at the bottom, where liquid was pumped back upward to the liquid inlet. Gas was pumped counter-current to liquid flow through an inlet at the bottom of the TBR. The gas outlet line at the top of the reactor was connected to a 560 mL Erlenmeyer flask with an inlet, an outlet, and gas sampling and feeding ports. Several three-way valves for liquid and gas sampling were fitted to circulation 106

lines. The TBR volume (1.24 L) was determined by taking the sum of the volumes of distilled and deionized (DI) water needed to fill the packed bed reactor (0.62 L), circulation tubing (0.06 L), and Erlenmeyer flask (0.56 L) (Honda et al., 2016). The headspace volume

(VH) was calculated by subtracting the volume of liquid added to the reactor (VL) from the

TBR volume. The TBR was placed in a walk-in incubator (36±1ºC) throughout the study.

5.2.2. Gas feeding procedure

The TBR was supplied with either purified CH4 (99% purity, Praxair, Danbury, CT,

USA) or biogas sampled from a commercial anaerobic digester that was fed food waste

(quasar energy group, Wooster, OH, USA). The biogas was sampled from the digester at several different times. Thus, the average composition of the biogas samples was 67.7±2.8%

CH4, 29.9±4.1% CO2, 3.2±3.0% N2, and 1.2±1.0% O2 according to gas chromatography (GC) analysis. Also, the H2S content in the biogas varied from <50 ppm (lowest detection limit) to

400 ppm (Dräger Short Term Detector Tubes, Fisher Scientific, Hampton, NH, USA).

Prior to gas feeding, the TBR was purged of residual gases by continuously pumping ambient air through the system for 10-15 min. Then, headspace gas was removed from the reactor with a plastic syringe (Figure 5.1, item 5) to reduce the pressure in the TBR headspace. A Tedlar gas bag filled with purified CH4 or biogas (Figure 5.1, item 3) was then attached to the gas feeding and sampling port of the gas sampling/feeding flask (Figure 5.1, item 4). The headspace was relieved back to ambient pressure by allowing the purified CH4 or biogas back into the TBR headspace. The volume of the gas removed from the headspace was modified to control the ratio of biogas to air (v/v) in the TBR headspace. The headspace gas was circulated through the TBR for at least 10 min, and then the initial headspace gas

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composition (CH4, O2, and CO2) was measured via GC. After a set period of time (0-24 h), the headspace gas was analyzed for CH4, O2, and CO2 contents (via GC) to determine CH4 removal, O2 removal, and CO2 production. Ambient air was again circulated through the

TBR, and then purified CH4 or biogas was fed by vacuum pressure relief. The reactor was operated under two phases. Phase 1 involved optimization of operating parameters using purified CH4 and examined the effect of biogas:air ratio, and Phase 2 focused on starting up the reactor on biogas under non-sterile conditions and studying the impacts of formate and biogas:air ratios on methanol production. Detailed procedures for each operating phase are described in Sections 5.2.3-5.2.4 and are summarized in Table 5.1. After initial inoculation in

Phase 1.1, the bioreactor was operated non-sterilely. The nitrate mineral salts (NMS) medium was autoclaved in order to dissolve the salts, but was exposed to the atmosphere of the walk- in incubator each time it was added to the TBR (Phases 1 and 2).

5.2.3. Phase 1: Optimization of operating parameters and effects of biogas

5.2.3.1. Phase 1.1: Optimization of TBR operating parameters using purified CH4 as substrate

The TBR was first inoculated with Methylocaldum sp. 14B, an obligate mesophilic methanotroph isolated from solid-state anaerobic digestate (Sheets et al., 2016). Based on previous work, strain 14B had similar morphological and physiological characteristics and shared >98% 16S rRNA gene sequence similarity with Methylocaldum gracile and

Methylocaldum tepidum. Strain 14B was stored in NMS medium with 10% DMSO at -80°C prior to use. The NMS medium (pH=6.7±1) was composed of 1.0 g/L KNO3, 1.0 g/L

MgSO4·7H2O, 0.816 g/L KH2PO4, 0.852 g/L Na2HPO4, 0.134 g/L CaCl2·2H2O, 0.2% (v/v) 108

chelated iron solution, 0.05% (v/v) trace element solution, and 1.0 μM CuCl2. The chelated iron solution contained ferric (III) ammonium citrate (1.0 g L-1), EDTA (2.0 g L-1), and concentrated HCl (0.3% (v/v)) in deionized (DI) water. The trace element solution contained

-1 -1 -1 EDTA (500 mg L ), FeSO4·7H2O (200 mg L ), ZnSO4·7H2O (10 mg L ), MnCl2·4H2O (3.0

-1 -1 -1 -1 mg L ), H3BO3 (30 mg L ), CoCl2·6H2O (20 mg L ), CaCl2·2H2O (1.0 mg L ), NiCl2·6H2O

-1 -1 (2.0 mg L ), and Na2MoO4·2H2O (3.0 mg L ) (Bowman, 2006; Sheets et al., 2016). A frozen stock culture of 14B was thawed and spread on solid NMS medium (included agar), and was incubated at 37°C in a gas tight jar with a filtered (0.2 μm) headspace mixture of purified

CH4 (99% purity) and air at a 1:4 CH4:air ratio (v/v) until single colonies formed. Single colonies were then transferred to 1 mL NMS broth in pre-sterilized gas tight test tubes, and a

CH4 headspace was supplied (1:4 CH4:air ratio (v/v)) using a sterile needle syringe. The culture was then scaled up to 50 mL of NMS medium in 250 mL flasks. The flasks were sealed with rubber stoppers and then supplied with a filtered (0.2 μm) mixture of purified

CH4 (99% purity) and air at a 1:4 CH4:air ratio (v/v). The flasks were incubated at 37ºC with continuous shaking (200 rpm) for two to three days until the optical density (OD600nm) of the culture reached approximately 0.3. The TBR packing was pre-wetted with DI water prior to inoculation, and it was verified that abiotic CH4 removal did not occur (i.e. there was no CH4 removal when the reactor was not inoculated with the methanotroph). A 200 mL culture of

Methylocaldum sp. 14B (OD=0.3) was then supplied to the liquid sampling port of the TBR.

For 21 days, four reactor parameters (gas circulation, liquid circulation, nutrient dilution, and liquid volume) were varied to examine their impacts on CH4 removal and visually observable flow stability (Table 5.1). Purified CH4 was added to the TBR headspace at a 1:5.5-1:7.7

CH4:air ratio every 8-24 h according to the methods described in Section 5.2.2.

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5.2.3.2. Phase 1.2: Impacts of biogas: air ratio

On day 21 of Phase 1, the CH4 source for the TBR was changed to biogas. The TBR was fed with biogas at three different biogas:air ratios to determine the effects of initial CH4 content on reactor performance. The three biogas:air ratios evaluated were 1:6.0 (10% CH4, days 21.0-24.0, day 27.0-27.5), 1:2.5 (20% CH4, days 24.0-27.0), and 1:28.0 (2% CH4, days

27.5-28.0). For biogas:air ratios of 1:6.0 and 1:2.5, biogas was fed every 8-12 h for three consecutive days. The use of biogas:air ratio of 1:6.0 on day 27.0-27.5 was to ensure that the same headspace CH4 content (10%) was used prior to each change in biogas:air ratio. At the biogas:air ratio of 1:28.0, biogas was fed once every 4 h for 12 h. Headspace gas composition was measured at 0, 2, and 4 h for each biogas:air ratio. At higher biogas:air ratios (1:2.5,

1:6.0), headspace gas composition was also measured at 8-12 h and 24 h. The average and standard deviation of CH4 removal, O2 removal, and CO2 production at 4 h were used to compare the rate of gas removal at different biogas:air ratios. Based on results obtained in

Phase 1.1 (Section 5.3.1.1), the liquid volume and NMS medium dilution rate in Phase 1.2 were set at 70 mL and 1.0 d-1, respectively. Liquid and gas circulation rates were set at 50 mL/min and 80 mL/min, respectively.

After Phase 1.2, preliminary experiments were conducted to determine which levels of phosphate buffer (MDH inhibitor), formate (electron donor), NMS medium dilution rate

(d-1), and biogas:air ratio were needed to promote methanol production in the TBR.

Additionally, a sample of TBR fluid (OD≈0.2) during these preliminary tests was removed to determine whether free cells could be used for biogas to methanol conversion. The liquid sample was centrifuged (10,000 rpm for 10 min) and resuspended in 8 mL phosphate buffer

(10 times concentration listed in Section 5.2.3.1) with 80 mM formate to reach an initial OD 110

of ~0.5. The 8 mL sample was split into two 4 mL samples, which were transferred to 40 mL test tubes. Rubber stoppers were added and biogas was added directly at a biogas to air ratio of 1:2. The two 40 mL test tubes were incubated at 37°C. After four hours, a 1 mL liquid sample from each 40 mL tube was filtered (0.2 μm) and the filtrate was subjected to methanol analysis via GC.

After Phase 1, the free space of the reactor was not measured in order to best preserve the loosely attached biomass on the packed bed. The ceramic balls with attached biomass were removed and characterized by visual observation. The packed bed biomass density (g/g balls) was determined by re-suspending the attached biomass of a representative sample of the ceramic balls from the packed bed in a known volume of DI water (250 mL) and the suspended biomass/water sample was subjected to dry weight analysis. The total biomass (g) in the representative sample was calculated by multiplying the mass of DI water added to the sample to the average suspended biomass/water dry weight (g/g). Then, the packed bed biomass density (g biomass/kg balls) was calculated by dividing the total biomass from the sample by the mass of balls in the sample. The total mass of ceramic balls added to the reactor (2.42 kg) was calculated by multiplying the specific gravity of the ceramic balls (2.3 g/cm3) by the empty volume of the packed bed portion of the reactor (1.05 L). The total dry attached biomass in the TBR after Phase 1 was calculated by multiplying the packed bed biomass density (g dry biomass/kg balls) by the mass of ceramic balls added to the reactor

(2.42 kg). After the ceramic balls were removed, the reactor was cleaned several times with

70% ethanol followed by DI water. Fresh ceramic balls were added to the TBR and the packed bed was pre-wetted with DI water before the second inoculation (Section 5.2.4, Phase

2.1).

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5.2.4. Phase 2: Impacts of biogas on reactor startup and methanol production

5.2.4.1. Phase 2.1: Non-sterile start-up using biogas as substrate

To determine if the TBR could be initiated under nonsterile conditions using biogas as the CH4 source, it was seeded with liquid broth that was collected from the TBR on day 16 of Phase 1.1 (sample stored at 4ºC prior to use, 35 mL liquid broth diluted in 35 mL fresh

NMS medium prior to inoculation). Biogas was fed at a biogas:air ratio of 1:6.0 every 10-14 h for six days. During this six-day period, the TBR liquid medium volume and NMS medium dilution rate were set at 70 mL and 0.29 d-1, respectively. This NMS medium dilution rate was selected to maintain high OD for methanol production experiments (Phase 2.2). The gas composition of headspace and OD of liquid medium were measured every 3-12 h. Liquid and gas circulation rates were set at 50 mL/min and 80 mL/min, respectively.

5.2.4.2. Phase 2.2: Methanol production from biogas

A 2 x 2 factorial design using two factors and two levels was used to determine the impacts of formate addition (6 mmol, 12 mmol) and biogas:air ratio (1:2.5, 1:6.0) on methanol production in the TBR. The procedure was started by pumping air through the TBR for 20 min to remove residual biogas and provide O2. Then, the volume of free liquid was measured with a syringe and NMS medium was supplied to reach approximately 70 mL.

Subsequently, 12 or 18 mL of liquid was removed from the TBR (depending on the necessary amount to maintain the total volume), and 6 mL of 0.6 M phosphate buffer (3.6 mmol), and 6 mL or 12 mL of 1 M sodium formate (6 or 12 mmol) were added. Then, biogas was fed at the desired biogas:air ratio (1:2.5 or 1:6.0), and the TBR was operated for 10 h with liquid and

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gas circulation rates set at 50 mL/min and 80 mL/min, respectively. CH4, O2, and CO2 concentrations in the headspace, and methanol and formate concentrations in the liquid, were measured at 0, 2, 4, 6, and 10 hours. After each 10-h methanol production phase, the reactor was washed three times with DI water at high liquid circulation rates (>100 mL/min) to remove residual methanol and formate (validated by GC and high-performance liquid chromatography (HPLC), respectively). Prior to starting another methanol production test, the 12 or 18 mL of TBR liquid medium that was removed at the beginning of a test was put back into the TBR, NMS was added to reach a volume of 70 mL, and then the reactor was operated until the liquid OD again reached ~0.2. This method of dilution was used because it mimicked the dilution strategy used in Phase 2.1 to accumulate free cells needed for methanol production. This method could potentially be implemented at large scale due to low cost. Additionally, the initial OD content was relatively stable, indicating this was a proper method to control initial biomass density in the TBR fluid. For each level of formate addition and biogas:air ratio, at least two replicates were performed.

5.2.5. Control experiments and mass transfer analysis

A control experiment was set up to compare biogas oxidation using shake flasks with similar headspace:liquid volume (VH:VL) ratios to the TBR (VH:VL=16.7:1.0). First, a small sample of the TBR liquid medium from Phase 1.2 was diluted in NMS medium in a 550 mL shake flask, biogas was added at a 1:6.0 biogas:air ratio, and the flask was agitated at 200 rpm until CH4 removal was verified. A small amount of this culture was then diluted in 70 mL of fresh NMS medium to reach an initial OD of 0.05, then placed into 1.16 L shake flasks equipped with a rubber stopper (VH:VL =15.6:1.0). Biogas was then added to the 1.16 L

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shake flasks at biogas:air ratios of either 1:2.5 or 1:6.0. The flasks were incubated in the same incubator as the TBR, and were shaken at 200 rpm for 24 h. Headspace CH4, O2, and CO2 contents were measured at 0, 10, and 24 h to determine gas removal and production rates.

This control experiment was performed in duplicate.

To determine the impact of the packed bed on reactor performance, the TBR was operated under normal conditions, except the packed bed was removed (VH=1950 mL). After removal of the packed bed, the TBR was supplied with an actively growing methanotrophic culture in NMS medium (OD=0.25, VL=70 mL) that used TBR liquid medium from Phase

1.2 as inoculum (stored at 4ºC, incubated at 37ºC in shake flasks prior to use). At the beginning of each experiment, the TBR liquid medium was diluted in NMS medium to reach an initial OD of approximately 0.2, and biogas was fed to the TBR at a 1:2.5 biogas:air ratio.

Gas composition was measured after 8-14 h. The average and standard deviation of TBR performance parameters (gas removal, change in OD) over two days of operation were compared to results from Phase 1.2. The liquid and gas flow rates of the TBR were set at 50 mL/min and 80 mL/min, respectively.

The abiotic volumetric mass transfer coefficient for O2 (KLA/VL) in the TBR was determined based on the methodology outlined in Orgill et al. (2013). The schematic of the

TBR was the same as shown in Figure 5.1, except a custom 220 mL flow-through cell with dissolved oxygen (DO) probe (ProODO, YSI Inc, Yellow Springs, OH, USA) was included in-line with the liquid circulation tubing. DI water was used as the liquid medium in the TBR, and the reactor was operated under the same incubation condition used in Phases 1-2. When necessary, fresh ceramic balls were added to the TBR. Two scenarios were compared: (1) operation with packed bed (VL=340 mL, VH=1120 mL), and (2) operation without packed

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bed but with the same liquid volume as Scenario 1 (VL=340 mL, VH=1900 mL). First, the

TBR was purged with N2 at 80 mL/min until the DO in the liquid (circulating at 50 mL/min) was near 0 mg/L. Then, ambient air was introduced to the TBR (80 mL/min) and DO levels in the DI water were recorded every 30 s using the DO probe. The volumetric mass transfer coefficient for O2 (KLA/VL) was calculated according to Eq. 5.1 (Orgill et al., 2013):

ln(C* -C ) KLA = L L (Eq. 5.1) VL t

2 where KL is the mass transfer coefficient (m/h), A is the mass transfer area (m ), VL is the

3 * total volume of liquid added to the TBR (m ), CL is the saturating DO concentration

3 (mol/m ) at the operating temperature, CL is the DO concentration measured in the flow- through cell (mol/m3), and t is time (h) (Orgill et al., 2013). The liquid circulation rate was again set to 50 mL/min for abiotic mass transfer tests. Each abiotic mass transfer test was performed in triplicate.

5.2.6. Analytical methods

The optical density (OD) of the TBR liquid medium was measured at 600 nm with an

Eon microplate spectrophotometer (Biotek, Winooski, VT, USA) according to methods in

Sheets et al. (2016). The methanol content of filtered (0.2 μm) samples of TBR liquid medium were analyzed by a GC with a flame ionization detector (FID) (Shimadzu, 2010Plus,

Columbia, MD, USA) according to methods in Sheets et al. (2016). Formate in filtered (0.2

μm) samples of TBR liquid medium was analyzed using a LC-20 AB HPLC system

(Shimadzu, Columbia, MD, USA) with a RID-10A refractive index detector (RID) and a

RFQ-Fast Fruit H+ (8%) column (Phenomenex, Torrance, CA, USA). The mobile phase was

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2.5 mM H2SO4 operated at a flow rate of 0.4 mL/min. The column and RID temperatures were maintained at 60°C and 55°C, respectively (Hu et al., 2012). The concentration of formate (M) was determined using a standard curve developed using several different standard solutions of sodium formate. The composition of headspace gas (CH4, O2, CO2, N2) was analyzed by a GC equipped with a thermal conductivity detector (TCD) according to methods described in Sheets et al. (2015a). Throughout the experiments, only minor reductions in headspace pressure were observed; therefore, CH4 removal (mmol/h) was calculated according to Eq. 5.2:

[(CH -CH )*V ] CH removal = 4, 0 4, t H * 1 (Eq. 5.2) 4 t 25.4

where CH4,0 is the initial headspace concentration (%), CH4,t is the headspace concentration at time t (%), VH is the headspace volume (mL), t is time (h), and 25.4 is the molar volume of gases at 37ºC (mL/mmol). O2 removal was calculated the same way, except that headspace

O2 concentrations were used in place of CH4 concentrations. The TBR produced CO2, so

CO2,0 and CO2,t had to be swapped in Eq. 5.2 to calculate CO2 production (mmol/h).

Volumetric CH4 removal (mmol/L/h) was calculated as the CH4 removal (mmol/h) divided by the total volume of liquid in the TBR system (VL). Methanol productivity (g/L/d) was determined by dividing the methanol content (g/L) by the cultivation time. CH4-to-methanol conversion (%) and formate-to-methanol conversion (%) were determined by dividing the amount of methanol produced (mmol) by the amount of CH4 or formate consumed (mmol).

Statistical significance was determined by analysis of variance (ANOVA, α = 0.05) using

JMP Statistical Software from SAS Institute Inc. (Version 10.0.2, Cary, NC, USA).

Experimental data are presented as average values ± standard deviations.

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5.2.7. Microbial community analysis

The microbial community of the TBR at different phases was analyzed to determine how operational conditions may have impacted the bacteria involved in CH4 oxidation.

Samples of circulation fluid (≥1 mL per sample) were taken at the end of Phase 1.1 (day 20 of P1), the end of Phase 1.2 (day 28 of P1), and the end of Phase 2.2 (day 20 of P2).

Additionally, one sample of the inoculum for Phase 2 (day 16 of Phase 1.1) and one sample from the end of Phase 2.1 (day 5) were taken to assess any changes in community composition within Phase 2. Each sample was centrifuged at 16,000 x g for 1 min to pellet the microbial biomass. Metagenomic DNA was extracted from each sample pellet using the repeated bead beating plus column purification (RBB + C) method as described in Yu and

Morrison (2004) . The quality of the DNA extracts was checked using agarose gel (0.8%) electrophoresis, and the DNA concentrations were quantified using a Quant-iT™ dsDNA

Assay Kit (ThermoFisher Scientific, Waltham, MA, USA). One amplicon library was prepared from each DNA extract using primers 515F and 806R that amplify the V4-V5 hypervariable region of the 16S rRNA gene of both bacteria and archaea. All the amplicons were sequenced using 2 × 300 paired-end kits on the Illumina MiSeq system (Illumina, San

Diego, CA, USA) at the OARDC Molecular and Cellular Imaging Center (Wooster, OH,

USA) (Li et al., 2016). The sequencing data were analyzed using Quantitative Insights Into

Microbial Ecology (QIIME) open-source software (v 1.9.0) (Caporaso et al., 2010) and the protocols described previously (Li et al., 2016). Briefly, bases with a quality score of less than 25 were trimmed off from each sequencing read, and then the two paired reads were joined to a single sequence using the fastq-join script (Aronesty, 2011). The barcodes and primers were further trimmed from each sequence. Sequences shorter than 248 bp after 117

trimming were discarded. Chimera sequences were identified using the ChimeraSlayer algorithm (Haas et al., 2011). Species-equivalent operational taxonomic units (OTUs) were identified by comparing the representative sequence of OTU to the Silva_119_release reference sequences (http://www.arb-silva.de/download/archive/qiime/) at 97% similarity

(pick_open_reference_otus.py) using the uclust algorithm (Edgar, 2010). Minor OTUs were filtered out if they were each represented by less than 0.005% of the total sequences

(Bokulich et al., 2013) or were less than 0.1% in any of the samples. The sequences were deposited in the Genbank SRA database with the accession number SRP090502. A summary of sampling information, sequencing, quality checking/sequence removal, and OTU clustering are shown in Table A.1. Relative abundances of taxa (phylum and order level) from each Phase were compared using one-way ANOVA using JMP statistical software. If the p-value from one-way ANOVA was less than the set significance value (α = 0.05), then the data was subjected to Tukey’s Honestly Significant Difference (HSD) test to rank the samples in the order of OTU relative abundance.

5.3. Results and Discussion

5.3.1. Phase 1: Influence of operational parameters and biogas on CH4 removal

5.3.1.1. Phase 1.1: Determination of optimal operating parameters using purified CH4

One week after the TBR was inoculated with strain 14B, the CH4 content in the headspace typically declined from 13% to 0-3% within only 10-16 h of incubation (Figure

5.2). Additionally, both loosely and strongly attached biomass were observed on the ceramic balls and the liquid medium in the reactor had the same characteristic brown color of a pure

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culture of Methylocaldum sp. 14B (Sheets et al., 2016). This indicated that a predominantly methanotrophic microbial community was forming in the TBR, because no other carbon source than CH4 was provided to the reactor (Estrada et al., 2014b). Very slight differences in

CH4 removal (0.26-0.40 mmol/h) at variable gas and liquid flow rates (Table 5.2, days 6-11) were likely because the apparent gas and liquid velocities tested in this study were too low

(UL=0.3-2.4 m/h, Ug=0.6-2.4 m/h) to influence gas-liquid mass transfer in the TBR (Estrada et al., 2014a, 2014b; Ranade et al., 2011). Although higher flow rates can improve mass transport, they were not attainable in this study because higher gas velocities caused heat damage to the gas circulation tubing and higher liquid velocities contributed to clogging of the packed bed. Still, CH4 removal (0.26-0.44 mmol/h) during Phase 1.1 was 8-13 times higher than that by a 50 mL pure culture of Methylocaldum sp. 14B previously cultivated in

250 mL flasks under rigorous shaking (200 rpm) (0.034 mmol/h) and similar OD (0.05-0.3)

(Sheets et al., 2016).

CH4 removal drastically declined from days 11-13 (0-0.09 mmol/h) while O2 removal

(0.17-0.20 mmol/h) and CO2 production (0.08-0.11 mmol/h) declined slightly (Figure 5.2)

(Table 5.2). It was likely that inhibition was caused by insufficient nutrients in the TBR

(Estrada et al., 2014b; Lebrero et al., 2016). These claims are supported because reactor performance quickly recovered after the entire TBR liquid medium was replaced with fresh

NMS medium (day 12.5) and the NMS medium dilution rate was increased (Table 5.2, days

13-16). The most stable flow, without clogging and/or bubbling, was observed when the liquid circulation rate was set at 50 mL/min, the gas circulation rate was set at 80 mL/min, and the liquid volume was controlled at 70 mL (days 16.5-19.5). Based on visual observation, the liquid in the packed bed formed thin rivulets and more brown biomass appeared in

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regions where the thin rivulets formed. There was also a nominal increase in OD of the TBR liquid medium (0.06-0.3) as headspace CH4 content declined (Figure 5.2) (days 16.5-19.5).

Under these conditions, less than 10 mL of the liquid was retained in the bottom of the TBR, and gas bubbles were not observed in the liquid circulation line. Interestingly, CH4 removal was observed when liquid circulation was turned off (Table 5.2, day 19.6-20.5), indicating that both attached and suspended methanotroph cells contributed to CH4 removal (Cáceres et al., 2016). The NMS medium had sufficient buffering capacity because the pH was stable between 6.8 and 7.5. The stable conditions of liquid volume (70 mL), liquid circulation rate

(50 mL/min), and gas circulation rate (80 mL/min) that provided a “trickle” flow regime were maintained for the remaining experiments (Devarapalli et al., 2016; Honda et al., 2016;

Ranade et al., 2011). Based on the consistent CH4 oxidation rates and visual observation of

“trickle flow” regime, the TBR system was considered stable and the CH4 source was shifted to biogas (Section 5.3.1.2).

5.3.1.2. Phase 1.2: Effect of biogas:air ratio on TBR performance and control experiments

The CH4 removal rates observed in Phase 1.1 were sustained after the CH4 source was switched to biogas (Figure 5.2) (days 22-28, NMS medium dilution rate=1 d-1), suggesting that the CO2 and H2S in the biogas did not have a major impact on the CH4 oxidizing capacity of the methanotrophic TBR. Similar to Phase 1.1, the OD of the liquid medium increased as CH4 content in the headspace declined. Operation at the lowest biogas:air ratio caused the highest OD (0.5-0.6) observed during Phase 1.2 (Figure 5.2) (day

27). The color of the suspended cells shifted from brown in Phase 1.1 to white in Phase 1.2.

There was a significant linear increase (ANOVA, p<0.05) in the rate of O2 removal and CH4

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removal by increasing biogas:air ratios from 1:28.0 to 1:6.0, while similar gas oxidation was observed at biogas:air ratios of 1:6.0 and 1:2.5 (Figure 5.3). Similarly, maximum volumetric

CH4 removal in the TBR at 4 h was observed for the biogas:air ratio of 1:2.5 (8.4±1.1 mmol/L/h), followed by the biogas:air ratio of 1:6.0 (6.0±1.3 mmol/L/h), and the lowest volumetric removal was observed at the biogas:air ratio of 1:28.0 (2.9±0.3 mmol/L/h).

Enhanced CH4 removal at higher biogas:air ratios was because higher CH4 content in the headspace provided a higher equilibrium CH4 concentration in the liquid. Therefore, the rate of CH4 removal was likely controlled by gas-to-liquid mass transfer under the operating conditions evaluated in this study (Cantera et al., 2016). This is also supported by the fact that CH4 removal at the 1:28 biogas:air ratio was fairly low (2% CH4, 2.86 mmol/L/h) compared to a study by Estrada et al. (2014b) that used a continuous methanotrophic biotrickling filter that treated gas at similar initial CH4 content (2% CH4) and had polyurethane foam packing with high specific surface area (1000 m2/m3) and was operated at high liquid velocity (5-15 m/h) (7-8 mmol/L/h CH4 removal, 1.2-1.4L liquid volume, 4L packed bed volume, used bacterial consortia dominated by type I and type II methanotrophs).

This improved performance demonstrated by Estrada et al. (2014b) was probably because gas-to-liquid mass transfer for sparingly soluble substrates (i.e. O2, CH4) is enhanced at higher liquid velocities when packings with high specific surface area are employed (Kim and Deshusses, 2008b). Trade-offs between enhanced mass transfer using different flow rates/tower packings and their associated costs need to be evaluated via techno-economic analysis.

The maximum volumetric CH4 removal (8.4±1.1 mmol/L/h) in the TBR was comparable to a recent study that applied a 2.5 L CSTR (1L working volume, 10% CH4 in

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headspace) in batch mode with significant agitation (1000 rpm) for Methylomicrobium buryatense 5GB1 cultivation (7.6 mmol/L/h) (Gilman et al., 2015). CH4 removal in the TBR at biogas:air ratios of 1:2.5 and 1:6.0 were three to four times higher than the shake flask control tests that were operated at a similar VH:VL ratio (200 rpm, 1.8-2.1 mmol/L/h) (Section

5.2.5). This indicates that TBRs could offer significant energy savings compared to CSTRs

(Devarapalli et al., 2016). Additionally, the abiotic KLA/VL for O2 increased approximately two-fold when the packed bed was included in the reactor (Table 5.3). Furthermore, the CH4 removal, O2 removal, and CO2 production for the biotic control test (no packed bed, inoculated with similar methanotrophic culture at similar initial OD) were approximately two times lower than the results obtained in Phase 1.2 (Table 5.3). The higher abiotic mass transfer coefficient and CH4 removal rates in Scenario 1 of the control tests showed that the presence of the TBR packing material improved gas-liquid mass transport and gas oxidation

(Table 5.3)(Estrada et al., 2014b; Orgill et al., 2013). For comparison, the abiotic KLA obtained in Scenario 1 (8.7-10.4 h-1) was only slightly lower than that (14-16 h-1) reported by

Orgill et al. (2013), who used similar packing materials, design dimensions, and gas (73 mL/min) and liquid circulation (50 mL/min) rates. Future studies should evaluate different packing materials to optimize reactor performance in methanotrophic TBRs (Devarapalli et al., 2016; Kim and Deshusses, 2008b; Yoon et al., 2009).

5.3.1.3. Preliminary methanol production experiments

The preliminary methanol production experiments (Section 5.2.3.2) showed that no methanol was produced after the reactor was washed with DI water and only NMS with phosphate (3.6-10.8 mmol) and formate (6 mmol) was used as the TBR liquid medium.

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However, when bacteria from the TBR were allowed to propagate until the liquid had an OD higher than 0.1, the addition of formate and phosphate caused detectable methanol concentrations in the TBR liquid medium. Additionally, the methanol production test in 40 mL shake flasks (Section 5.2.3.2) showed that the free cells could produce 0.26 g/L of methanol in approximately 4 h. These results indicated that the presence of free cells were needed to induce methanol formation in the TBR. Overall, biogas:air ratios greater than or equal to 1:6.0, formate additions greater than or equal to 6 mmol, and NMS medium dilution rates ≤ 0.3 d-1 were needed to attain detectable methanol concentrations in the TBR liquid medium. The need for higher biogas:air ratios was because it provided higher concentrations

-1 of CH4 in the liquid, while the low NMS dilutions (≤ 0.3 d ) were needed to maintain a higher OD in the TBR liquid medium. Phosphate buffer (Na2HPO4/KH2PO4) additions higher than 3.6 mmol did not improve methanol production.

Most of the biomass on the packed bed after Phase 1 was brown to off white and was very loosely attached. The total biomass density was estimated at 0.96 g dry biomass/kg balls, indicating that roughly 2.3 g dry biomass was attached to the packing material at the end of Phase 1. Assuming an average daily increase in OD of 0.2 throughout Phase 1 and using the correlation developed by Sheets et al. (2016) for dry biomass v. OD (X

(g/L)≈0.7*OD), the total biomass produced in the free liquid phase would have been about

0.3 g dry biomass. These results indicate that the attached cells had a significant role in CH4 oxidation in the TBR. It is possible that attached cells also contributed to methanol production during preliminary tests, but was probably rapidly consumed by other microbes in the biofilm matrix, and the net methanol produced was below the detection limit of the GC

(LOD=0.001 g/L). Overall, the results from Phase 1 were useful to determine the operational

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parameters and effective biogas:air ratios needed for consistent CH4 oxidation and a stable

“trickle” flow regime, and because it provided the non-sterile methanotrophic community sample for inoculation in Phase 2.

5.3.2. Phase 2: Start-up on biogas and methanol production

5.3.2.1. Phase 2.1: Startup under non-sterile conditions

The non-sterile liquid from previous TBR experiments was an effective inoculum to initiate CH4 consumption in a new TBR. OD dynamics showed that there was an initial build- up of free cell biomass (days 0-2), followed by a rapid decline (days 2-3), and eventual stabilization (Figure A.1). Additionally, a brown biofilm was observed on the packing material by day 6. Microbial community analysis indicated that the reactor was dominated by

Methylococcales, an order of bacteria than contains Methylocaldum sp. and other known methanotrophs (Bowman, 2006), even though the non-sterile inoculum had a more variable bacterial community composition with low detected levels of Methylococcales (Table A.2).

Consistent CH4 consumption (0.4 mmol/h) by day 6 indicated that the TBR was stable and the operational regimes were changed to induce methanol production.

5.3.2.2. Phase 2.2: Effects of biogas and formate on methanol production

Despite operating under non-sterile conditions, methanol production was achievable in the TBR (Figure A.1 and Figure 5.4). The highest methanol content (~0.28 g/L) was observed at a high biogas:air ratio (1:2.5) and a high formate addition (12 mmol) (Figure

A.1-days 7 and 9, Figure 5.4a). Under those conditions, there was sufficient CH4 and reducing power (formate) to sustain methanol production for the 10-h incubation period

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(Figure 5.4a). At the same biogas:air ratio (1:2.5) and a lower formate level (6 mmol), methanol production peaked at 4 h (0.14 g/L), but declined rapidly thereafter (Figure 5.4b).

This is because the formate was almost completely consumed after 6 h, which limited the amount of reducing equivalents available for biogas to methanol conversion (Figure 5.4b)

(Ge et al., 2014; Mehta et al., 1991). To maintain viability, the microbes in the TBR had to use methanol as a source of carbon and energy (Sheets et al., 2016). Similar methanol production kinetics were observed at a low biogas:air ratio (1:6.0) and a high formate (12 mmol) addition, except that methanol production peaked at 6 h (0.17 g/L) (Figure 5.4c). In this case, there was sufficient formate in the TBR liquid medium, but CH4 in the headspace declined to 3% (Figure 5.4c). Insufficient CH4 in the TBR could have caused the microbial community to use formate and methanol as its primary carbon sources, which explains the observed reduction in formate and methanol after 6 h (Figure 5.4c). The combined effects of insufficient formate and insufficient CH4 were the reason for the lowest methanol content

(0.05-0.07 g/L) observed at a low biogas:air ratio (1:6.0) and a low formate (6 mmol) addition (Figure A.1-days 15 and 17, Figure 5.4d). Additionally, lower CH4:O2 ratios have been shown to limit production of excreted products and promote conditions for balanced growth (i.e. higher biomass and CO2 production) in methanotrophic enrichment cultures (Wei et al., 2016). This could also explain the higher OD (0.5-0.6) observed at the lowest biogas:air ratio during Phase 1.2 (Figure 5.2, day 27.5-28.0). Based on the aforementioned results, it is not surprising that the higher biogas:air ratio and higher formate addition caused higher CH4 to methanol and formate to methanol conversion ratios (Table 5.4).

Under optimal conditions (biogas:air=1:2.5, formate addition=12 mmol), both methanol productivity (0.9 g/L/d) and CH4 to methanol conversion (22.4%) in the non-sterile

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TBR were in the upper range of reported values for pure methanotroph cultures (Ge et al.,

2014). Additionally, the results from Phase 2.2 suggest that methanol production is feasible using non-sterile methanotrophic consortia and raw biogas. This could drastically reduce reactor sterilization and biogas cleaning requirements, thus decreasing operational costs and energy demands. However, Phase 2.2 revealed some issues that need to be addressed prior to scale up. Unstable methanol production after day 20 was presumably because formate and methanol-consuming microbes became prevalent in the TBR. After the system recovered

(day 28), formate consumption was high. Furthermore, maximum formate to methanol conversion in the study (12-13%) was low (Table 5.4). In an ideal scenario in which MDH is completely inhibited and all electrons produced from formate dehydrogenase went to CH4 oxidation, the theoretical conversion of formate (and CH4) to methanol would be 100%. This shows that low formate yields significantly impact the economic feasibility of CH4 to methanol conversion; thus, formate costs must be reduced or alternative electron donors are needed (Ge et al., 2014). In the short term, formate costs could be substantially reduced to

~$200/MT if lower cost electricity produced during off-peak hours was used to electrochemically reduce concentrated CO2 from industrial sources (Yishai et al., 2016). Future approaches to reduce costs also include catalytic oxidation of lignocellulosic biomass to formate or photo-reduction of CO2 (Yishai et al., 2016). Alternative electron donors for bioconversion of CH4 to methanol include renewable hydrogen (H2) (S. K. S. Patel et al.,

2016a), acetate from wastewater, or direct use of renewable electricity using genetically modified “electrophilic” methanotrophs (Ge et al., 2014). Although the free space volume was not measured at the conclusion of Phase 1 or Phase 2, the portion taken up by microbial biomass did not likely influence results, because the initial gas composition, which was

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controlled using an assumed free space volume of the TBR, was consistent throughout the study.

5.3.3. Microbial community

More than 33,000 quality-checked sequences were obtained from the samples (Table

A.1), and 92-98% of the obtained DNA sequences were assigned to known bacterial phyla, with the most dominant being Proteobacteria (62-86%) and Bacteroidetes (0.2-8.7%) (Figure

5.5) (Tables A.2 and A.3). Other major phyla (each represented by ≥ 2% of total sequence in at least one sample) included Cyanobacteria (0.4-8.9%), Chlorobi (0-6.4%), and

Gemmatimonadetes (0-3.7%), while Chloroflexi, Firmicutes, and Actinobacteria were detected as minor phyla. There were significant differences in the number of OTUs that had order-level taxonomic assignment between each operating phase (Figure 5.5) (Table A.3).

One of the most predominant orders from Phases 1.1 and 2.2 was Methylococcales, which contains methylotrophic bacteria that can only consume one-carbon compounds (Figure

5.5)(Bowman, 2006; Chistoserdova and Lidstrom, 2013). Methylococcales had the greatest relative abundance in Phase 2.2, followed by Phase 1.1, which was higher than Phase 1.2 samples (Table A.3). Additionally, Methylocaldum, the genus of the inoculum within

Methylococcales, was the most predominant methanotrophic genus in all phases (Table 5.5).

Apparently, the operating conditions used in Phase 2.2 allowed Methylocaldum spp. to dominate the reactor and produce methanol, despite the fact that the non-sterile inoculum had low relative abundance of Methylococcales (Table A.2). This was most likely due to the low dilution rates and supplementation of formate and phosphate used during Phase 2.2 compared to Phase 1 (Table 5.1). Additionally, the inoculum for Phase 2.2 was sampled

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directly after the CH4 oxidation rate in the TBR was recovered. This indicates that some non- methanotrophic bacteria could have still been active in the inoculum sample, but the conditions in Phase 2 supported more rapid growth of Methylococcales. Although only a small amount (26.7%) of the sequences from the Phase 1.1 and 1.2 samples were classified to known genera (Table 5.5), the orders Burkholderiales, Rickettsiales, Rhodospirales, and

Rhizobiales were very prevalent in those samples (Figure 5.5). There were high amounts of

Rhizobiales, which contains the metabolically versatile type II methanotrophic genera

Methylocystis and Methylosinus (Bowman, 2006; Chistoserdova and Lidstrom, 2013). In fact, there were significantly (p<0.05, Tukey HSD) more sequences identified as Rhizobiales in

Phase 1.2 than in the other phases (Figure 5.5) (Table A.3). Therefore, it is possible that the high dilution rates and variable biogas:air ratios applied in Phase 1.2 caused type II methylotrophs to outcompete the type I methanotrophs present in the original inoculum. In fact, type II methylotrophs are known to survive in variable growth conditions by producing polyhydroxybutyrate (PHB) as a carbon storage compound (Pieja et al., 2011). This indicates that the high dilution rates used in Phase 1.2 could be applicable for PHB producing strains in methanotrophic TBRs. Still, no type II methanotrophic genera were detected. Phase 1 samples also had greater relative abundance of methanol-utilizing bacteria (i.e.

Burkholderiales, Rhodospirillales) than other phases (Figure 5.5) (Table A.3) (Chistoserdova and Lidstrom, 2013). Synergism between methanol-oxidizing bacteria and methanotrophs is a common observation in methanotrophic enrichments (Wei et al., 2016).

The other major bacterial genera identified from Phase 1 (Sediminibacterium,

Phaeospirillum, Limnohabitans, Agrobacterium, Ralstonia) probably survived on metabolic byproducts (i.e. acetate/methanol) produced by methanotrophs, because no other carbon

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source than CH4/biogas was supplied during that phase (Ho et al., 2014; Wei et al., 2016).

Previously, Bothe et al. [50] identified Ralstonia as a common genus that grows in association with methanotrophs, likely because it consumes acetate produced by the methanotrophs. Meanwhile, Sediminibacterium and Limnohabitans have been identified in other methanotrophic samples and in a coal-packed CH4-biofilter (Creveceour et al., 2015;

Limbri et al., 2014). Furthermore, Rhizobiales (the order that contains Agrobacterium) has been shown to stimulate methanotrophic activity by supplying vitamins that promote the growth of methanotrophs (Iguchi et al., 2011). The supply of formate could have contributed to growth of the Flavobacterium or other formate-consuming microbes in Phase 2.2 (Table

5.5) (Wei et al., 2016). The microbial community samples were based on free cells in the

TBR recirculation fluid. Although the total microbial population was probably similar, the relative ratios of microbes in free and immobilized biomass were likely different because of variable CH4 and O2 gradients and oxygen stresses in the biofilm (i.e. micro-aerobic, anaerobic conditions) (Devinny and Ramesh, 2005).

At the current state of the technology, biological conversion of biogas to methanol is unlikely to be economically feasible due to the low reported yields (≤1 g/L). However, this study shows that TBRs have good mass transfer properties, and can potentially be used to cultivate methanotrophs for cell-based products such as biopolymers and single cell protein, or for other excreted compounds such as lactic acid (Strong et al., 2016). Continued research on TBRs for CH4 conversion could lead to unique designs that have substantial cost advantages compared to conventional equipment such as CSTRs. Specifically, future work should evaluate the effects of operational conditions (flow rates, operational time) on the development and stability of methanotrophic biofilms. Future work could also include

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alteration and engineering of the microbial community supplied to methanotrophic TBRs in order to maintain stability and improve product synthesis (Jagmann and Philipp, 2014).

5.4. Conclusions

A methanotrophic trickle-bed reactor improved mass transport of O2 and enhanced

CH4 oxidation to methanol. The highest CH4 to methanol conversion rates were observed at high biogas:air ratios and high formate additions. There were considerable differences in the bacterial community in samples from each operating phase, and the genus in which the inoculum is classified (Methylocaldum sp. 14B) was observed throughout the study.

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Table 5.1: TBR operating phases

Phase 1.1 Phase 1.2 Phase 2.1 Phase 2.2 Daya 0-21 22-28 0-6 7-28

Gas flow Start-up Biogas:air on biogas Parameters Liquid flow Biogas:air ratio and evaluated ratio Liquid volume nonsterile Formate addition NMS dilution inoculum

TBR TBR TBR liquid liquid liquid Inoculum Methylocaldum from from from sp. 14B end of day 16 of end of Phase 1.1 Phase 1.1 Phase 2.1

CH4 Source Purified CH4 Biogas Biogas Biogas

1:2.5 Biogas:air 1:2.5 1:5.5-1:7.7b 1:6.0 1:6.0 ratio 1:6.0 1:28.0

Gas circulation 20-80 80 80 80 (mL/min)

Nutrient NMSc NMSc NMSc NMSc source

Nutrient dilution 0.17-1.0 1.0 0.3 0.29 (1/d)

Liquid volume 0-200 70 70 70 (mL)

Liquid circulation 0-80 50 50 50 (mL/min) a. day within each phase b. purified CH4:air ratio c. NMS=nitrate mineral salts medium

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Table 5.2: TBR performance during Phase 1.1

Day of Gas Liquid Liquid NMS CH4 O2 CO2 operation circulationa circulation volume dilution removal removal production (mL/min) (mL/min) (mL) rate (mmol/h) (mmol/h) (mmol/h) (1/d) 0.0-6.0 80 50,80 200 0 0.11-0.37 0.20-0.47 0.10-0.23 6.0-7.0 80 50 200 0.17 0.26-0.32 0.45-0.46 0.16-0.20 7.0-8.5 20 50 200 0.17 0.32-0.44 0.42-0.48 0.20-0.22 8.6-10.0 20 10 200 0.17 0.35-0.41 0.30-0.43 0.13-0.18 10.0-11.0 80 10 200 0.17 0.30-0.32 0.33-0.35 0.14-0.16 11.0-13.5b 40-80 10-30 200 0.25 0.00-0.09 0.17-0.23 0.08-0.12 13.6-16.0 80 50 200 0.25-0.5 0.09-0.36 0.18-0.43 0.08-0.21 16.0-19.5 80 50 70 0.5-1.0 0.29-0.55 0.37-0.58 0.16-0.27 19.6-20.5 80 0c 0c 0c 0.32-0.48 0.43-0.47 0.21-0.24 a. purified CH4 (>99%) used as CH4 source b. period of reactor failure c. circulated 70 mL of fresh NMS medium to wet packed bed prior to incubation

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Table 5.3: Effects of the packing material on mass transfer and performance in the TBR

Abiotic mass Biotic CH4 oxidation transfera Packed CH O CO K A/V 4 2 2 Scenario bed L L removal removal production (h-1) (Y/N) (mmol/h) (mmol/h) (mmol/h) 1 Y 9.54±0.86 0.54±0.03b 0.63±0.01b 0.33±0.01b

2 N 4.62±0.33 0.32±0.01 0.30±0.08 0.14±0.07 a. based on O2 gas-liquid mass transfer b. 8 h gas removal and production data from days 24 to 26 of Phase 1.2 (biogas:air ratio=1:2.5)

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Table 5.4: Methanol production efficiencies at different biogas:air ratios and formate

additions

Formate Biogas:air Initial Formate to CH4 to added ratio CH4 content methanol methanol (mmol) (%) conversiona conversiona (%) (%) 6 1:6.0 10.5±0.3 0.23±0.33 0.27±0.38 6 1:2.5 21.0±2.2 5.15±2.02 6.12±1.17 12 1:6.0 9.9±0.8 11.26±1.61 11.56±0.21 12 1:2.5 20.9±0.4 13.66±1.65 22.41±2.83 a. conversion at 6 h incubation time

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Table 5.5: Relative abundance of genera in the TBR at different operational phases

Genus Phase 1.1 Phase 1.2 Phase 2.2 Methylocaldum 16.97±0.41 0.94±0.06 67.66±4.89 Agrobacterium 4.29±0.15 7.84±2.00 0.35±0.11 Limnohabitans 0.24±0.04 11.56±1.57 0.04±0.02 Phaeospirillum 7.68±0.03 0.13±0.07 1.81±0.13 Sediminibacterium 8.22±0.60 0.24±0.09 1.70±0.23 Phenylobacterium 0.83±0.03 0.39±0.09 0.69±0.17 Ralstonia 1.62±0.19 2.97±0.03 0.01±0.01 Hyphomicrobium 0.77±0.08 1.15±0.13 0.25±0.08 Flavobacterium 0.00±0.00 0.00±0.00 1.02±0.15 Sphingomonas 0.83±0.06 0.32±0.18 0.07±0.02 Stenotrophomonas 0.55±0.01 0.25±0.09 0.00±0.00 Blastomonas 0.70±0.08 0.05±0.03 0.00±0.00 Others 1.44±0.02 0.92±0.01 0.81±0.10 Percentage of total seqsa 44.13 26.76 74.44 a. only shows genera with ≥0.5% relative abundance in at least one sample

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4 3 5

7 8 6 9

Figure 5.1: TBR set up for biogas conversion to methanol: solid lines show direction of liquid

flow and dashed lines show direction of gas flow. 1) TBR; 2) Gas feeding and sampling flask; 3) Gas bag; 4) Gas sampling and feeding port; 5) Syringe for vacuum creation; 6) Gas

circulation pump; 7) Three-way valve for gas circulation shut off; 8) Three-way valve for

liquid sampling and medium replacement; 9) Liquid circulation pump.

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25% 1.0 Methane-initial Methane-final 20% OD 0.8

15% 0.6 OD 10% 0.4

5% 0.2 Gas (%) Gas composition

0% 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Time (d)

Figure 5.2: Dynamics of CH4 removal in the TBR during Phase 1.

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1.00 Methane removal Oxygen removal 0.80 Carbon dioxide production

0.60

at 4 hours 4 at 0.40

0.20

Gas removal or production (mmol/h) (mmol/h) production or Gas removal 0.00 1:28 1:6.0 1:2.5 Biogas:Air ratio

Figure 5.3: Effects of biogas: air ratio on TBR performance during Phase 1.2.

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30% 0.4 Methane Oxygen (a) 25% Carbon dioxide Methanol 0.3 20% Formate

15% 0.2

10% 0.1

5% Headspace gas composition (%) composition gas Headspace 0% 0.0 formate (M) (g/l) and Methanol 0 2 4 6 8 10 Time (h)

30% 0.4 Methane Oxygen (b) 25% Carbon dioxide Methanol 0.3 20% Formate

15% 0.2

10% 0.1

5% Methanol (g/l) and formate formate (M) (g/l) and Methanol Headspace gas composition composition gas (%)Headspace 0% 0.0 0 2 4 6 8 10 Time (h)

Continued

Figure 5.4: Effects of biogas:air ratio and formate addition on methanol production in the

TBR: (a): biogas:air=1:2.5, formate=12 mmol; (b): biogas:air=1:2.5, formate=6 mmol; (c):

biogas:air=1:6.0, formate=12 mmol; (d): biogas:air=1:6.0, formate=6 mmol.

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Figure 5.4: Continued

30% 0.4 Methane Oxygen (c) 25% Carbon dioxide Methanol 0.3 20% Formate

15% 0.2

10% 0.1

5% Methanol (g/l) and formate formate (M) (g/l) and Methanol Headspace gas composition (%) composition gas Headspace 0% 0.0 0 2 4 6 8 10 Time (h)

30% 0.4 Methane (d) 25% Oxygen Carbon dioxide Methanol 0.3 20% Formate

15% 0.2

10% 0.1

5% Methanol (g/l) and formate formate (M) (g/l) and Methanol Headspace gas composition composition gas (%)Headspace 0% 0.0 0 2 4 6 8 10 Time (h)

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100 Unassigned Other Bacterial Phyla Actinobacteria Firmicutes 80 Chloroflexi Gemmatimonadetes Chlorobi 60 Cyanobacteria Other Bacteroidetes Flavobacteriales Bacteroidetes Saprospirales 40 Other Proteobacteria Sphingomonadales Caulobacterales

Relative abundance (%) abundance Relative Rhodospirillales 20 Rickettsiales Proteobacteria Rhizobiales Burkholderiales Methylococcales 0 Phase Phase Phase Phase Phase Phase 1.1-1 1.1-2 1.2-1 1.2-2 2.2-1 2.2-2 Sample name

Figure 5.5: Major bacterial phyla (bolded, each representing >0.5% of total sequences in ≥1

sample) and major orders of Bacteroidetes and Proteobacteria (each representing >5% of

total sequence of each phylum in ≥1 sample)

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Chapter 6: Exploratory Modeling of Biological Conversion of Biogas to Methanol in a Trickle Bed Bioreactor

Johnathon P. Sheetsa,b, Gonul Kaletunçb, Jay Martinb

a. Department of Food, Agricultural and Biological Engineering, The Ohio State

University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH, 44691-4096, USA b. Department of Food, Agricultural and Biological Engineering, The Ohio State University,

Columbus, OH, 43210, USA

Biological conversion of biogas to methanol in a trickle-bed reactor (TBR) was assessed using a steady-state model that considered gas-to-liquid mass transport and gas conversion kinetics by methanotrophs (methane-oxidizing bacteria). Modeling several “TBRs in series” produced data that were comparable to experimental results from a laboratory scale

TBR. The model was adapted to analyze the effects of operational parameters on a theoretical large scale TBR. For a given methane-to-oxygen ratio (1:3.87 to 1:0.52), the highest methanol concentrations (6-18 g/L liq.) were predicted at the highest cell density (40 kg/m3), gas velocity (500 m/h), and reactor pressure (3 atm). Sensitivity analysis showed that methanol production could be improved using packing materials with high specific surface area and methanotroph strains that have high methane oxidation rates and high methanol tolerance. 142

6.1. Introduction

Massive quantities of organic wastes are landfilled or sent to anaerobic digestion

(AD) facilities each year (>50 million tons/year in U.S.) (USEPA, 2016b). At these waste treatment sites, anaerobic microorganisms convert organic material into biogas that is primarily composed of methane (CH4) and carbon dioxide (CO2) along with other trace impurities (i.e. hydrogen sulfide). This energy rich CH4-source could be an abundant feedstock for the production of renewable electricity and/or transportation fuels such as compressed natural gas (Abbasi et al., 2012; Strong et al., 2016). However the low prices of electricity and natural gas have limited the economic incentive to use biogas as a fuel (Strong et al., 2016). In fact, only 60% of the landfills in the U.S. that can install gas recovery and utilization systems have done so (USEPA, 2016c). Meanwhile, less than one-fifth of the organic waste production sites that could support AD have implemented the technology

(American Biogas Council, 2017; USDA et al., 2014). Clearly, new technologies that can improve biogas value are needed.

An emerging opportunity to improve the value of bio-CH4 is to use methanotroph- based biotechnologies for biogas upgrading. Methanotrophs are aerobic CH4-oxidizing bacteria that can convert CH4 to a variety of products such as single-celled protein, polyhydroxybutyrate (PHB), methanol, and others (Ge et al., 2014; Strong et al., 2016).

Methanol is attractive because it has large market demand, it is easily transportable, and it can be converted to other products such as polymers and gasoline (Bertau et al., 2014; Ge et al., 2014; Zakaria and Kamarudin, 2016). Additionally, well-known downstream processing technologies (i.e. distillation) can be used to recover methanol from methanotroph growth medium. Some methanotrophs can also use raw biogas for methanol production, whereas 143

traditional thermochemical technologies for methanol production require a CH4 source free of impurities (Riaz et al., 2013; Sheets et al., 2016). These attributes indicate that biological conversion of biogas to methanol has potential to valorize waste-derived CH4 from landfills and AD.

A major issue with the biological biogas-to-methanol process is that methanol dehydrogenase inhibitors and external electron donors such as formate are needed to support methanol production (Ge et al., 2014). Additionally, methanol can be toxic to most methanotrophs at relatively low concentrations (Kim et al., 2010). These issues can be addressed by developing technologies to produce formate from CO2, H2O, and renewable electricity (Yishai et al., 2016) and by using adaptive evolution to increase the tolerance of methanotrophs to higher methanol concentrations (Best and Higgins, 1981). However, the process could still be limited by the low solubility and mass transfer of CH4 and O2 into liquid growth medium. This issue can be resolved by designing bioreactors that improve gas- liquid mass transport, such as the trickle bed reactor (TBR).

The TBR is an attractive design for biogas conversion because it can improve mass transfer properties at low cost. In counter-current operating mode, liquid medium is supplied at the top of the TBR, where it flows downward across a packed bed, forming a thin film that has low resistance to mass transfer. The gases flow from the bottom to the top, where they are rapidly transferred to the liquid phase. Then, suspended or attached cells convert the dissolved gases to other products (Devarapalli et al., 2016; Iliuta and Larachi, 2006; Sheets et al., 2017). This low impact method to improve gas-liquid mass transport is more favorable compared to energy intensive sparging and impeller based techniques used for continuous stirred tank reactors (Devarapalli et al., 2016; Orgill et al., 2013; Strong et al., 2016).

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However, a thorough understanding of biological TBRs for gas conversion is challenging due to complex gradients in both gas composition and biological kinetics along the reactor height

(Chen et al., 2015). Furthermore, it is difficult and costly to design pilot scale TBR prototypes that emulate the process conditions at large scale.

Mathematical models of gas-liquid mass transfer and biological reaction kinetics could be an effective and low cost tool to rapidly determine the impacts of commercially relevant operational parameters (i.e. cell density, gas flow rate) on TBR performance. In fact, exploratory modeling has been a valuable tool to better understand the analogous syngas fermentation process in a bubble-column reactor (Chen et al., 2015). However, there are no studies that have used mathematical modeling to analyze the biological biogas-to-methanol process. Therefore, the purpose of this study was to 1) outline the development of a model for gas-liquid mass transport and biological conversion of biogas in a TBR; 2) compare laboratory-scale model predictions to laboratory-scale TBR data; and 3) illustrate potential applications of the model by evaluating the impacts of operational parameters on theoretical large-scale TBR performance.

6.2. Model Development

6.2.1. Methanol production kinetics

Methanotrophs convert CH4 and O2 to methanol and water via the methane

+ - monooxygenase (MMO) enzyme according to the reaction CH4+O2+2H +2e CH3OH+H2O

(Hanson and Hanson, 1996). Normally, methanotrophs oxidize methanol to formaldehyde via methanol dehydrogenase (MDH). Formaldehyde is either used for biosynthesis reactions or is further oxidized to generate electron donors (i.e. NADH+H+) (Hanson and Hanson, 1996). 145

Several chemicals (phosphate, EDTA, NaCl) can be added to methanotroph growth medium to inhibit the MDH enzyme and support methanol accumulation (Ge et al., 2014). However, inhibition of MDH also inhibits the enzymatic reactions that produce electron donors needed for CH4 oxidation by MMO (Ge et al., 2014). Electrons can be supplied by adding exogenous formate, because formate dehydrogenase (FDH) rapidly converts formate to CO2 and

NADH+H+ (Sheets et al., 2016). In fact, FDH in methanotrophs oxidizes formate (>100 mmol/g dry cells/h) (Patel and Hoare, 1971) at a faster rate than MMO oxidizes CH4 and O2

(2.5-21.8 mmol/g dry cells/h) (Lawton and Rosenzweig, 2016). Therefore, when MDH inhibitors and formate are added, methanol production can be simplified to Eq. 6.1:

HCOOH+CH4+O2 → CH3OH+CO2+H2O (Eq. 6.1) MMO

When formate is supplied in excess in aqueous medium, Eq. 6.1 can be adjusted to Eq. 6.2:

CH4+γ *O2 → CH3OH+CO2 (Eq. 6.2) O2/CH4 MMO

where γ is the O2 uptake to CH4 uptake ratio that takes into account the fact that O2/CH4 methanotrophs uptake about 1.3 times more O2 than the stoichiometric requirement (Petersen et al., 2016).

The rate equation for methanol production was developed considering Michaelis-

Menten kinetics with inhibition by methanol (Cáceres et al., 2016; Pen et al., 2014; Yoon et al., 2009) (Eq. 6.3):

C C C r =SA *X* CH4, L * O2, L *(1- CH3OH, L ) (Eq. 6.3) MMO MMO C +K C +K K CH4, L CH4 O2, L O2 CH3OH

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where SAMMO is the CH4 uptake rate (mol/cell mass/t), X is the cell concentration (cell

3 3 mass/l ), CCH4, L is the concentration of CH4 in the liquid medium (mol/l ), KCH4 is the half

3 saturation constant for CH4 (mol/l ), CO2, L is the concentration of O2 in the liquid medium

3 3 (mol/l ), KO2 is the half saturation constant for O2 (mol/l ), CCH3OH, L is the concentration of

3 3 methanol in the liquid medium (mol/l ), and KCH3OH is the methanol inhibition term (mol/l ).

For lab scale modeling, the TBR was assumed to be operated using methanotrophs that have the particulate MMO (pMMO), a CH4 uptake rate of 9 mmol/g cells/h, high affinity to CH4

3 (KCH4= 8.3 μM) and O2 (KO2= 2.0 μM) and low methanol tolerance (KCH3OH =9.375 mol/m =

0.3 g/L) (Table B.1) (Lawton and Rosenzweig, 2016). These parameters were selected based on the observation that the lab scale TBR was primarily inhabited by pMMO expressing methanotrophs (Methylocaldum sp.) that have low maximum methanol yields (~0.3 g/L)

(Sheets et al., 2017, 2016). The goal of large scale TBR modeling was to analyze the maximum potential of this system for methanol production, so the methanotrophs (use pMMO, 9 mmol/g cells/h) were assumed to have high methanol tolerance (937.5 mol/m3 = 30 g/L) (Table B.2) (Best and Higgins, 1981). The equations to describe CH4 and O2 uptake, and

CO2 and methanol production are shown in Table 6.1.

6.2.2. TBR model

The TBR was modeled as a one-dimensional isothermal (37°C) packed bed reactor operating in counter-current mode (Figure 6.1) (Sheets et al., 2017). Model development was inspired by Z. Wang et al. (2016)’s 1-D transport model that described CO2 absorption in amine solutions in a counter-current foam-packed bed reactor. In the current model, a mixed gas composed of biogas (65% CH4, 35% CO2) and air (79% N2, 21% O2) enters the bottom of

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the TBR at a superficial velocity, uGin (l/t). The liquid, which contains nitrate mineral salts

(NMS) medium, MDH inhibitor, and methanotroph cells at constant cell density, X (cell

3 mass/l liquid), is supplied at the top of the TBR at a velocity, uL (l/t) (Figure 6.1). The liquid also has excess formate to supply electron donors for methanol production (Section 6.2.1)

(Ge et al., 2014; Sheets et al., 2016). The liquid is distributed evenly and completely wets the packed bed as it flows downward. As CH4 and O2 are absorbed and converted to CO2 and methanol, the composition of the mixed gas varies. Thus, the velocity of the gas across the packed bed, uG (l/t), was calculated using Eq. 6.4 described by COMSOL (2012), which assumes constant mass flux across the reactor:

ρgin uG=uGin* (Eq. 6.4) ρg

3 where ρGin is the gas density at the inlet and ρG is the gas density (mass/l ) across the reactor.

The Transport of Concentrated Species interface in COMSOL® Multiphysics was used to describe mass transport of individual gas species (CH4, O2, CO2, N2) in the TBR (Eq.

6.5) (COMSOL Multiphysics, 2015):

dMN dT d dωi, G dz dz d - ∗ (ρ *Di, G* +ρ *ωi, G*Di, G* +Di, G* ) + *(ρ *uG*ωi, G)=Ri, G (Eq. 6.5) dz G dz G MN T dz G

2 -5 2 where dz is the differential height (l), Di,G is the diffusivity (l /t=2×10 m /s) of a species

(i=CH4, O2, CO2, N2) in the mixed gas, ωi,G is the mass fraction of a gas species (mass species/mass mixed gas), MN is the molecular weight (mass/mol) of the mixed gas

ωi,G -1 (MN=( ∑i ) ), Mi is molecular weight of an individual gas species (mass/mol), and T is the Mi temperature (K). N2 was in excess compared to the other components and was used in the

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software to ensure the mole fractions for gases along the TBR summed to unity (COMSOL

Multiphysics, 2015). In Eq. 6.5, the first term represents diffusive flux, the second term

3 represents the convective flux, and the third term, Ri,G (mass/l /t), refers to the mass transport of a gas (Eq. 6.6) (Z. Wang et al., 2016):

ωi, G*ρG*R*T Ri,G=-Kova* ( -Ci, L*Mi) (Eq. 6.6) Hi

where Kov is the overall mass transfer coefficient (l/t), a is the effective area for mass transfer

2 3 3 (l /l ), R is the universal gas constant (J/mol-K), Hi is the Henry’s Law constant (Pa*l /mol),

3 and Ci,L is the dissolved gas concentration (mol/l ) (i=CH4, O2, CO2) (Z. Wang et al., 2016).

The Henry’s Law constant for a gas species at 37°C (Hi,37℃) was calculated according to

Sander (2015) (Eq. 6.7):

-∆ H 1 1 H =H *exp ( sol * ( - ) ) (Eq. 6.7) i,37℃ i,25℃ R 310 298 where H is the Henry’s Law constant (Pa*l3/mol) at 25°C (298 K) and -∆solH is a heat of i,25℃ R solution constant (K). Previously, Henry’s Law has been shown to be reasonable to describe gas solubility in methanotroph reactors and absorption towers over the range of gas concentrations (5-25% CH4) and pressures (1-3 atm) evaluated in the current study (Cozma et al., 2015; Levett et al., 2016; Serra et al., 2006).

For relatively insoluble gases such as CH4, O2, and CO2 in H2O, the mass transfer rate is controlled by the liquid film resistance (Estrada et al., 2014b; Kim and Deshusses, 2008a,

2008b). In the present study, it was assumed that Kova ≈ KLa*EF, where KLa is the abiotic mass transfer coefficient and EF is a biological enhancement factor (Merchuk, 1977; Z. Wang

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et al., 2016). For lab scale model verification (Section 6.2.3), the KLa determined from experiments was used (3.18 h-1) (Table B.1) (Sheets et al., 2017). For large scale TBR

-1 modeling, KLa (122 h ) was calculated using a linearized correlation developed for porous ceramic spherical packing (Table B.2) (Kim and Deshusses, 2008a, 2008b) (Eq. 6.8):

(logC3+i3*uL) KLa=10 (Eq. 6.8)

where logC3 (1.43) and i3 (0.94) are constants and uL is the liquid velocity (5 m/h). EF was calculated using the equation proposed by Merchuk (1977) for aerobic biological processes that operate at constant cell density (Eq. 6.9):

Di,L*SAMMO*X EF=1+ 2 (Eq. 6.9) KLa * 2*( ) *Ci,L,EF ) aP

2 -9 2 where Di,L is the diffusivity of gases in the liquid phase (l/t =2×10 m /s), aP is the packing

2 3 ∗ 3 specific surface area (l /l ), and Ci,L,EF is the dissolved gas concentration (mol/l ) calculated based on the inlet gas concentration and Henry’s Law. In this study, a separate EF was calculated for each of the two gases consumed by methanotrophs (CH4, O2).

The Transport of Dilute Species interface in COMSOL® Multiphysics was used to describe the transport of components in the liquid (Eq. 6.10) (Z. Wang et al., 2016):

d dC * (-D * i, L +C *u ) =R (Eq. 6.10) dz i, L dz i, L L i, L

2 -9 2 3 where Di,L is the diffusion coefficient (l/t =2×10 m /s), Ci,L is the concentration (mol/l ) of a

3 chemical (i=CH4, O2, CO2, N2, CH3OH) in the liquid, and Ri,L is the reaction term (mol/l /t).

Again, the first two components in the parentheses on the left side of Equation 6.10 represent

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3 diffusive and convective flux, respectively. For dissolved gases, Ri,L (mol/l /t) was expressed by Eq. 6.11:

ωi, G*ρG*R*T ( -Ci, L*Mi) Hi Ri,L=K a* + ri (Eq. 6.11) ov Mi where the first term represents the mass transfer of gases into/out of the liquid and the second

3 term, ri, is the biological uptake/production rate shown in Table 6.1 (mol/l /t). It was assumed

that methanol did not leave the liquid phase, making RCH3OH,L equal to the MMO reaction

rate (rCH3OH =rMMO) (Table 6.1). Because the methanotrophs in the lab scale TBR system did

not use N2 as a nitrogen source, RN2, L was zero (Sheets et al., 2017). Counter-current operation required that the liquid velocity (uL) be input as a negative value in Eq. 6.10.

The boundary conditions for Equations 6.6 and 6.10 are:

dC at z=0, ω =ω 0, i, L =0 (Eq. 6.12) i, G i, G dz

0 dωi, G at z=HR. C =C , =0 (Eq. 6.13) i, L i, L dz

0 where HR is the TBR height (l), ωi,G is the mass fraction of a gas species at the gas inlet,

dC dω C 0 is the concentration of a species in the liquid at the liquid inlet, and i, L =0 and i, G =0 i, L dz dz specify that convection dominates flux at the outlets (Figure 6.1) (Z. Wang et al., 2016).

The baseline assumptions of the model are:

1. The gas and liquid are laminar and incompressible at the Reynolds numbers evaluated

in this study (Table B.1, Table B.2) (Z. Wang et al., 2016).

2. The gas-liquid interface is in equilibrium according to a Henry’s Law constant.

3. Gas-liquid mass transfer resistance can be described by a single parameter (Kova). 151

4. Liquid phase flows at constant velocity, has similar properties to water, and its

properties are unaffected by the biological reactions in the TBR.

5. The packed bed is completely wetted by the liquid phase.

6. Pressure drop has a negligible effect on TBR performance based on the observations

in Sheets et al. (2017).

7. Radial effects can be neglected (Kim and Deshusses, 2003).

8. Methanotroph cells at constant cell density suspended in the liquid convert biogas to

methanol, and any methanol produced remains in the liquid phase.

6.2.3. Laboratory scale TBR simulation

The laboratory scale TBR model solutions were compared to selected data from a recent study that used a semi-batch counter-current TBR for biogas to methanol conversion

(Sheets et al., 2017). The TBR described in Sheets et al. (2017) had both gas and liquid circulation and was assumed to be at steady-state such that the semi-batch data could be compared to model solutions from a series of steady-state continuous TBRs with equivalent design dimensions (Mohammed, 2011).

Figure 6.2 shows the schematic for modeling the TBRs in series. First, known operating parameters and species concentrations at the gas inlet (measured) and liquid inlet

(assumed at equilibrium with gas phase) were input into the model equations to solve for the:

1) density of the gas at the gas outlet (top of TBR); 2) velocity of the gas at the gas outlet; 3) concentrations of CH4, O2, and CO2 in the gas at the gas outlet; and 4) concentrations of

CH3OH, CH4, O2, and CO2 in the liquid at the liquid outlet (bottom of TBR) after one pass through the reactor. Then, the model solutions for gas density, gas velocity, and species

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concentrations in the gas/liquid outlets after the first pass were input into the appropriate equations (Eq. 6.4 and 6.9) and boundary conditions (Eq. 6.12 and 6.13) for the second pass.

This procedure was repeated 39 times to generate data that could be compared to two studies from Sheets et al. (2017) (Figure 6.2). The two studies used a lab scale TBR (0.51 m packed bed height, H/D ratio=10, atmospheric pressure) inoculated with a methanotrophic consortia dominated by bacteria classified in the order Methylococcales (cell density=0.14 g/L) and were supplied excess formate in the liquid medium (12 mmol). The only difference between the two lab scale studies were that each had different inlet gas compositions because they were supplied different biogas:air ratios (1:2.5 (CH4:O2=1:0.69) or 1:6.0 (CH4:O2=1:1.72))

(see Table B.1 for details). The gas retention time (τ) for each steady-state TBR in series was calculated using Eq. 6.14:

τ = 2*VG (Eq. 6.14) (uG, n-1+uG, n)*AR

where VG was the total gas volume in the reactor system (1.17 L), uG, n-1 is the gas velocity at

2 pass n-1, uG, n is the gas velocity at pass n (l/t), and AR was the entrance surface area (l ) of the TBR. The semi-batch data collected at different times (0, 2, 6, 10 h) was then compared with the model solutions at similar theoretical gas retention times (0, 8, 24, and 39 passes).

The mean absolute percentage error (MAPE) between the predicted and measured values was used to assess the validity of the model (Hadlocon et al., 2015).

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6.2.4. Sensitivity analysis

The sensitivity of the lab-scale TBR model to the selection of parameter values was analyzed using the method of López et al. (2016). Physical and kinetic parameters were varied 0.9 and 1.1 times their original value shown in Table B.1. The steady state solution was solved for the first pass through the TBR (operating at biogas:air ratio of 1:2.5) at the three different parameter values (0.9, 1.0, 1.1 times reference value). The relative change in exit CH4, O2, CO2, or methanol concentration compared to the base case value was used to determine the sensitivity of the model results to each parameter.

6.2.5. Large scale TBR simulation

The model was applied to determine potential trade-offs that could be encountered in a large scale TBR (H=20 m) with similar design dimensions to the lab scale TBR (H/D=10) that uses methanotrophs with high methanol tolerance (~30 g/L) and packing material with high specific surface area (2500 m2/m3) (Best and Higgins, 1981). The height of the TBR was selected based on a recent modeling study of syngas fermentation in bubble column reactors

(Chen et al., 2015). The following variables were adjusted to assess their impacts on reactor performance: 1) gas velocity; 2) cell density; 3) biogas to air ratio; and 4) reactor pressure.

The levels for these variables were not specifically addressed in lab scale TBR modeling (i.e. higher cell densities, velocities, and pressure), but were selected because they have been previously considered as realistic operating conditions for large scale biological gas conversion and methanotroph-based biotechnology applications in experimental and modeling studies (Strong et al., 2016, Levett et al., 2016, Kim and Deshusses, 2008a, 2008b,

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Chen et al., 2015) Aside from the modeling constraints listed in Section 6.2.2, the following additional criteria and assumptions were applied:

1. The TBR was 20 m tall and was well insulated at 37°C.

2. TBR was packed with the ceramic porous spheres described in Kim and Deshusses

(2008a) and Kim and Deshusses (2008b).

3. Reaction rate and mass transfer were varied by changing the cell density, which was

adjustable up to 40 kg/m3 (Strong et al., 2016).

4. Gas composition was varied by changing the biogas to air (CH4: O2) ratio.

5. The TBR headspace could be compressed up to 3 atm, gases were assumed ideal and

Henry’s Law was applicable. These assumptions have been applied in similar

methanotroph reactor modeling studies (Levett et al., 2016; Strong et al., 2016).

6. Dissolved gas concentrations at the entrance were set to zero to improve model

stability.

7. The enhancement factors were constant per Eq. 6.9.

8. Complete wetting and uniform liquid velocity could be attained in by installing

several liquid collectors and distributors along the length of the large scale TBR.

Liquid distributors are commonly used in large scale absorption towers and TBRs to

control reactor hydrodynamics (Ranade et al., 2011).

A detailed listing of other parameters and variables used for the large-scale simulations are shown in Table B.2. Reactor performance in the TBR was assessed by evaluating the gas and liquid phase concentrations of CH4, O2, and CO2, methanol concentration in the liquid;

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3 average CH4 mass transfer across the TBR (RCH4,G, mol/l /t, Eq. 6.6), and by calculating the

CH4 and O2 conversion ratios (%) (Eq. 6.15, i=CH4 or O2):

u *ω -u *ω Gas conversion (%)= Gin i, Gin Gout i, Gout *100 (Eq. 6.15) uGin*ωi, Gin

6.2.6. Numerical solutions

The differential equations and functions shown in Eq. 6.3-6.13 were solved numerically using the specified physical/chemical properties and boundary conditions via the finite-element method with COMSOL® Multiphysics Version 5.1 (COMSOL Multiphysics,

2015) licensed by the Ohio Supercomputer Center (Columbus, OH, USA) (Ohio

Supercomputer Center, 1987). A length independent, symmetric geometric mesh with 500 elements and element ratio of 50 was used to discretize the one-dimensional counter-current

TBRs. The equations were solved using the Fully Coupled Direct Solver (PARDISO) in

COMSOL (Schenk et al., 2001). A description of terms and boundary conditions that were input into the COMSOL modeling interface are shown in Table 6.2.

6.3. Results and Discussion

6.3.1. Laboratory scale TBR simulation

6.3.1.1. Comparison between simulations and laboratory data

The data produced by modeling a series of continuous steady-state TBRs (Section

6.2.3) were in agreement with results from semi-batch laboratory experiments in Sheets et al.

(2017) (Figure 6.3). At both biogas:air ratios (1:2.5, 1:6.0), the model predicted a nonlinear decrease in CH4 and O2 contents and increase in CO2 content. This was caused by 156

progressively lower gas velocity and lower gas consumption as the total gas retention time increased. Lower gas consumption rates were due to declining levels dissolved CH4 and O2.

At the high biogas:air ratio (1:2.5), dissolved CH4 and O2 levels were predicted to decline over the course of the experiment from 0.24 mol/m3 to 0.12 mol/m3 and 0.16 mol/m3

3 to 0.004 mol/m , respectively. Thus, dissolved CH4 levels were much higher than KCH4

3 (0.0083 mol/m ), indicating the gas conversion rate was limited by the level of O2 in the reactor. Meanwhile, at the low biogas:air ratio (1:6.0), dissolved CH4 and O2 levels were predicted to decline from 0.11 mol/m3 to 0.003 mol/m3 and 0.18 mol/m3 to 0.05 mol/m3, respectively. In that case, the rate-limiting reactant was CH4, because dissolved O2 levels

3 were always higher than KO2 (0.002 mol/m ). These results suggest that more stable gas consumption and methanol production rates could be observed if more CH4 (low biogas:air ratio) or O2 (high biogas:air ratio) were supplied after the rate limiting gas was completely consumed. Potentially, this type of steady-state model could be used to predict the appropriate distance above the gas inlet where additional gas should be supplied. In the future, low-pressure TBRs for CH4 conversion could be operated in parallel using biogas:air ratios that supply close to equimolar quantities of CH4 and O2.

The most important parameter that controlled methanol production kinetics was the

methanol inhibition term (KCH3OH ). For example, the MMO reaction rate in the TBR declined from 1.12 to 0.32 mol/m3/h in the laboratory scale model shown in Figure 6.3a.

Meanwhile, the expected MMO reaction rate would have only declined from 1.18 to 0.77

3 mol/m /h if KCH3OH was not included in Eq. 6.3 (biogas:air=1:2.5).

The MAPE for CH4, O2, CO2, and methanol were 16.1%, 58.9%, 21.5%, and 19.1% at biogas:air ratio of 1:2.5 (Figure 6.3a), and were 43.4%, 19.6%, 26.4%, and 59.4% at the

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biogas:air ratio of 1:6.0 (Figure 6.3b). Generally, models exhibit good accuracy at a MAPE from 11 to 20%, indicating that CH4 oxidation and methanol production were well predicted for the high biogas:air ratio experiment (1:2.5), and O2 conversion was reasonably predicted at the low biogas: air ratio (1:6.0) (Hadlocon et al., 2015). CH4 and O2 levels in both experiments were lower than experimental values, likely because the “series of TBRs” approach presumed that at each pass through the reactor, the gas and liquid entered an empty

TBR that had no dissolved gases or methanol. This caused a high concentration gradient that led to higher rates of gas-to-liquid mass transfer. Additionally, higher methanol concentrations in the experiments could have led to earlier inhibition than that predicted by the model. Poor prediction for methanol at the 1:6.0 biogas:air ratio was because CH4 was depleted and the bacteria consumed methanol during that experimental trial (Sheets et al.,

2017) (Figure 6.3b).

The actual CO2 levels in the lab scale TBR at both biogas:air ratios were normally lower than the predicted CO2 levels from the model. The sensitivity analysis showed that selecting a lower heat of solution constant for CO2, higher Henry’s Law constant for CO2 at

25°C, lower specific MMO activity, lower inlet dissolved CO2 levels, or lower reactor temperature would reduce CO2 levels in the gas phase outlet (Table 6.3). Higher Henry’s

Law constant and lower inlet dissolved CO2 levels would have lowered CO2 levels in the gas without considerably impacting CH4, O2, and methanol contents (Table 6.3). Another possible reason for the discrepancy is that the model did not account for gas-film resistance, which has more of an impact on the mass transport of CO2 than on CH4 and O2 (Kim and

Deshusses, 2008a, 2008b; Z. Wang et al., 2016). This suggests that assumptions involving

CO2 solubility could be refined to improve the accuracy of future models. Other

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improvements in the model could be made by taking into account radial effects, the impacts of time, characterizing the effects of attached cells, and by using computational fluid dynamics to include pressure effects (Iliuta et al., 2005). Nonetheless, the assumptions used in this model produced data that was similar to the lab scale results, especially at the high biogas:air ratio. Additionally, the methanol production curve was similar to other biological

CH4 to methanol studies that reported rapid increases in methanol concentrations, followed by a decline in production due to methanol inhibition (Kim et al., 2010; Pen et al., 2014).

6.3.1.2. Sensitivity analysis

The model was sensitive to many of the parameters that directly influenced gas-to- liquid mass transfer in the TBR (Table 6.3). Methanol production and gas conversion increased when aP and Di,L were increased because that increased the overall mass transfer coefficient, Kova. Therefore, continued research on the impacts of packing materials with high specific surface area to improve gas-liquid mass transfer should be conducted (Estrada et al, 2014b, Kim and Deshusses, 2008a, 2008b). Methanol concentration was predicted to be higher at lower reactor temperatures, indicating that low-temperature methanotrophs that have high CH4 oxidation rates could be useful for conversion of biogas to methanol.

Methanol production rates were predicted to improve at higher liquid velocity. However, methanol concentrations were lower at higher liquid velocity due to higher total liquid demands (López et al., 2013; Wang et al., 2016). There will likely be a tradeoff between higher methanol production rates and higher energy demands at high liquid flow rates, because energy requirements for methanol distillation increase at lower methanol concentrations, and because higher liquid flow rates increase the pressure drop across the

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TBR (Green and Perry, 2008; Kim and Deshusses, 2008b; Zakaria and Kamarudin, 2016).

Methanol concentrations increased after increasing the MMO activity and reducing the gas half saturation constants (KCH4, KO2), which could be achieved via genetic modification of methanotrophs (Kalyuzhnaya et al., 2015; Strong et al., 2016). Maximum methanol yields are expected to be limited by methanol inhibition, which highlights the need for identification and development of methanol tolerant strains (Best and Higgins, 1981).

6.3.2. Large scale TBR simulation

6.3.2.1. Effects of cell density, gas velocity, and biogas:air ratio

Methanol production rates and gas conversions in the large scale TBR model were highest at elevated cell densities (Fig. 6.4) because of higher MMO reaction rate and enhancement factors. However, at the lowest gas velocity (100 m/h), raising the cell density from 5 to 10 kg/m3, 10 to 20 kg/m3, and 20 to 40 kg/m3 only caused 18%, 11%, and 3% increase in methanol concentration, respectively (Figure 6.4a). These marginal improvements in methanol production at high cell densities were because CH4 and O2 were rapidly consumed (Figure 6.4a) (Cantera et al., 2016). Rapid CH4 and O2 consumption and CO2 production also caused the gas velocity to decrease (Eq. 6.4). These factors caused the gas conversion and methanol production curves to be highly non-linear at elevated cell densities

3 (>5 kg/m ) and 100 m/h gas velocity. In fact, nearly 95% of the O2 was converted in the bottom half of the TBR when cell density was set at 40 kg/m3 (Figure 6.4a). These predictions are very similar to a recent study in which Cáceres et al. (2016) observed considerable variation in gas phase CH4 concentrations along the length of a lab scale methanotrophic biotrickling filter (BTF) when gas flow rates were set to low levels (~0.3 160

L/min). In the current study, the highest gas conversions were predicted at the lowest gas velocity (100 m/h) due to increased gas-liquid contact time (Figure 6.4a) (Chen et al., 2015;

Z. Wang et al., 2016).

Higher gas velocities increased the average rate of CH4 and O2 transfer, leading to higher methanol concentrations in the TBR liquid (Fig. 6.4b, Fig. 6.4c). For instance, increasing the gas velocity from 100 m/h to 300 m/h caused nearly threefold improvement in methanol production at 40 kg/m3 cell density (Figure 6.4a, 6.4b). Chen et al (2015) also predicted that raising the syngas velocity in bubble column reactors from 100 to 300 m/h caused approximately two-fold increase in ethanol yields (Chen et al., 2015). Gas velocities above 100 m/h also caused more stable and nearly linear CH4 oxidation and methanol production curves (Figure 6.4b, 6.4c). This suggests that at elevated gas velocities (>300 m/h), CH4 and O2 transfer rates were high and TBR performance could be improved by increasing cell density (Cantera et al., 2016). Therefore, high cell densities and high gas velocities should be used to enhance CH4 oxidation in TBRs. Cell immobilization and/or encapsulation are attractive options to maintain high cell densities in methanotrophic TBRs.

However, methods to maintain strain homogeneity and ensure that MDH is inhibited throughout the TBR are needed (Devinny and Ramesh, 2005; Estrada et al., 2014b; Ge et al.,

2017; S. K. S. Patel et al., 2016c). Furthermore, diffusional resistances become more important in immobilized cell reactors, which could negatively impact mass transfer and methanol production rates (Kraakman et al., 2011).

The optimal biogas:air ratio for methanol production was predicted at 1:3.33

(CH4:O2=1:1.08) or 1: 5.50 (CH4:O2=1:1.77), because those biogas:air ratios provided more balanced levels of dissolved CH4 and O2 (Fig. 6.5). Similar to the laboratory scale results

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(Section 6.3.1), the lowest biogas to air ratio (biogas: air=1:12.00 (CH4:O2=1:3.87)) provided high O2 concentrations but was limited in CH4, while higher biogas: air ratios (1:2.25

(CH4:O2=1:0.73) and 1:1.6 (CH4:O2=1:0.52)) provided high CH4 concentrations, but were limited in O2.

6.3.2.2. Effect of pressure

Elevated reactor pressure improved methanol production at all biogas:air ratios because it increased the equilibrium concentrations of dissolved gases in the liquid medium

(Fig. 6.6) (Levett et al., 2016; Strong et al., 2016). Pressure positively impacted methanol production at all cell densities, but the greatest improvements were predicted at elevated cell densities (>1 kg cells/m3). As an example, methanol production only increased by ~7% when pressure was increased from 1 to 3 atm at 1 kg cells/m3 and 500 m/h gas velocity (data not shown). However, increases of 47-62% were predicted when the cell density was 5-40 kg/m3

(Figure 6.6). This was because dissolved gas levels in the TBR were already high at low cell densities. Higher pressures (4 and 5 atm) were investigated, but led to considerable error in the model solution at biogas:air ratios of 1:3.33 (CH4:O2=1:1.08) and 1:1.60 (CH4:O2=1:0.52)

(data not shown). This was likely because the high dissolved gas concentrations caused the gas consumption rates become unbalanced with the mass transfer rate, and because methanol levels in the liquid phase came close to the inhibitory concentration (30 g/L). Higher

Reynolds numbers were also expected at elevated pressure, indicating that turbulence could be more important under those conditions. Still, the predictions from this study indicate elevated TBR pressure could be used to overcome mass transfer limitations for biological conversion of biogas to methanol (Levett et al., 2016). Future work should compare the

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benefits of enhanced gas solubility at elevated pressures to the expected increase in cost due to higher energy consumption.

6.4. Conclusions

A steady-state model that considered gas-liquid mass transfer and gas oxidation by methanotrophs was developed to analyze the impacts of process parameters on the conversion of biogas to methanol in a trickle-bed bioreactor (TBR). The model was compared to and showed agreement with results obtained from a laboratory-scale TBR.

Simulation of a large scale TBR showed that reactor performance was strongly influenced by methanotroph cell density, gas velocity, gas composition, and operating pressure. Elevated pressures, high gas velocities, high cell densities, and TBR packing with high specific surface area were identified as possible methods to improve methanol production.

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Table 6.1: Biochemical reaction rates used in TBR modeling

Reaction term Symbol Equation

CH4 uptake rCH4 -rMMO

O2 uptake r -rMMO*γ O2 O2/CH4

CO2 production rCO2 rMMO

CH3OH production rCH3OH rMMO

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Table 6.2: Transport terms, boundary conditions and reactions used in TBR simulation

Transport terms and Reactions Boundary conditions Parameter Gas phase Liquid phase Chemical species Gas phase Liquid phase Diffusion Di,G Di,L CH4 RCH ,G RCH ,L coefficient 4 4 a Velocity uG -uL O2 RO2,G RO2,L

b 0 0 Inlet ωi, G=ωi, G Ci, L=Ci, L CO2 RCO2,G RCO2,L

c dωi, G dCi, L Outlet =0 =0 CH3OH N/A rCH OH dz dz 3 a. calculated across reactor length using Equation 4 b. gas phase inlet is bottom of reactor; Liquid phase inlet is top of reactor c. gas phase outlet is top of reactor; Liquid phase outlet is bottom of reactor i=CH4, O2, CO2, N2 in gas phase and i= CH3OH, CH4, O2, CO2, N2 in liquid phase

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Table 6.3: Sensitivity analysis of lab scale TBR model

Methanol in liquid CH4 at exit (%) O2 at exit (%) CO2 at exit (%) (kg/m3) Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity Parameter Symbol Unit -Δ10% +Δ10% -Δ10% +Δ10% -Δ10% +Δ10% -Δ10% +Δ10% Specific surface a m2/m3 -0.07 0.05 0.01 -0.01 0.02 -0.02 0.00 0.00 area of P packing Gas phase D m2/s 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 diffusivity i, G Liquid 2 phase Di, L m /s -0.03 0.03 0.01 0.00 0.01 -0.01 0.00 0.00 diffusivity 166 Heat of

solution ∆ H sol , CH K 0.01 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 constant for R 4 CH4 Heat of solution ∆ H sol , O K 0.01 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 constant for R 2 O2 Heat of solution ∆ H sol , CO K 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.07 constant for R 2 CO2 Henry’s Law 3 constant at HCH4, 25°C Pa/m /mol -0.06 0.05 0.02 -0.02 0.00 0.00 0.00 0.00 25°C for CH4 Continued

Table 6.3: Continued

Henry’s Law 3 constant at HO2, 25°C Pa/m /mol 0.00 0.00 0.00 0.00 0.02 -0.02 0.00 0.00 25°C for O2 Henry’s Law 3 constant at HCO2, 25°C Pa/m /mol 0.00 0.00 0.01 -0.01 0.01 -0.01 0.24 -0.23 25°C for CO2 Mass transfer KLa 1/h 0.02 -0.02 0.00 0.00 -0.01 0.01 -0.01 0.01 167 coefficient

Half saturation K μM 0.05 -0.04 0.00 0.00 0.00 0.00 0.00 0.00 constant for CH4 CH4 Half saturation K μM 0.02 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 constant for O2 O2 Methanol 3 inhibition KCH3OH mol/m -0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 term Specific mmol/g MMO SA -0.96 0.96 0.02 -0.02 0.05 -0.05 -0.02 0.02 MMO DCW/h activity

Continued

Table 6.3: Continued Inlet 0 3 dissolved CL,CH4 mol/m 0.00 0.00 -0.02 0.02 0.00 0.00 0.00 0.00 CH4 conc. Inlet 0 3 dissolved CL,O2 mol/m 0.01 -0.01 0.00 0.00 -0.03 0.03 0.00 0.00 O2 conc. Inlet 0 3 dissolved CL,CO2 mol/m 0.00 0.00 -0.01 0.01 -0.01 0.01 -0.23 0.23 CO2 conc.

168 Inlet gas u m/s 0.00 0.00 -0.02 0.02 -0.05 0.04 0.01 -0.01 velocity Gin

Liquid u m/s 1.08 -0.88 0.00 0.00 -0.01 0.01 0.02 -0.01 velocity L

Reactor T K 0.35 -0.70 -0.24 0.14 -0.21 0.13 -2.53 1.39 temperature R

O /CH 2 4 γ - 0.02 -0.03 0.00 0.00 0.05 -0.05 0.00 0.00 uptake ratio O2/CH4

*biogas:air ratio=1:2.5 (CH4:O2=1:0.69):

Liquid inlet Mixed gas outlet

uL, X, Ci,L,in ρGout,uGout, ωi,G,out 0 0 0 0 0 0 0 0 i=CH , O , CO , N i=CH4,L , O2,L , CO2,L , N2,L , CH3OH 4,G 2,G 2,G 2,G

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Air

Air: 21% O2, 79% N2

Liquid outlet Mixed gas inlet Biogas uL, X, Ci,L,out MIX ρGin,uGin, ωi,G, in 65% CH4, 35% CO2 0 0 0 0 i=CH , O , CO , N , CH OH i=CH , O , CO , N 4,L 2,L 2,L 2,L 3 4,G 2,G 2,G 2,G

Figure 6.1: Conceptualization of trickle bed reactor model for methanol production from biogas.

0 Ci,L ρG,1,uG,1, Ci,L,1 ρG,2,uG,2, Ci,L,n-2 ρG,n-1,uG,n-1, Ci,L,n-1 ρG,n,uG,n, ωi,G,1 ωi,G,2 ωi,G,n-1 ωi,G,n

……….

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ρ ,u , ρ ,u , Ci,L,1 Gin Gin Ci,L,2 G,1 G,1 Ci,L,n-1 ρG,n-2,uG,n-2, Ci,L,n ρG,n-1,uG,n-1, ω 0 ω ω ω Pass i,G i,G,1 i,G,n-2 i,G,n-1 (n): ………. n-1 n 1 2 Figure 6.2: Lab scale model verification using a series of steady-state TBRs: Each pass through the TBR occurred over a set gas

retention time, and produced results that were applied as boundary conditions for the following pass through the TBR. A designated

number of passes corresponded to theoretical retention times that were compared to laboratory-scale semi-batch data.

25% 1.0 Methane Oxygen (a) Carbon dioxide 20% Methanol 0.8

15% 0.6

10% 0.4

5% 0.2 (g/L) Methanol Gas (%) Gas composition

0% 0.0 0 5 10 Total gas retention time (h)

25% 1.0 Methane Oxygen (b) Carbon dioxide 20% Methanol 0.8

15% 0.6

10% 0.4

5% 0.2 (g/L) Methanol Gas (%) Gas composition

0% 0.0 0 5 10 Total gas retention time (h)

Figure 6.3: Comparison of model predictions from the “TBR in series” approach (solid lines) to laboratory scale data from Sheets et al. (2017) (symbols) at biogas to air ratios of 1:2.5 (a)

and 1:6.0 (b).

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1.0 1.0 1.0 1.0 (a) 0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

172 0.2 0.2 0.2 0.2 Dimensionless height (z/L) Dimensionless height

0.0 0.0 0.0 0.0 0% 5% 10% 15% 0% 5% 10% 15% 0% 10% 20% 30% 0 5 10 15

CH4 in gas (%) O2 in gas (%) CO2 in gas (%) Methanol (g/Lliq.) X=1 kg/m3 X=1 kg/m3 X=1 kg/m3 X=1 kg/m3 X=5 kg/m3 X=5 kg/m3 X=5 kg/m3 X=5 kg/m3 X=10 kg/m3 X=10 kg/m3 X=10 kg/m3 X=10 kg/m3 X=20 kg/m3 X=20 kg/m3 X=20 kg/m3 X=20 kg/m3 X=40 kg/m3 X=40 kg/m3 X=40 kg/m3 X=40 kg/m3 Continued Figure 6.4: Impacts of cell density on biogas to methanol conversion in the large scale TBR at atmospheric pressure and gas

velocities of (a) 100 m/h, (b) 300 m/h, and (c) 500 m/h (uL=5 m/h; biogas:air=1:1.33 (CH4:O2=1:1.08)).

Figure 6.4: Continued

1.0 1.0 1.0 1.0 (b)

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

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0.2 0.2 0.2 0.2 Dimensionless height (z/L) Dimensionless height

0.0 0.0 0.0 0.0 0% 5% 10% 15% 0% 5% 10% 15% 0% 10% 20% 30% 0 5 10 15

Continued

Figure 6.4: Continued

1.0 1.0 1.0 1.0 (c)

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

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0.2 0.2 0.2 0.2 Dimensionless height (z/L) Dimensionless height

0.0 0.0 0.0 0.0 0% 5% 10% 15% 0% 5% 10% 15% 0% 10% 20% 30% 0 5 10 15

1.0 1.0 1.0 1.0

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

175 0.2 0.2 0.2 0.2 Dimensionless height (z/L) Dimensionless height

0.0 0.0 0.0 0.0 0% 10% 20% 30% 0% 10% 20% 30% 0% 10% 20% 30% 0 5 10 15 CH4 in gas (%) O2 in gas (%) CO2 in gas (%) Methanol (g/Lliq.) Methane:Oxygen=1:3.87 Methane:Oxygen=1:3.87 Methane:Oxygen=1:3.87 Methane:Oxygen=1:3.87 Methane:Oxygen=1:1.77 Methane:Oxygen=1:1.77 Methane:Oxygen=1:1.77 Methane:Oxygen=1:1.77 Methane:Oxygen=1:1.08 Methane:Oxygen=1:1.08 Methane:Oxygen=1:1.08 Methane:Oxygen=1:1.08 Methane:Oxygen=1:0.73 Methane:Oxygen=1:0.73 Methane:Oxygen=1:0.73 Methane:Oxygen=1:0.73 Methane:Oxygen=1:0.52 Methane:Oxygen=1:0.52 Methane:Oxygen=1:0.52 Methane:Oxygen=1:0.52

Figure 6.5: Effects of methane to oxygen ratio on large scale TBR performance at atmospheric pressure, cell density of 40 kg/m3

and gas velocity of 500 m/h (uL=5 m/h).

30 Methane:Oxygen=1:3.87 (a) Methane:Oxygen=1:1.77 25 Methane:Oxygen=1:1.08

liq) Methane:Oxygen=1:0.73 3 20 Methane:Oxygen=1:0.52

Methanol (kg/m Methanol 5

0 1 2 3 Pressure (atm)

(b) Methane:Oxygen=1:3.87 100 Methane:Oxygen=1:1.77 Methane:Oxygen=1:1.08 Methane:Oxygen=1:0.73 80 Methane:Oxygen=1:0.52

conversion (%) conversion 40 4

CH 20

0 1 2 3 Pressure (atm) Continued

Figure 6.6: Effects of methane to oxygen ratio and pressure on methanol production (a), CH4 conversion (b), and O2 conversion (c) in the large scale TBR at inlet gas velocity of 500 m/h

3 and cell density of 40 kg/m (uL=5 m/h).

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Figure 6.6: Continued

100 Methane:Oxygen=1:3.87 (c) Methane:Oxygen=1:1.77 80 Methane:Oxygen=1:1.08 Methane:Oxygen=1:0.73 Methane:Oxygen=1:0.52 60

conversion (%) conversion 2

O 20

0 1 2 3 Pressure (atm)

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Chapter 7: Techno-Economic Comparison of Biogas Upgrading via Purified Biogas for Grid Injection, Compressed Natural Gas, Thermochemical Conversion of Biogas to Methanol, and Biological Conversion of Biogas to Methanol

Johnathon P. Sheetsa,b, Ajay Shahb a. Department of Food, Agricultural and Biological Engineering, The Ohio State University,

Columbus, OH, 43210, USA

b. Department of Food, Agricultural and Biological Engineering, The Ohio State

University/Ohio Agricultural Research and Development Center, 1680 Madison Ave.,

Wooster, OH, 44691-4096, USA

Technologies to upgrade biogas to value-added products have great potential to reduce greenhouse gas emissions and provide economic benefits to society. However, there are no studies that have compared the anticipated costs of conventional biogas upgrading technologies, such as purification for natural gas grid injection or compressed natural gas

(Bio-CNG), to emerging technologies such as thermochemical or biological conversion of biogas to methanol. Thus, the purpose of this study was to compare the techno-economic feasibility of upgrading biogas from a large-scale landfill or anaerobic digestion (AD) facility

3 3 (5900 Nm /h, 5,000,000 sft /d) to: 1) purified biogas (>97% CH4) for natural gas grid injection; 2) bio-CNG; 3) methanol via thermochemical conversion; and 4) methanol via biological conversion using methanotrophs (methane-oxidizing bacteria). Bio-CNG had the highest net present value (NPV) ($43 million), followed by purified biogas for grid injection

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($80,000), biological conversion of biogas to methanol (-$303 million), and thermochemical conversion of biogas to methanol (-$358 million). Methanol production costs were slightly lower for thermochemical conversion ($2.11/kg methanol, 1.99/kg after credits) compared to biological conversion ($2.24/kg methanol, $2.19/kg after credits) because the thermochemical technology had higher rates of methanol production (i.e. 7.6 Mgal/year for thermochemical compared to 6.0 Mgal/year for biological) and higher CH4 to methanol conversion ratios (i.e. 70% for thermochemical compared to 50% for biological). Sensitivity analysis suggested that biological conversion costs can be reduced if methanotrophs are modified to have higher CH4 oxidation rates and higher tolerance to methanol, and if the cost of formate is reduced.

7.1. Introduction

Population growth and the associated rise in food demands will undoubtedly lead to increased organic waste production (Hochman et al., 2015; Hodge et al., 2016). Currently, the majority of wastes produced in the United States are sent to landfills or anaerobic digestion (AD) systems, where anaerobic microorganisms convert organic materials to biogas

(USDA et al., 2014). Biogas, which is composed primarily of methane (30-70% CH4), carbon dioxide (30-70% CO2), and other impurities such as hydrogen sulfide (0-2000 ppm H2S), is a valuable carbon source for fuel and chemical production (Yang and Ge, 2016). In the U.S. alone, about 600 landfills and over 800 AD facilities generate, distribute, and use biogas for renewable fuels (USEPA, 2016e).

The most common method of biogas valorization is combustion in boilers, engines, and gas turbines to generate heat and electricity (USDA et al., 2014; USEPA, 2016c, 2015).

However, even the most efficient combined heat and power (CHP) technologies have

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considerable heat losses (40%). This has stimulated interest in the purification of biogas for direct injection to the natural gas grid or for use as transportation fuels such as compressed natural gas (Bio-CNG) (Budzianowski et al., 2016). Biogas can also be an alternative feedstock to make liquid chemicals, such as methanol, that are normally produced from natural gas (Blug et al., 2014; Ge et al., 2014; Yang et al., 2014).

Currently, pressurized (5-10 atm) water scrubbing (PWS) is the most common biogas cleaning method (~40% of all projects worldwide), because the process is well optimized. In contrast to the other cleaning technologies (i.e. amine absorption, pressurized swing absorption), PWS do not need expensive chemicals and/or adsorption media. This process can remove CO2 and H2S at the same time (Yang et al., 2014). Once biogas impurities are removed and the CH4 content is above 97%, biogas can be injected to natural gas pipelines, can be compressed (>200 atm) to form Bio-CNG, or can be thermo-chemically converted to chemicals such as methanol (Yang and Ge, 2016). The most common route for thermochemical conversion of CH4 to methanol consists of: 1) catalytic steam-reforming of

CH4 at high temperature/low pressure (700-1000°C, 1-30 atm) to produce syngas; 2) catalytic conversion of syngas to methanol at high pressure/low temperature (200-300°C, 50-150 atm); and 3) methanol purification via distillation (Riaz et al., 2013; Yang and Ge, 2016).

Biogas and oxygen (O2) can also be biologically converted to methanol using methanotrophs that have the methane monooxygenase (MMO) enzyme. Biological conversion of biogas to methanol has several advantages over other upgrading technologies.

One advantage is that several methanotroph strains isolated from AD systems can convert biogas from commercial AD facilities to methanol (Sheets et al., 2016; W. Zhang et al.,

2016). Additionally, biological conversion operates under mild conditions (25-40°C, 1-5 atm). This suggests that capital and operating expenses are probably lower than

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thermochemical conversion of biogas to methanol (Levett et al., 2016; Yang and Ge, 2016).

The disadvantages of biological conversion are that methanol dehydrogenase (MDH) inhibitors and electron donors such as formate are needed to support methanol production

(Sheets et al., 2016). Additionally, the highest reported tolerance for methanol is around 30 g/L, which limits yields and increases downstream methanol separation costs (Best and

Higgins, 1981). Although formate costs are currently too high (>$500/ton) to justify biological CH4 to methanol conversion, it has been predicted that formate costs can be reduced to $200/ton if off-peak electricity is used to electrochemically convert water and CO2 to formate (Yishai et al., 2016). At that price, biogas to methanol conversion has potential to be cost-competitive with other upgrading technologies.

Techno-economic analysis is a valuable tool to identify process bottlenecks, guide research, and compare the costs of conventional biogas upgrading technologies such as electricity generation, purified biogas for grid injection, and Bio-CNG (Deng and Hägg,

2010; Patterson et al., 2011a; Rotunno et al., 2017). However, there are no studies that have compared conventional biogas upgrading technologies (i.e. purified CH4 to gas grid, Bio-

CNG) to emerging processes such as thermochemical and biological conversion of biogas to methanol. Therefore, the objective of this study was to compare the techno-economic feasibility of four biogas upgrading scenarios (PWS to purified biogas (>97% CH4), Bio-

CNG, thermochemical methanol production, and biological methanol production). These analyses are critical to identify the most important technical aspects that need to be improved to enhance the commercial viability of these biogas valorization technologies.

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7.2. Modeling Overview

Four upgrading technologies compared via techno-economic analysis were: 1) biogas cleaning via pressurized water scrubbing (PWS) for grid quality purified biogas (>97 mol%

CH4); 2) cleaning via PWS and compression of purified biogas to Bio-CNG; 3) cleaning via

PWS and thermochemical conversion of purified biogas to methanol; and 4) biological conversion of unpurified biogas to methanol using methanotrophs. The major criteria used in the techno-economic analysis to compare between the four upgrading technologies were net present value (NPV), payback period, and internal rate of return (IRR) (Shah et al., 2016).

These four biogas upgrading technologies were each designed based on a 5900 Nm3/h

(5,000,000 std. ft3/d) biogas production rate from either a large landfill or AD facility (Hodge et al., 2016), and the biogas was assumed to have a composition of 65% CH4, 34.95% CO2 and 0.05% H2S (Yang et al., 2014). To produce this much biogas, about 307 MT/d of dry

3 organic waste would need to be anaerobically degraded (CH4 yield=300 Nm CH4/dry MT)

(Hodge et al., 2016). Currently, about 80 landfills listed in the U.S. EPA Landfill Methane

Outreach Program (LMOP) collect at least this much biogas, and almost ten wastewater treatment facilities equipped with AD will produce this quantity by 2040 (Murray et al.,

2014; USEPA, 2016c). AD systems that treat food waste, animal manure, biomass residues, and energy crops also have great potential to produce this much biogas. Purified biogas for grid injection and CNG facilities have been designed based on these biogas flow rates, but it is unknown whether thermochemical or biological conversion of biogas to methanol can be economically feasible at these biogas flow rates (Ge et al., 2016; Hodge et al., 2016; USDA et al., 2014). The processes were each designed in SuperPro Designer v. 9.5 (Intelligen,

2016), and the assumptions are described in detail in the following sections.

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7.2.1. Biogas cleaning via PWS

The PWS process was designed using an absorption tower, flash unit, and stripping tower as the primary unit operations (Figure C.1). Biogas (5663 m3/h (96% of total flow);

3 65% CH4, 34.95% CO2, 0.05% H2S) and recycled gas from the flash unit (194 m /h, 75.6%

CH4, 24.4% CO2, 0.01% H2S) were mixed and compressed to 10 atm before entering the bottom of an absorption tower (Bauer et al., 2013; USEPA, 2015). Fresh and recycled water were mixed to reach a total flow of ~150 m3/h and were pumped to 10 atm to the top of the absorption tower. The input water flow rate (~150 m3/h) was selected based on the suggestion

3 3 that ~0.2 m of H2O is needed per Nm biogas for efficient CO2/H2S removal (Bauer et al.,

2013; Cozma et al., 2015; Muñoz et al., 2015). The absorption efficiencies for CO2, H2S, and

CH4 were set at 95, 99.9, and 4%, respectively (Bauer et al., 2013; Budzianowski et al., 2016;

Cozma et al., 2015; Muñoz et al., 2015; Yang et al., 2014; Yang and Ge, 2016). The exit water (S-115 in Figure C.1) was sent to the flash unit (20°C, 2 atm), then off gas from the flash unit was sent to a condenser to remove excess water, and gas was recycled back to the absorption tower. The liquid from the flash unit was sent to an air stripper (2 atm, 25°C,

99.99% removal of all dissolved components), and 90% of the process water was recycled back to the absorption tower (Bauer et al., 2013; Cozma et al., 2015; Rotunno et al., 2017).

An air flow rate of 900 m3/h was used based on the assumption that ~20 m3/h of air is needed per m3 of stripper volume (Rotunno et al., 2017).

Most components (i.e. pumps, compressors, condensation unit) were sized using the

Design Mode in SuperPro Designer (Intelligen, 2016). The absorption tower (V=217 m3,

H=35.2 m, D=2.8 m) and stripping tower (V=45 m3, H=17.6 m, D=1.8 m) were designed by scaling up the PWS system described in Rotunno et al. (2017) by multiplying the volume

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used in their study by the ratio of the biogas molar flow rate in this study (230.5 kmol/h) and theirs (5.3 kmol biogas/h). This design also provided reasonable entrance velocities for the absorption tower (liquid velocity=24.4 m/h, gas velocity=980 m/h) and stripping tower

(liquid velocity=60 m/h, gas velocity=355 m/h) (Cooper and Alley, 2011). Both the absorber and stripping tower were assumed to be packed with generic low cost ($1/kg) foam packing

(85% porosity and 2000 m2/m3 specific surface area) that limited pressure drop and enhanced mass transfer (Z. Wang et al., 2016; Zhao et al., 2010). 630 kg of packing was required per m3 reactor volume and the packing had to be replaced every 40,000 operator hours

(Intelligen, 2016). Capital costs for all equipment in the PWS process were calculated using the Built-in Cost Model in SuperPro (Intelligen, 2016). A cost factor for corrosion and pressure resistant stainless steel 316 (1.3) was included for the absorption tower, flash unit, and stripping tower (Towler and Sinnot, 2013). Overall, these assumptions provided an efficient PWS process (>95% CH4 recovery) that produced grid quality purified biogas (97%

CH4, <3% CO2, <0.2 ppm H2S) (Yang and Ge, 2016). The price of purified biogas from the

PWS process was set at $0.176/Nm3 (~$5/thousand sft3) (USEIA, 2017a).

7.2.2. Bio-CNG

The Bio-CNG process consists of three main process steps: 1) biogas cleaning; 2) compression (197-247 atm) of purified biogas (>97% CH4) to Bio-CNG; and 3) CNG storage

(Figure D.1) (Yang and Ge, 2016). Therefore, the Bio-CNG process was just an extension of the PWS process model, and included a compressor that raised the pressure of the gas by 200 atm and a pressurized storage tank with 1560 Nm3 capacity (55,000 sft3) that costs $130,000

(2014 dollars) based on the DOE-CNG Vehicle Fueling Infrastructure report (Smith and

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Gonzalez, 2014). The selling price of Bio-CNG was set at $2 per gasoline gallon equivalent

(gge) ($0.78/kg, 2.65 kg CNG/gge) (USDOE, 2016, 2014).

7.2.3. Thermochemical conversion of biogas to methanol

Thermochemical conversion of biogas to methanol consisted of five process sections:

1) biogas cleaning via PWS; 2) steam-CH4 reforming to syngas in a plug flow reactor; 3) syngas to methanol conversion in a plug flow reactor; 4) methanol purification; and 5) energy recovery from unreacted gases (Figure E.1) (Wernicke et al., 2014).

7.2.3.1. Biogas cleaning via PWS

To reduce energy demands, 17% (990 Nm3/h) of the incoming biogas was used as a fuel source to generate steam required for the syngas production unit. Therefore, all previous assumptions for the PWS process were kept the same, except the input biogas flow rate was

4760 Nm3/h and the biogas recycled from the flash unit was ~150 Nm3/h, making the total flow entering the biogas compressor about ~4910 Nm3/h. The PWS process is a critical step to remove impurities such as H2S that can poison the metal catalysts used in the thermochemical conversion process (Yang and Ge, 2016).

7.2.3.2. Steam CH4-reforming for syngas production

CH4 was converted to syngas via steam-CH4 reforming (Riaz et al., 2013). The purified biogas (97% CH4, 3% CO2, <0.2 ppm H2S, 10 atm) from the PWS system was mixed with steam at a 3:1 steam to CH4 ratio before entering the steam-CH4 reforming reactor

(Zhang et al., 2017). The low-pressure steam (5 atm, 152°C) was generated using raw biogas

3 3 (986 Nm /h) as the fuel source. The steam-CH4 reformer (V=40 m , H/D=6, H=12.24 m,

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D=2.04 m) was designed based on the “small scale” reactor described by Ogden (2002) (0.1-

3 20,000,000 sft H2 production/d). The temperature and pressure of the steam reforming unit were set at 850°C and 5 atm, respectively (Yang and Ge, 2016; C. Zhang et al., 2016). Under those conditions, the equilibrium steam-CH4 reforming reaction can approach stoichiometric conversion ratios. Therefore, the reaction was modeled in an isothermal plug flow reactor based on Eq. 7.1 (Yang and Ge, 2016; C. Zhang et al., 2016):

kJ CH4+H2O → CO+3H2 ∆H298K=+12.9 (Eq. 7.1) 850°C, 5 bar g CH4 Ni/Al2O3 catalyst

The extent of the reaction for CH4 was set at 90% according to the report by Riaz et al. (2013). Because steam-CH4 reforming is highly endothermic, natural gas (i.e. CH4) was used as a heating agent. The natural gas properties were set based on 100% CH4 and the fuel was combusted to a temperature of 1200°C (Weber et al., 2000), had a return temperature of

900°C, a heat content of 50 MJ/kg (Sheets et al., 2015a), and a price of $0.18/kg (USEIA,

2017a). The equipment cost of the steam reformer ($4,500,000, 2001 dollars) was determined based on by Ogden (2002)’s estimate that the capital costs for small scale steam CH4

3 reforming systems (0.1-20 million ft H2/d) range from $150-200/kW H2 produced (0.18 kg/s

H2 produced in this study, HHV for H2=142 MJ/kg) (Ogden, 2002; USDOE, 2014). ZnO catalysts with void fraction of 50% and bulk density of 900 kg/m3 were assumed to cost

$55/kg ($25/lb) and had to be replaced every 40,000 operator hours (Jones and Zhu, 2009).

Overall, these assumptions provided a H2 yield of 67% (328 kmol/h H2/490 kmol/h feed gas), which is in the range (60-70% H2 yield) reported by Alves et al. (2013).

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7.2.3.3. Conversion of syngas to methanol

After the steam reformer, heat was recovered from raw syngas in a low pressure steam recovery heat exchanger (U=341 W/m2/K, 60 Btu/°F-ft2-h, shell and tube, 90% efficiency) (Green and Perry, 2008). Then, a condenser (20°C, 5 bar, 90% efficiency) was used to remove 99.2% of the water from the syngas. The water collected from the condenser was considered as a credit ($0.5/MT) because of its high purity (<0.1% dissolved CO2, <0.01 ppm dissolved H2S). The vapor stream from the condenser was sent to a component split unit to remove all the unreacted CH4 in the system. The component split unit was considered as a gas membrane system (100% CH4 removal) with a $200,000 equipment cost (2010 dollars), a membrane consumable cost of $20/m2 (10,400 m2 per equipment unit), and a membrane replacement rate of every 32,000 operator hours (Deng and Hägg, 2010). The syngas stream

(S-113 in Figure E.1) (74.1% H2, 24.7% CO, 0.72% CO2, 0.48% H2O) was compressed to

100 atm, was mixed with unreacted gas and sent to the methanol production reactor. The methanol production reactor was held at 250°C and 100 atm and three reactions (Eq. 7.2, 7.3,

7.4) occurred in parallel (Clausen et al., 2010; Jones and Zhu, 2009; Onel et al., 2016; Yang and Ge, 2016; C. Zhang et al., 2016):

kJ CO+2H ↔ CH OH ∆H =-3.2 (Eq. 7.2) 2 250°C, 100 atm 3 298K g CO Cu, ZnO,Al2O3 based catalysts

kJ CO2+3H2 ↔ CH3OH+H2O ∆H298K=-1.1 (Eq. 7.3) 250°C, 100 atm g CO2 Cu, ZnO,Al2O3 based catalysts

kJ CO+H O ↔ CO +H ∆H =-1.5 (Eq. 7.4) 2 250°C, 100 atm 2 2 298K g CO Cu, ZnO,Al2O3 based catalysts

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For simplification, these equilibrium reactions were treated as stoichiometric reactions, and the extent based on carbon species (CO (Eq. 7.2), CO2 (Eq. 7.3), and CO (Eq.

7.4)) were set at 25% (Eq. 7.2 and 7.3) and 10% (Equation 7.4, water-gas-shift) (Riaz et al.,

2013; Spath and Dayton, 2003; Yang and Ge, 2016). These conditions provided a per pass syngas (CO+H2+CO2) conversion efficiency of ~8%, which is within the range reported by

Blug et al. (2014) (4-14%). Additionally, the concentration of methanol in the gas phase leaving the reactor was 2.9 mol%, which is similar to that reported by Bussche and Froment

(1996) (2.0-2.5 mol%).

Because of low per-pass conversion, the unreacted syngas had to be recovered and recycled back to the methanol production reactor (Blug et al., 2014; Jones and Zhu, 2009;

Summers, 2014; Yang and Ge, 2016). It was assumed that 95% of the vapor phase was recycled (after condensation of process water/methanol mixture (HX-105 in Figure E.1)), heated via heat exchange with high pressure steam (U=341 W/m2/K, shell and tube, 90% efficiency) (Green and Perry, 2008), and mixed with the syngas stream. These assumptions have been used in several techno-economic studies for biomass, natural gas, and/or coal-to- methanol processes (Clausen et al., 2010; Jones and Zhu, 2009; Onel et al., 2016; Summers,

2014). The recycling of unreacted gas led to a global syngas conversion of 63%

(CO+H2+CO2)S-117-(CO+H2+CO2)S-121 (XSG-global= *100%), global H2 conversion of 55% (CO+H2+CO2)S-117

(H2)S-117-(H2)S-121 (XH2-global= *100%), global CO conversion of 92% (H2)S-117

(CO)S-117-(CO)S-121 (XCO-global= *100%), and a global CO to methanol conversion of ~90% (CO)S-117

(CH3OH)S-119 (XCO-CH3OH= *100%). This also indicated that the selectivity of CO towards (CO)S-117-(CO)S-121 methanol was about 98% (Blug et al., 2014; Moriarty, 2013; Yang and Ge, 2016). The total

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volume (14 m3) and equipment cost of the syngas to methanol conversion unit was $7.9 million (2012 dollars) based on Kempegowda et al. (2012)’s techno-economic analysis of a 2

MT/h methanol production reactor (current study=2.9 MT/h). This meant that the methanol conversion reactor had similar equipment cost to the steam CH4 reforming unit, which has been reported previously (Summers, 2014; Zhang et al., 2017). Cu/ZnO/Al2O3 catalysts with

50% void fraction and bulk density of 900 kg/m3 were assumed to cost $22/kg ($10/lb) and had to be replaced every 40,000 operator hours (Jones and Zhu, 2009).

7.2.3.4. Methanol purification

The methanol/water mixture from the condensation unit had 77% methanol, 18% water, and 5% dissolved CO2, which is similar to the concentrations reported by Blug et al.

(2014). The crude methanol stream was flashed (40°C, 1 atm) to remove dissolved gases and then was sent to atmospheric methanol distillation column (65°C, 1 atm) for final purification. The relative volatility of methanol over water, minimum reflux ratio (R/Rmin), feed quality, column pressure, vapor linear velocity, stage efficiency, and methanol product in distillate were set at 10, 1.25, 100%, 1.0 atm, 3.0 m/s, 35%, and 99% respectively

(Intelligen, 2013; Natarajan and Srinivasan, 1980). The capital costs for the flash unit and cooler were determined using the Built-in Cost Model in SuperPro. The capital cost for the distillation column was calculated using the cost function developed for ethanol distillation

(Intelligen, 2016, 2013). The final product (99.9% methanol) was cooled to 25°C, and the price was set at $400/ton (Atsonios et al., 2016; Kim et al., 2011; Pérez-Fortes et al., 2016).

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7.2.3.5. Energy recovery

Unreacted CH4, syngas and gases from the flash unit were mixed and sent to a boiler for low pressure (5 atm, 152°C) steam generation. The SuperPro Built-In Cost Model was used to estimate boiler and pump equipment costs (Intelligen, 2016).

7.2.4. Biological conversion of biogas to methanol

The biological conversion process consisted of four process sections: 1) methanotroph biomass production; 2) biogas-to-methanol conversion in a trickle-bed bioreactor (TBR); 3) methanol purification; and 4) energy recovery from excess biogas

(Figure F.1).

7.2.4.1. Methanotroph biomass production

Methanol production via methanotrophs is independent of cell growth. First, methanotroph biomass needs to be cultivated using biogas, air, and a nitrogen based medium

(ammonia mineral salts (AMS)) as CH4, O2, and nutrient sources, respectively. Then, a methanol dehydrogenase (MDH) inhibitor (i.e. 50-100 mM phosphate) and formate can be introduced to support conversion of CH4 to methanol (Sheets et al., 2016). In this study, methanotroph biomass production was modeled using the stoichiometry in Eq. 7.5 (Levett et al., 2016):

6.2332CH4+8.1253O2+0.3254NH4 → 3.2481CH1.8O0.5N0.2+2.9851CO2+10.6370H2O (Eq. 7.5)

Eq. 7.5 assumes that: (1) the biomass yield was 0.8 g biomass/g CH4 (Levett et al.,

2016), (2) 1.3 mol of O2 was needed per mole of CH4, (3) ammonium (NH4) was a limiting

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nutrient in AMS medium (contained ammonium sulfate (5 g/L), magnesium sulfate (1 g/L), and phosphate (0.6 g/L)), (4) any residual carbon from CH4 went to CO2, and (5) the mass balance was closed via water production (Bowman, 2006; Deshusses and Cox, 1999;

Petersen et al., 2016; Sheets et al., 2016). Eq. 7.5 was assumed to be exothermic with a low heat generation rate of 30 kJ/kg biomass. Biomass molecular weight was 24.63 g/mol

(Intelligen, 2016).

The kinetics of cell production were modeled with the Monod equation for multiple substrates (Eq. 7.6) (Shuler and Kargi, 2002):

C C Biomass production (g/L/h)=μ*X* CH4 * O2 (Eq. 7.6) C +K C +K CH4 CH4 O2 O2

-1 where μ is the growth rate (0.35 h ), X is the cell density (g cells/L), CCH4 is the

concentration of CH4 in the gas (mg/L), KCH4 is the half saturation constant for CH4 in the

gas (52 mg/L), CO2 is the concentration of O2 in the gas (mg/L), and KO2 is the half saturation

-1 constant for O2 in the gas (2.25 mg/L). A similar growth rate (0.37 h ) was used in a recent case study for protein G synthesis via genetically modified Methylococcus capsulatus (Bath)

(Strong et al., 2016). Gas phase substrate concentrations and half saturation constants were used in Eq. 7.6 because it was assumed that mass transfer limitations were overcome using a

pressurized (5 atm) biomass production unit (Levett et al., 2016). The values used for KCH4

and KO2 were calculated using Henry’s Law (at 37°C) and the half saturation constants for dissolved gases reported in Lawton and Rosenzweig (2016) (92 μmol CH4/L) and Sipkema et al. (1998) (2 μmol O2/L).

Preliminary analyses indicated that 10% of the biogas supply was needed to produce enough methanotroph biomass for methanol production. Thus, 590 Nm3/h of biogas and 2607

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Nm3/h of air were compressed to 5 atm and supplied to the biomass production unit (Figure

F.1). Biogas cleaning via PWS was not required because it was assumed that the methanotrophs had tolerance to H2S (W. Zhang et al., 2016). The biomass production unit was a pressurized (5 atm) continuous stirred tank reactor (CSTR) maintained at 37°C and had

90% working volume, similar to the methanotroph fermentors described in Criddle et al.

(2014). The biomass production unit was continuously fed with 40 m3/h of AMS medium and

0.005 MT/h of methanotroph inoculum (both pumped to 5 atm). The AMS solution was heat sterilized (140°C) prior to entering the biomass production unit. The inoculum provided an initial biomass concentration of 0.1 g/L. The total liquid dilution rate of the biomass production unit was set at 6 h such that ~99% of the CH4 in biogas was converted to biomass and CO2. Using these assumptions, the total volume of the biomass production unit was calculated at 268 m3 (241 m3 working volume).

The products of the biomass production unit were: 1) an aqueous exit stream (40.8 m3/h, 5 atm) that contained 4.8 g/L of methanotrophic biomass, and 2) a gaseous emission

3 stream (522 m /h, 5 atm) composed of 82.07% N2, 14.84% CO2, 2.90% O2, 0.18% CH4, and

0.01% H2S. The capital cost for the methanotroph biomass production unit was estimated

0.582 using an extrapolated capital cost function ($19530*VR , 2014 dollars) from Criddle et al.

(2014). An cost factor (1.3) was multiplied by the capital cost to account for corrosion and pressure resistant materials (Towler and Sinnot, 2013). Energy requirements (3 kW/m3) for the biomass production unit were also estimated from Criddle et al. (2014). Ammonium sulfate and magnesium sulfate prices were set at $80/MT and $350/MT, respectively, based on the cost estimates in SuperPro (Intelligen, 2016). The price of the inoculum was set at

$0.01/kg (Baral and Shah, 2016b).

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7.2.4.2. Biogas to methanol conversion in a TBR

Biogas (5310 Nm3/h) and air (25291 Nm3/h) were each compressed to 2 atm and supplied to the bottom of three parallel trickle bed reactors (TBRs). The liquid exit stream from the biomass production unit (“Biomass” in Figure F.1) was fed to the top of the TBRs.

The low maximum CH4 uptake rates (22 mmol/g cell/h) by methanotrophs indicated that high cell densities were needed to overcome kinetic limitations (Lawton and Rosenzweig, 2016).

Thus, cell recycle via disk-stack centrifugation was included because it has previously been used to maintain high ethanol fermentation rates (Ehstrom et al., 1991; Guidoboni, 1984;

Mota et al., 1987; Schulte et al., 2016; Shuler and Kargi, 2002). In this study, it was assumed that 95% of the cells exiting the TBR could be continuously reused for biogas conversion, because similar results have been obtained at lab scale (S. K. S. Patel et al., 2016c). The disk- stack centrifuge was configured to remove 2 μm particles at a 50% sedimentation efficiency and 95% biomass recovery rate, and the recycle stream (“Rec Biomass” in Figure F.1) was set at 300 g/L cell density (Petrides, 2003). Under these conditions, the biomass concentration in the TBR could be maintained at ~31 g/L. Similar biomass densities have been considered in recent techno-economic analyses for methanotrophic protein or PHB production (Levett et al., 2016; Strong et al., 2016).

Formate (5.0 MT/h) and phosphate (0.4 MT/h) were the electron donor and MDH inhibitor, respectively. This quantity of formate was selected because it provided close to the exact amount needed for maximum conversion. This quantity of phosphate was chosen because it provided phosphate concentrations in the TBR around 50 mM, which has previously been shown to inhibit the MDH enzyme (Sheets et al., 2016). Half of the process water after primary distillation was fed back to the TBRs after dead biomass was removed

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(via rotary vacuum filtration). This served to reduce water demands and improve process efficiency. All biomass inside the TBR was assumed to be active and participated in CH4 to methanol conversion.

Inside the TBR, the stoichiometry of methanol production was based on Eq. 7.7:

1.25HCOOH+CH4+1.3O2 → 0.8CH3OH+1.45CO2+1.428H2O (Eq. 7.7) MMO

Eq. 7.7 assumes a CH4 to methanol conversion of 80% (Ge et al., 2014), a formate to CH4 requirement of 1.25 (formate to methanol conversion=64%), and an O2/CH4 ratio of 1.3

(Petersen et al., 2016; Sheets et al., 2017). Eq. 7.7 also assumes that 100% of the reacted formate is converted to CO2 by formate dehydrogenase (FDH), that any unconverted carbon from CH4 is converted to CO2, and that remaining mass was balanced via water production

(Ge et al., 2014).

Methanol production kinetics in the TBR were modeled using Eq. 7.8:

C C C Methanol production (g/L/h)=SA *X* CH4 * O2 *(1- CH3OH, L ) (Eq. 7.8) MMO C +K C +K K CH4 CH4 O2 O2 CH3OH

-1 where is SAMMO is the specific CH4 consumption (0.35 g CH4/g cells h ) calculated from the maximum reported CH4 oxidation rate for methanotrophs (22 mmol CH4/g cells/h) described

in Lawton and Rosenzweig (2016), CCH3OH, L was the concentration of methanol in the liquid

(g/L), and KCH3OH is the methanol inhibition term (30 g/L). The value for the methanol inhibition term (30 g/L) was selected from the maximum reported methanol tolerance for

Methylosinus trichosporium OB3b (Best and Higgins, 1981). Other terms in Eq. 7.8 were

described earlier. The same values for KCH4 and KO2 used in Equation 7.6 were applied in

Equation 7.8.

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The three identical TBR units (maintained at 37°C) were sized at 500 m3 (~40 m tall,

~4 m diameter) and were packed with the same low-cost foam packing material as the PWS absorber (Z. Wang et al., 2016). Because the TBR already has good gas to liquid mass transfer properties, it was assumed that the pressure of the system only needed to be raised to

2 atm to overcome mass transfer limitations. The working volume to total volume ratio was set at 30% based on the expected liquid holdup inside the TBR (Ranade et al., 2011). This provided a total liquid residence time of ~4 h. Meanwhile, the liquid and gas velocity in each

TBR were ~3 m/h and ~400 m/h respectively, which are within the range of velocities used in large scale biotrickling filters for gas treatment (Deshusses and Cox, 2002). For simplicity, it was assumed that free cells in the liquid participated in biogas to methanol conversion, and the impacts of attached cells were not considered.

The capital cost of the TBR was estimated using the correlation made for biotrickling

0.767 filters by Deshusses and Cox (1999) ($13000*VR , 1999 dollars). No installed cost factor was included in the capital cost estimates because the correlation was for total installed cost.

The replacement frequency of the low-cost foam packing ($1/kg, 630 kg/m3 TBR volume) was set at every 40,000 operator hours. The passive methods to enhance mass transport in the

TBR allowed the electricity demands to be low at 0.1 kW/m3 of TBR volume (Deshusses and

Cox, 1999). Pressure drop was assumed to be included in these power requirements

(Deshusses and Cox, 1999). Labor hours for the TBR were set at 0.4 labor hours/h based on the report by Deshusses and Cox (2002). A formate cost of $200/ton was used in all simulations to carry out a future scenario where biological conversion of biogas to methanol may be feasible (Yishai et al., 2016). Phosphate (sodium phosphate) prices were set at

$500/ton based on online estimates (Alibaba.com, 2017).

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7.2.4.3. Methanol purification

The crude methanol exiting the disk stack centrifuge was sent to a two stage distillation process for methanol recovery and purification (Riaz et al., 2013). The two distillation units were modeled as continuous short-cut distillation units with the exact same parameters as the one described for the thermochemical methanol production process above.

As stated earlier, 50% of the process water from the first distillation unit was filtered (rotary vacuum filter, 500 L/m2 flux, cake porosity=0.4 v/v, 100% biomass removal), cooled and sent back to the TBR to recover water, formate, and phosphate. Purified methanol after the second distillation unit (99.6%) was cooled to 25°C. The price was set at ~$400/ton methanol

(Atsonios et al., 2016; Kim et al., 2011; Pérez-Fortes et al., 2016).

7.2.4.4. Energy recovery

The unconverted gas from the TBRs was sent to a boiler to generate low pressure steam (152°C, 5 atm). This also ensured that there were minimal CH4 emissions from the overall process.

7.2.5. Analyses

7.2.5.1. Resource assessment and GHG emissions

Major resource requirements (i.e. materials, consumables, utilities, waste) were allocated after solving the material and energy balances for each process. The resources/wastes were normalized to both the total inlet biogas flow rate (i.e. kg/Nm3 biogas)

3 and to the final product (i.e. kg/Nm purified CH4, kg/GGE CNG, kg/kg methanol) in order to compare between each of the four biogas upgrading technologies. Total water requirements

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3 (kg total H2O/Nm biogas) were calculated by summing the mass of water assigned as a material in the process to the total mass of heat transfer agents used (steam, cooling, and chilled water).

3 Greenhouse gas (GHG) emissions (kg CO2eq. emitted/Nm biogas) were calculated as the sum of direct CO2 or CH4 emissions from the process to the expected CO2 emissions from utility usage. CH4 was assumed to have 25 times the GHG effect as CO2. The CO2 emissions for low pressure (LP) steam (152°C, 5 atm) and high pressure (HP) steam (242°C, 34 atm) generation were calculated by setting up two separate continuous steam generation models in

SuperPro and calculating both the specific CO2 emissions (kg CO2/kg steam) from burning the fuel (used CH4 as fuel) and the specific electricity requirements (kWh/kg steam).

Likewise, the specific electricity demands for cooling room temperature water (25°C) to cooling water (5°C) were calculated by setting up an electric cooler (COP=4.5) model in

SuperPro (Intelligen, 2016). The CO2 emission for burning natural gas (3.06 kg/kg CH4 burned) in the steam-CH4 reforming reactor (Section 7.2.3) was calculated based on stoichiometry of combustion and a 90% heating efficiency. The CO2 emission per unit electricity (0.5545 kg CO2/kWh) was estimated from USEIA (USEIA, 2014). Overall, the specific CO2 production for LP steam, HP steam, chilled water, and natural gas were calculated at 0.1678 kg CO2/kg, 0.1718 kg CO2/kg, 0.0029 kg CO2/kg, and 3.056 kg CO2/kg, respectively.

7.2.5.2. Process economics

Economic parameters for investment and operational costs are shown in Table 7.1

(Shah et al., 2016). Equipment prices (Sections 7.2.1 to 7.2.4) were all adjusted to the analysis year ($2017) using cost index calculations in SuperPro (Intelligen, 2016). The total

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capital investment (TCI) cost was calculated as the sum of: 1) total plant direct cost (TPDC), which included equipment purchase costs (PC), installation, piping, instrumentation, electrical, insulation, buildings, yard improvement and auxiliary facilities costs; 2) total plant indirect cost (TPIC), which included engineering and construction fees; 3) contractor’s fee and contingency (CF&C); 4) working capital (WC); and 5) startup costs (SU) (Baral and

Shah, 2016b) (Table 7.1).

The annual operational costs were calculated as the sum of: 1) materials; 2) facility- dependent costs (=equipment maintenance (10% of PC) + depreciation + insurance (1% of

DFC)+ local taxes (2% of DFC) + factory expenses (5% of DFC)); 3) labor costs; 4) consumables; 5) lab/quality control/quality assurance (Lab/QA/QC=15% of total labor costs);

6) utilities (low pressure steam + high pressure steam + cooling water + chilled water + electricity costs); and 7) waste treatment and disposal costs (Baral and Shah, 2016b). Details on specific materials and consumables costs for each biogas upgrading technology are previously described (Sections 7.2.1 to 7.2.4). Operator hours estimates for selected equipment were estimated from Brown and Brown (2014), and labor costs were estimated as an adjusted labor rate of $69/h base salary (included basic rate, benefits, supervision, operating supplies, administration) (Baral and Shah, 2016b).

Demands for heating/cooling agents and electricity were calculated from the mass and energy balance results in SuperPro. The costs for steam, cooling water, and chilled water

(heat transfer agents) were set at 12, 0.05, and 0.4 $/ton, respectively (Baral and Shah, 2016b;

Intelligen, 2016). The electricity price was set at $0.07/kWh for industrial use (Baral and

Shah, 2016b; USEIA, 2017b). Wastewater treatment costs were set at $1.5/MT (Shah et al.,

2016), and any air emissions streams that contained H2S and/or SO2 (caused by combustion of H2S in biogas) had treatment costs of $0.50/MT ($500/MT pollutant, 0.1 mol% in

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emissions stream) (USEPA, 2003). Because high purity water was needed in all processes, water and RO water costs were set at $0.5/MT and $5.0/MT, respectively. The plants were all assumed to operate for 7920 h per year and production costs were estimated based on plant service life of 20 years (Baral and Shah, 2016b; USEIA, 2017b). Credits for low pressure steam (152°C, 5 atm) were set at $12/MT. The operational costs per volume of biogas supply

3 3 (Nm ) and per volume of product ($/Nm CH4, $/GGE CNG, $/kg methanol) and the NPV, payback period, and IRR were used to compare between the biogas upgrading technologies.

7.2.5.3. Sensitivity analysis

A sensitivity analysis was conducted for each biogas upgrading technology by varying major input parameters from their base case values. In most cases, the parameters were adjusted by ±20%. However, some parameters had strong influence on the model and were adjusted by lower amounts. The change in annual operating costs was divided by the percentage change in the parameter value to determine the relative sensitivity of operating costs to each parameter. The base case, low, and high values for each parameter that were varied in the sensitivity analyses are shown in Tables C.1, D.1., E.1., and F.1.

7.3. Results and Discussion

7.3.1. Material and resource analysis

The major utility requirements for PWS were electricity and cooling/chilled water for the gas compressors (Table 7.2). This is in coherence to the findings of Budzianowski et al.,

(2016). In fact, the specific power requirement for PWS (0.18 kWh/Nm3 biogas) was within the range reported in several other analyses of PWS (0.16-0.43 kWh/m3) (Bauer et al., 2013;

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Budzianowski et al., 2016; Kadam and Panwar, 2017; Muñoz et al., 2015; Patterson et al.,

2011a; Rotunno et al., 2017; Yang et al., 2014). Power requirements for the Bio-CNG process (0.32 kWh/Nm3 biogas) were nearly double than the PWS process because the high- pressure compressor had significant electricity demands (Table 7.2, Table C.4, Table D.4).

This electricity requirement was comparable to a recent estimate for PWS-CNG (0.32 kWh/Nm3 biogas) (Rotunno et al., 2017).

3 The total water consumption for Bio-CNG (36.86 kg H2O/Nm biogas) was 54%

3 higher than for PWS for purified biogas (23.89 kg H2O/Nm biogas), because of the additional cooling/chilled water needed to cool the high pressure-CNG compressor. The Bio-

3 CNG process had 16% higher CO2 emissions (0.80 kg CO2,eq/Nm biogas) compared to PWS

3 (0.69 CO2,eq/Nm biogas) because of higher utility demands. These results suggest that Bio-

CNG will have higher process-level resource requirements and GHG emissions than PWS alone. However, Patterson et al. (2011b) estimated that using bio-CH4 for transportation fuel had less aggregated environmental impacts (i.e. climate change, land use) than using it for centralized/distributed heating. This indicates that the final use of bio-CH4 has an important effect on life cycle environmental impacts of biogas upgrading (Patterson et al., 2011b).

Thermochemical conversion of biogas to methanol was energy intensive and was the only process that required external natural gas (Table 7.2). In fact, the endothermic steam-

3 3 CH4 reforming reaction required nearly 0.2 Nm natural gas for every Nm of biogas entering the system (Table 7.2). This amounts to about 30% of the total energy content in the total biogas added to the system (Wernicke et al., 2014). Similarly, Zhang et al. (2017) estimated that 0.31-0.32 MMBTU of fuel natural gas was needed for every MMBTU of feed natural gas for a larger scale methanol production plant. The thermochemical conversion process required the most electricity (0.55 kWh/Nm3 biogas) out of all the technologies evaluated.

200

This was primarily due to power demands for the high pressure (100 atm) syngas compressor

(Table E.4). A significant amount of cooling water/chilled water and LP/HP steam were needed to cool and heat syngas to the temperatures of the methanol production reactor

(250°C, 100 atm) and for crude product purification (Table E.4).

The biological conversion process had about 31% more air requirements than thermochemical conversion because the aerobic methanotrophs required O2 for methanol production. Additionally, the biological process needed nutrients for biomass cultivation and needed formate/phosphate for biogas to methanol conversion (Table 7.2).Total water

3 requirements for biological conversion (437 kg H2O/Nm biogas) were also 7% higher than

3 thermochemical conversion (410 kg H2O/Nm biogas) (Table 7.2). This was because the methanotrophs were suspended in water, which also caused the maximum methanol concentration exiting the TBRs (2-3 % v/v) to be much lower than the methanol stream exiting the condenser in the thermochemical process (18% v/v). Therefore, LP steam requirements for methanol distillation were much higher for biological conversion compared to thermochemical conversion (Table 7.2).

The biological conversion process also had a slower rate of methanol production (6.0

Mgal/year for biological conversion compared to 7.6 Mgal/year for thermochemical conversion). This caused a 20-21% higher power requirement per unit product (kWh/kg

MeOH) than thermochemical conversion (Table 7.2). The gas compressors, biomass production unit, rotary vacuum filter and biomass centrifuge also had significant power demands (Table F.4). Total CO2 emissions from the biological conversion process (3.09 kg

3 CO2eq./Nm biogas, 8.07 kg/kg MeOH) were slightly higher than thermochemical conversion

3 (3.04 kg CO2eq./Nm biogas, 6.08 kg/kg MeOH), because CO2 was produced from formate oxidation and from methanotroph metabolism.

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These results indicate that the rate of CH4 oxidation by MMO (max rate=22 mmol/g cell/h) needs to be improved (via genetic modification) to improve biomass growth rates and methanol production rates for this process to compete with thermochemical conversion

(Blanchette et al., 2016; Lawton and Rosenzweig, 2016). Additionally, utility requirements for methanol separation could be significantly reduced if methanol tolerance was improved

(Best and Higgins, 1981). Finally, new electron donors (i.e. renewable H2, electricity) that do not generate additional CO2 could be used to reduce GHG emissions (Blanchette et al., 2016;

Ge et al., 2014).

7.3.2. Economic analysis

7.3.2.1. PWS for purified biogas

The specific capital cost and operational costs for the PWS process ($3800/sft3 biogas/min, $4.3 million/yr) were close to those reported by USEPA for upgrading landfill gas to grid quality biogas ($2600-4300/sft3/min, $0.875-3.5 million) (USEPA, 2015). The costliest piece of equipment for PWS was the biogas compressor (43.6%), followed by the absorption tower (17.2%), stripper (6.1%), water pump (5.2%), air compressor (3.7%), flash unit (2.5%) and condenser (1.7%). Unlisted equipment made up 20.0% of equipment costs

(Table C.3).

3 3 The estimated cost for purified biogas via PWS was $0.16/Nm CH4 ($0.09/Nm biogas), which was very comparable to the range of estimates ($0.06-0.26/Nm3 biogas,

3 $0.09-0.40/Nm CH4) reported previously (Figure 7.1a) (Balussou et al., 2012; Cozma et al.,

2015; Patterson et al., 2011a; Rotunno et al., 2017; Yang et al., 2014; Zhao et al., 2010). The most significant contribution to operational costs were facility (54%), labor (21%), and

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utilities (16%) (Figure 7.1a). As expected, the highest contributor to utility costs was electricity for the biogas compressor (63% of total utility costs) (Table C.4), which is in agreement with previous studies (Muñoz et al., 2015; Rotunno et al., 2017). At a purified

3 3 biogas (97% CH4) selling price of $0.176/Nm (~$5/thousand sft CH4)(USEIA, 2017a), the

NPV, payback period, and after-tax internal rate of return (IRR) for this PWS system were estimated at ~$80,000, 10 years and 7%, respectively. This system was profitable because it was based on a relatively large biogas production facility where costs are reduced compared to smaller operations (i.e. farm scale) (Brown and Brown, 2014; Rotunno et al., 2017).

Therefore, the influence of biogas production rate (farm-scale to landfill-scale) on the profitability of PWS should be evaluated in future studies.

7.3.2.2. Bio-CNG

The addition of the high-pressure-CNG compressor and storage unit to the PWS process increased total investment costs by $7.5 million (58% increase) and raised annual operating costs up to $0.16/Nm3 biogas (72% increase) (Table 7.3, Figure 7.1b). The estimated cost for CNG was $0.36/kg CNG, or $0.95 per gasoline gallon equivalent, which is within the range estimated by USEPA for landfill gas-to-CNG ($0.68-1.40/gge) (USEPA,

2015) (Figure 7.1b). Despite higher capital and operational costs, the NPV ($43 million), payback period (3 years), and IRR (42%) for the Bio-CNG was much better than PWS-to- purified biogas. This was likely because the selling price for CNG was fairly high ($2/gge) and operational costs were low because the gas compressors and pumps in the process model were efficient (USDOE, 2016; USEIA, 2017b). Overall, these results suggest that upgrading

CH4-rich biogas from large scale biogas production facilities to Bio-CNG can be economically feasible (USEPA, 2015).

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7.3.2.3. Thermochemical conversion of biogas to methanol

Thermochemical conversion of biogas to methanol had the highest capital investment

(~$170 million) and operational costs ($48 million/year) out of all the upgrading technologies evaluated (Table 7.3). High capital costs were primarily attributed to the syngas to methanol conversion reactor (33% of total equipment costs), steam-CH4 reformer (28%), and syngas compressor (8%). Unlisted equipment was 20% of total equipment cost (Total Equipment

Cost≈$26 million) (Table E.3). Equipment costs ($1170/ton per year methanol capacity) in this study were similar to an estimate for small scale methanol production plants ($700-

1,100/ton per year) (Turaga, 2017).

The estimated methanol production costs were $2.11/kg methanol ($1.99/kg after steam credits) ($1.05/Nm3 biogas), which was primarily made up of facility-dependent costs

(63%), utilities (22%), and labor (12%) (Table 7.3, Figure 7.1c). Material costs were low

(<$200,000/year) because biogas was free at the source. Typically, natural gas prices have the greatest impact on natural gas-to-methanol process economics (40-50% of operating costs), followed by facilities (40-50%), then utilities (<5%) (Blug et al., 2014). The major utility requirements for thermochemical conversion in the current study were cooling/chilled water for the methanol production reactor/condensers, natural gas for the steam-CH4 reformer, and electricity demands for gas compression (Figure 7.2a, Table E.4). This illustrates the considerable energy demands for thermochemical methanol production plants

(Riaz et al., 2013). For comparison, Okeke and Mani (2017) estimated that Fischer-Tropsch fuel production costs were ~$4/gal FT fuels at a 5,000-6,000 Nm3/h biogas flow rate, which is slightly lower than methanol production costs in the current study ($6-7/gal methanol).

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The thermochemical biogas to methanol process was not economically feasible

(NPV=-$358 million) because of the high capital/operational costs and low methanol price

($400/MT). However, this process model was performed at a scale much smaller than traditional large methanol production plants (>5,000 MT methanol/d) based on natural gas

(<$0.2/kg operating costs) (Blug et al., 2014; Riaz et al., 2013). A possible strategy to improve economic feasibility is to send biogas from several landfills/AD systems to a centralized facility, because even the largest landfill in the U.S. (Puente Hills, CA) does not produce enough gas (current production=45 million sft3/d, 53,000 Nm3/h) to meet the scale needed to compete with fossil-fuel based methanol (USEPA, 2016c). Biogas could also be better utilized via dry reforming of CO2 to produce additional syngas, or CO2 could be hydrogenated directly to methanol (Atsonios et al., 2016). An alternative approach is to divert biomass wastes from landfills/AD to gasifiers for syngas production. In fact, some large-scale biomass-to-methanol plants based on gasification were predicted to have low methanol production costs ($0.2-0.4/kg) (Blug et al., 2014; M. Patel et al., 2016).

7.3.2.4. Biological conversion of biogas to methanol

The biological process to convert biogas to methanol had significantly lower investment ($98 million) and operational costs ($40 million/year) compared to the thermochemical conversion process (Table 7.3). This was because the biological conversion process had ~40% lower total equipment costs and had over 50% lower heat transfer agent costs (Table 7.3, Figure 7.2b). However, the methanol production cost ($2.24/kg methanol,

$2.19/kg with steam credits) was slightly higher than the thermochemical conversion (Figure

7.1c, Figure 7.1d). This was primarily attributed to the costs for formate/phosphate (>$0.5/kg methanol) and because the biological methanol production rate was about 20% slower than

205

thermochemical conversion (Figure 7.1d) (Section 7.3.1.2). The global CH4 to methanol conversion ratio (52%) was also lower than the thermochemical conversion process (70%).

Total electricity costs were comparable between both methanol production technologies, primarily because the gas compressors, biomass production unit, biomass centrifuge, and rotary vacuum filter, and TBR had significant electricity demands (Figure 7.2) (Table F.4).

The highest heat transfer agent costs for biological conversion were attributed to low pressure steam for primary distillation (Figure 7.2, Table F.4). The biological conversion process was not economically feasible, but had better net present value than thermochemical conversion because of lower capital costs (NPV=-$303 million).

These results illustrate several major issues that need to be addressed to improve the commercial feasibility of the biological biogas-to-methanol process. First, the use of free methanotroph cells for biocatalysis is expensive and complex. For example, biomass production made up about 14% ($0.31/kg methanol) of the total production costs.

Meanwhile, catalyst costs for thermochemical conversion were less than 0.01% of total production costs ($186,000/year). A potential option to overcome this issue is to use MMO enzymes directly for CH4 to methanol conversion, because they may have better reusability than whole cell systems (Blanchette et al., 2016). However, the rate of CH4 oxidation by

MMOs are currently too slow to be competitive with catalytic conversion. Genetic modification could be a valuable tool to bridge the gap and improve CH4 oxidation and conversion rates (Lawton and Rosenzweig, 2016). Additionally, cost effective electron donors need to be developed to improve process economics. In this study, formate costs ($7.9 million/year) were higher than the expected revenues from methanol ($7.1 million), even though low formate prices ($200/MT) were considered. Therefore, formate conversion ratios need to be improved, the costs of formate need to be reduced, or alternative electron donors

206

that can supply more than one reducing equivalent (i.e. acetate, renewable electricity) need to be developed (Ge et al., 2014). Economies of scale using CH4 from shale gas wells could also improve the economic feasibility of biological or thermochemical conversion technologies

(Levett et al., 2016; Venvik and Yang, 2017). In the short term, it is more attractive to develop methanotroph-based biotechnologies for higher value products such as polyhydroxybutyrate, protein, or ectoine (Criddle et al., 2014; Strong et al., 2016).

7.3.3. Sensitivity analysis

Production costs for the PWS, Bio-CNG, and thermochemical methanol processes were most sensitive to the methane content in biogas. In all cases, varying the biogas composition by 15.4% (to 55% or 75% CH4) led to a 7-16% decrease (at 55% CH4) or increase (at 75% CH4) in upgrading costs (Figure 7.3). This indicates that optimization of AD to maintain high CH4 contents and high biogas yields can improve the profitability of downstream biogas upgrading. The PWS-to-purified biogas and the Bio-CNG processes were both sensitive to compressor/pump efficiencies (Figure 7.3a, Figure 7.3b). This indicates that new methods to improve compressor efficiency, such as a rotary hydraulic pumping, could lower costs (Budzianowski et al., 2016). The thermochemical methanol production process was most sensitive to parameters involved with the steam-CH4 reformer and the syngas-to- methanol production reactor (i.e. syngas recycle rate, CH4 to syngas conversion, syngas to methanol conversion, capital costs) (Figure 7.3c) (Riaz et al., 2013). This indicates that development of new reactors that are more suitable for small scale systems (i.e. membrane, microchannel packed bed w/ integrated heat exchange) could lower costs for thermochemical conversion of biogas-to-methanol (Riaz et al., 2013; Venvik and Yang, 2017).

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The biological process to convert biogas to methanol was most sensitive to centrifuge recycle rate (Figure 7.3d). Also, CH4/formate conversion and CH4 oxidation rate had considerable influence on production costs (Figure 7.3d, Figure 7.3e). This illustrates the importance of high methanotroph cell densities in order to overcome the slow rate of CH4 oxidation by MMO (Lawton and Rosenzweig, 2016). Methanol production costs were greatly impacted by product toxicity (Baral and Shah, 2016b). For example, when methanol tolerance was increased by 20%, production costs decreased by 11% (to <$2/kg methanol).

Methanol tolerance could be improved via genetic modification/adaptive evolution (Yang and Ge, 2016). Vacuum recovery units that can remove methanol from the reactor as it is produced could also be used lower production costs (Baral and Shah, 2016b). Liquid holdup and TBR capital costs also had considerable sensitivity, suggesting that lower cost TBRs that have high porosities, low pressure drops and high mass transfer rates could reduce methanol production costs.

7.4. Conclusion

The techno-economic feasibility of two existing and two emerging technologies for biogas upgrading at a large biogas production facility were compared. Cleaning and compression to Bio-CNG had the best profitability out of all the technologies because of high

CNG prices. Biological conversion of biogas to methanol had lower capital and operational expenses compared thermochemical conversion, but neither process was profitable at the biogas flow rates evaluated in this study. The sensitivity analysis suggested that the costs for biological conversion can be reduced if methanol tolerance and CH4 oxidation rates are improved, and if electron donors that are more cost-effective than formate are developed.

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Table 7.1: Economic evaluation parameters for biogas upgrading technologies

Time Financing Construction Value Value Value Parameters Parameters Plan First year Year of Analysis 2017 Debt (%) 40 30 (% of TPDC) Year Loan term Second year Construction 2017 10 40 (years) (% of TPDC) Starts Construction Loan interest Third year 18 8 30 period (months) (%) (% of TPDC) Start-up Depreciation 12 straight-line (months) method Depreciation Project life 20 period 10 (years) (years) Income tax rate Inflation rate (%) 3 40 (%) Fixed cost Operating Plant direct cost Value Value Value parameters parameters parameters Unlisted Annual Process Piping (% Equipment 20 operating time 7920 35 of PC) (% of PC)* (h) Auxiliary Salvage factor Instrumentation facilities 40 5 40 (% of DFC) (% of PC) (% of PC) Engineering (% Start-up cost (% Insulation 25 5 3 of TPDC) of DFC) (% of PC) Construction (% Electrical 35 10 of TPDC) (% of PC) Contractor’s fee Buildings (% of 5 45 (% of PC) TPDC+TPIC) Yard Contingency (% 10 Improvement (% 15 of TPDC+TPIC) of PC) PC=Purchase Cost of Equipment (Listed Equipment Purchase Cost+Unlisted Equipment Purchase Cost); TPDC=Total Plant Direct Cost= PC+Installation+Piping+Instrumentation+Insulation+Electrical+Buildings+Yard Improvement+Auxiliary Facilities; TPIC=Total Plant Indirect Cost=Engineering+Construction costs; CF&C=Contractor’s Fee+ Contingency; DFC=Direct Fixed Capital=TPDC+TPIC+ CF&C. *installation costs of unlisted equipment set at 50% of unlisted equipment purchase cost.

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Table 7.2: Resource requirements for biogas upgrading technologies

Pressurized Thermochemical- Biological- Water Bio-CNG Methanol Methanol Scrubbing Bulk kg/ kg/ kg/ kg/ kg/ kg/ kg/ kg/ 3 3 3 3 3 Materials Nm biogas Nm p-CH4 Nm biogas GGE Nm biogas kg MeOH Nm biogas kg MeOH Air 0.19 0.30 0.19 1.09 4.13 8.25 5.39 14.07 Amm. Sulfate 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.09 Biogas 1.01 1.61 1.01 5.90 1.01 2.03 1.01 2.65 Biomass 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Formic Acid 0.00 0.00 0.00 0.00 0.00 0.00 0.85 2.21 Magne Sulfate 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 Phosphate 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.19 RO water 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 Water 2.63 4.17 2.63 15.33 8.35 16.67 8.59 22.39

210 Total 3.84 6.07 3.84 22.32 13.49 26.94 15.97 41.66

Heat kg/ kg/ kg/ kg/ kg/ kg/ kg/ kg/ Transfer Nm3 biogas Nm3 p-CH Nm3 biogas GGE Nm3 biogas kg MeOH Nm3 biogas kg MeOH Agents 4 Natural Gas 0.00 0.00 0.00 0.00 0.13 0.25 0.00 0.00 LP Steama 0.00 0.00 0.00 0.00 0.40 0.80 4.77 12.45 HP Steamb 0.00 0.00 0.00 0.00 2.20 4.40 0.00 0.00 Cooling Water 15.58 24.67 15.58 90.67 95.38 190.47 421.22 1098.68 Chilled Water 5.67 8.98 18.65 108.50 303.19 605.47 2.07 5.40 Total H.T. Agents 21.26 33.65 34.23 199.17 401.30 801.39 428.06 1116.53

kWh/ kWh/ kWh/ kWh/ kWh/ kWh/ kWh/ kWh/ Electricity 3 3 3 3 3 Nm biogas Nm p-CH4 Nm biogas GGE Nm biogas kg MeOH Nm biogas kg MeOH Std. Power 0.18 0.28 0.32 1.84 0.55 1.10 0.51 1.33 Continued

Table 7.2: Continued kg/1000 kg/1000 kg/1000 kg/1000 kg/1000 kg/1000 kg/1000 kg/1000 Consumables 3 3 3 3 3 Nm biogas Nm p-CH4 Nm biogas GGE Nm biogas kg MeOH Nm biogas kg MeOH Packing 0.57 0.90 0.57 3.31 0.56 1.12 4.00 10.45 SMR catalyst - - - - 0.03 0.06 - - MP catalyst - - - - 1.21 2.41 - - Membranec - - - - 0.03c 0.06c - -

Waste kg/ kg/ kg/ kg/ kg/ kg/ kg/ kg/ 3 3 3 3 3 Production Nm biogas Nm p-CH4 Nm biogas GGE Nm biogas kg MeOH Nm biogas kg MeOH Emissions 0.76 1.21 0.76 4.43 5.01 10.00 6.41 16.72 Aqueous Waste 2.63 4.17 2.63 15.31 2.67 5.32 7.32 19.08 Total 3.39 5.37 3.39 19.74 7.67 15.32 13.72 35.80

LP steam=low pressure steam (152°C, 5 atm) HP steam=high pressure steam (242°C, 34 atm) 211 m2/unit biogas or m2/unit product

Table 7.3: Comparison of investment and operational costs for biogas upgrading technologies

Thermo- Bio- PWS Bio-CNG Methanol Methanol Investment Costs

Total Plant Direct Cost (TPDC) $ $ $ $ 1. Equipment Purchase Cost 1,958,000 3,090,000 25,658,000 15,859,000 2. Installation 979,000 1,531,000 13,162,000 4,150,000 3. Process Piping 685,000 1,082,000 8,980,000 5,551,000 4. Instrumentation 783,000 1,236,000 10,263,000 6,344,000 5. Insulation 59,000 93,000 770,000 476,000 6. Electrical 196,000 309,000 2,566,000 1,586,000 7. Buildings 881,000 1,391,000 11,546,000 7,137,000 8. Yard Improvement 294,000 464,000 3,849,000 2,379,000 9. Auxiliary Facilities 783,000 1,236,000 10,263,000 6,344,000 TPDC 6,616,000 10,430,000 87,056,000 49,825,000

Total Plant Indirect Cost (TPIC) 10. Engineering 1,654,000 2,608,000 21,764,000 12,456,000 11. Construction 2,316,000 3,651,000 30,469,000 17,439,000 TPIC 3,970,000 6,258,000 52,233,000 29,895,000

Contractor’s Fee and Contingency (CF&C) 12. Contractor's Fee 529,000 834,000 6,964,000 3,986,000 13. Contingency 1,059,000 1,669,000 13,929,000 7,972,000 CF&C 1,588,000 2,503,000 20,893,000 11,958,000

Direct Fixed Capital Cost 12,174,000 19,191,000 160,182,000 91,678,000 (DFC=TPDC+TPIC+CF&C) Working Capital 167,000 303,000 1,530,000 1,972,000 Startup Cost 609,000 960,000 8,009,000 4,584,000

Total Capital Investment (TCI) 12,950,000 20,454,000 169,721,000 98,233,000

Operational Costs Cost Item $/year $/year $/year $/year Materials 59,085 59,085 189,900 10,040,920 Facility 2,287,065 3,605,660 30,084,474 17,323,878 Labor 897,789 1,717,509 5,581,903 5,620,937 Consumables 25,551 25,551 187,274 187,110 Lab/QC/QA 134,668 257,626 837,285 843,141 Utilities 688,636 1,361,662 10,753,630 5,362,674 Waste Trtmt/Disp 194,145 194,145 305,517 662,462 Annual Operational Costs 4,286,939 7,221,238 47,939,983 40,041,122

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$0.20 ) 4 Pressurized Water Scrubbing

(a) $1.00

biogas)

3 3 $0.15 $0.75

$0.10 $0.50

$0.05 $0.25

* Annual OperatingCost ($/Nm $0.00 $0.00 Annual OperatingCost ($/Nm

$1.00 Compression to CNG (b) $1.00

Pressurized Water Scrubbing biogas)

$0.80 3

$0.75 $0.60

$0.50 $0.40

$0.25

$0.20 * Annual Operating Cost ($/GGE CNG) $0.00 $0.00 Annual OperatingCost ($/Nm

Continued

Figure 7.1: Annual operating costs for (a) pressurized water scrubbing; b) bio-CNG; c) thermochemical conversion of biogas to methanol; and d) biological conversion of biogas to

methanol. *=operating cost based on biogas feed stream (secondary axis).

213

Figure 7.1: Continued

$2.50 Energy Recovery (c) * Methanol Purification $1.00

Methanol Production biogas) $2.00 3 Steam Methane Reforming Pressurized Water Scrubbing $0.75 $1.50

$0.50 $1.00

$0.50 $0.25 Annual OperatingCost ($/Nm Annual OperatingCost ($/kg methanol) $0.00 $0.00

$2.50 Energy Recovery (d) $1.00

Methanol Purification biogas)

$2.00 Biogas to Methanol Conversion * 3 Biomass Production $0.75 $1.50

$0.50 $1.00

$0.50 $0.25 Annual Annual Cost($/Nm Operating Annual OperatingCost ($/kg methanol) $0.00 $0.00

214

Total Electricity= $1.76 million/year (25,158,716 kWh/year) Energy Recovery 0.10% Pressurized Water Scrubbing 26.81%

Methanol Prod. 72.97% Steam Methane Reform. 0.11%

Total Heat Transfer Agents= $8.99 million/year Pressurized (18,263,007 MT/year) Water Methanol Scrubbing Purif. 1.39% 3.85% Steam Methane Reform. 11.47%

Methanol Prod. 83.28% (a)

Continued

Figure 7.2: Breakdown of utility costs for thermochemical (a) and biological (b) conversion

of biogas to methanol

215

Figure 7.2: Continued

Total Electricity= $1.66 million/year (23,755,925 kWh/year) Energy Recovery 0.07% Methanol Purif. 18.09% Biomass Prod. 40.85% Biogas to Methanol Conv. 40.98%

Total Heat Transfer Agents= $3.70 million/year (20,000,330 MT/year) Biogas to Biomass Methanol Prod. Conv. 9.20% 7.08%

Energy Recovery Methanol 0.00% Purif. 83.71%

(b)

216

Methane Content in Biogas (a) Fluid Moving Equip. Efficiency

Absorber Recycle Ratio

Absorber and Stripper Diameter

Absorber Pressure

Labor Price

Electricity Price

Absorber Water Flow Rate

Increase Heat Exchanger Efficiency Decrease

-1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00

Relative Change in Net Annual Operating Cost

Continued

Figure 7.3: Sensitivity analysis of operating costs for (a) pressurized water scrubbing; (b) bio-

CNG; (c) thermochemical conversion of biogas to methanol; (d) biological conversion of biogas to methanol (includes centrifuge recycle rate); and (e) biological conversion of biogas

to methanol (no centrifuge recycle rate). Figures only show parameters that caused an

average relative change of 0.025 or higher

217

Figure 7.3: Continued

Methane Content in Biogas (b)

Fluid Moving Equip. Efficiency

Labor Price

Absorber Recycle Ratio

Absorber and Stripper Diameter

Electricity Price

CNG exit pressure

Absorber Water Flow Rate

Chilled Water Price Increase Absorber Pressure Decrease

-1.00 -0.50 0.00 0.50 1.00 1.50

Relative Change in Net Annual Operating Cost

Continued

218

Figure 7.3: Continued

Methane Content in Biogas Syngas Recycle Rate (c) Methane to Syngas Conversion Methanol Prod. Reactor Cap. Cost Methanol Prod. React. Temp Syngas to Methanol Conversion Steam Meth. Reformer Cap Cost Heat Exchanger Efficiency Labor Price Fluid Moving Equip. Efficiency Chilled Water Price Steam Meth. Reformer Temperature HP Steam Price Methanol Prod. React. Pressure Electricity Price Absorber Recycle Ratio Increase Absorber and Stripper Diameter Decrease

-1.50 -1.00 -0.50 0.00 0.50 1.00 1.50

Relative Change in Net Annual Operating Cost

Continued

219

Figure 7.3: Continued

Centrifuge Recycle Rate Methanol Tolerance (d) Methane Content in Biogas Methane and Formate Conversion Methane Oxidation Rate Liquid Holdup TBR Cap. Cost Electricity Price Formate Price Labor Price AMS flow rate Relative Volatility Recycle Ratio Fluid Moving Equp. Efficiency Cent. Micron Size LP Steam Price Cent. Rec. Biomass Comp Phosphate Price Sterilizer Temperature Reactor Temp Cent. Sed. Efficiency Increase R/Rmin Decrease Cooling Water Price -10.00 -5.00 0.00 5.00 10.00 Relative Change in Net Annual Operating Cost

Continued

220

Figure 7.3: Continued

Methanol Tolerance Methane Content in Biogas (e) Methane and Formate Conversion Methane Oxidation Rate Liquid Holdup TBR Cap. Cost Electricity Price Formate Price Labor Price AMS flow rate Relative Volatility Recycle Ratio Fluid Moving Equp. Efficiency Cent. Micron Size LP Steam Price Cent. Rec. Biomass Comp Phosphate Price Sterilizer Temperature Reactor Temp Cent. Sed. Efficiency Increase R/Rmin Decrease Cooling Water Price -1.500 -1.000 -0.500 0.000 0.500 1.000 1.500

Relative Change in Net Annual Operating Cost

221

Chapter 8: Conclusions and Suggestions for Future Research

8.1. Conclusions

Solid-state anaerobic digestion (SS-AD) is a promising technology to convert energy crops to renewable biogas. Experimental studies from this research indicated that SS-AD of switchgrass was unaffected by minimal air exposure, and biogas production was improved when SS-AD was operated under thermophilic conditions. Net energy analysis suggested that positive net energy could be obtained from a ‘garage-type’ SS-AD digester operating in a temperate climate under different total solids contents and temperatures. The results from this work are useful to guide optimization and scale-up efforts for the SS-AD of energy crops.

Biogas has been slow to develop as a renewable methane source, presumably because biogas is difficult to transport and store and because it contains impurities such as carbon dioxide.

Therefore, a biological process was developed with this research to directly convert biogas from commercial AD facilities to methanol using methanotrophs isolated from SS-AD.

For the first time, a methanotroph (Methylocaldum sp. 14B) was successfully isolated from solid-state anaerobic digestate. This methanotroph can convert biogas from a commercial anaerobic digester directly to methanol. Methanol production was possible using phosphate as a methanol dehydrogenase (MDH) inhibitor and formate as an electron donor.

The optimal methanol concentrations and productivities obtained in shake flasks were among the highest reported in the literature. 222

A trickle-bed reactor was developed to improve gas-to-liquid mass transfer and enhance methane oxidation by the methanotrophs. Abiotic transport of oxygen to water and gas oxidation kinetics were enhanced when a packed bed of ceramic balls was included in the trickle bed reactor. Methane oxidation was highest at the highest biogas:air ratio, likely because it increased dissolved methane concentrations. Additionally, methanol production was achieved under non-sterile conditions using formate as an electron donor and phosphate as a MDH inhibitor. Operation under non-sterile conditions impacted the microbial community of the trickle-bed reactor, and the methanotroph from the inoculum

(Methylocaldum sp. 14B) was observed throughout the study.

A mathematical model that considered gas-to-liquid mass transport and gas uptake kinetics by methanotrophs was utilized to analyze biological conversion of biogas to methanol in a trickle-bed bioreactor. Using parameters and variables from literature and laboratory data from this research, the model was adapted to generate results that were comparable to select data from experimental studies using the lab-scale trickle bed bioreactor. Model predictions for a theoretical large scale trickle-bed bioreactor suggested that high gas velocities, elevated pressure, reactor packing with high specific surface area, and high densities of methanotroph cells with enhanced methanol tolerance are needed to improve methanol production rates.

Techno-economic analysis showed that conventional biogas upgrading technologies, such as purification of biogas for natural gas grid injection or compressed natural gas, may be economically feasible at a large-scale biogas production facility. Biological conversion of biogas to methanol had lower capital and operational costs compared to thermochemical conversion of biogas to methanol. However, the rate of methanol production from

223

thermochemical conversion was higher than biological conversion. Therefore, the cost per kilogram of methanol produced via thermochemical conversion was slightly lower than biological conversion. The costs for biological conversion could be lowered if methanotrophs are engineered to have higher gas oxidation rate, better substrate conversion ratio, and increased tolerance to methanol. Finally, the cost of formate as an electron donor must be reduced or cheap alternative electron donors are needed before biological conversion of biogas to methanol can be scaled up.

8.2. Suggestions for Future Research

Results from this work showed that an integrated process to convert switchgrass to methanol via solid-state anaerobic digestion and biological conversion of biogas to methanol is technically feasible, and could be commercially viable if certain limitations are overcome.

First, the methane production rate from SS-AD needs to be improved. Co-digestion of energy crops with feedstocks that have higher protein contents can enhance biogas production via

SS-AD. However, SS-AD of feedstocks with high protein content will likely generate biogas with higher hydrogen sulfide content. Therefore, future studies should evaluate the impacts of limited air exposure on the in-situ removal of hydrogen sulfide in co-digestion reactors.

The AD process produces both methane and volatile fatty acids, indicating that facultative methanotrophs that can use both methane and acetate as substrates could be present in anaerobic digestate. Facultative methanotrophs isolated from AD could then be modified to use acetate in wastewater as an electron donor for the conversion of methane to methanol, which could drastically reduce costs. Novel methanotrophs that can generate high value products, such as exopolysaccharides or polyhydroxybutyrate, could also be present in

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AD systems. Therefore, continued research on the isolation of methanotrophs from AD could help improve the technical and economic feasibility of biological biogas upgrading technologies.

The trickle-bed bioreactor can enhance gas-to-liquid mass transport and methane oxidation by methanotrophs. Future work should evaluate the impacts of different packing materials with high specific surface area and high porosity to optimize mass transfer and improve cell attachment, because higher cell densities are needed to improve methane oxidation rates. Engineered methanotrophic consortia could also be used as inocula for trickle-bed reactors to help stabilize the microbial community, improve methane oxidation rates, and reduce sterilization costs. Further modeling research should build on the current work and integrate reaction kinetics, mass transport, heat transfer, and computational fluid dynamics to improve the understanding of trickle bed reactor phenomena and guide scale-up efforts.

Results from the present study and the literature suggest that genetic modification of methanotrophs is needed to increase methane oxidation rates, carbon conversion efficiencies, and methanol tolerance before biological methane-to-liquid technologies can become an economical alternative to conventional biogas upgrading technologies. In the short term, techno-economic analysis can be used to determine the biological methane uptake rates that are necessary to make biological biogas upgrading processes more competitive. Finally, life cycle assessment should be used to compare the environmental impacts of different biogas upgrading technologies and assess their long-term sustainability.

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246

Appendix A: Supplemental Data for Chapter 5

247

Table A.1: Summary of sampling data and sequence clustering for microbial community analyses

Samples Controls Phase Phase Phase Phase Phase Phase Phase Phase 1.1-1 1.1-2 1.2-1 1.2-2 2.2-1 2.2-2 1-inoc-2 2.1-1 Day sampled 20 20 28a 28b 20 20 16 5 w/in Phase

Replicate # 1 2 1 2 1 2 1 1

OD 0.3 0.3 0.2 0.5 0.2 0.2 0.3 0.4

248 # of sequences 42164 33840 39855 39235 39695 36811 43317 33821

# of sequences 38731 31190 36945 36488 36949 34465 40433 31327 assigned to OTU After removal 36602 29294 35150 34496 35931 33652 38686 29910 of 0.005% After chimera 33148 26285 29912 29831 35141 32928 34107 28671 filtration a. sampled at beginning of day b. Sampled at end of day

Table A.2: Relative abundance of major bacterial OTUs in Phase 2.1 samples

Abundance (%) Taxonomic Phase Phase Bacterial taxa rank 1-inoc-2 2.1-1 Proteobacteria Phylum 62.59 79.67 Methylococcales Order 4.10 53.65 Burkholderiales Order 11.10 1.51 Rhizobiales Order 22.65 4.69 Rickettsiales Order 0.00 6.24 Rhodospirillales Order 7.60 2.52 Caulobacterales Order 5.00 0.17 Sphingomonadales Order 11.77 1.00 Pseudomonadales Order 0.00 6.16 Other Proteobacteria - 0.37 3.73 Bacteroidetes Phylum 24.02 3.54 Saprospirales Order 24.02 3.54 Cyanobacteria Phylum 0.95 12.71 Gemmatimonadetes Phylum 6.50 0.55 Chloroflexi Phylum 0.22 0.00 Firmicutes Phylum 0.02 0.03 Actinobacteria Phylum 0.34 0.09 Other bacterial phyla Phylum 0.62 0.23 Unassigned Phylum 4.74 3.18

249

Table A.3: Comparative relative abundance of bacterial taxa in TBR samples

Mean abundance (%)a Tukey Bacterial taxa Taxonomic Phase Phase Phase HSD test rank 1.1 1.2 2.2 (α = 0.05)b,c,d Proteobacteria Phylum 70.88±0.72 85.93±0.26 84.98±1.19 P1.1

Saprospirales Order 8.22±0.60 0.24±0.09 3.50±0.62 P1.20.05) a. data contains AVG±STDEV of replicates shown in Fig. 5 b. items separated by commas are not significantly different c. items separated by commas in ascending order of mean values d. items separated by less than symbol “<” indicate significant differences. For example, P2.2, P1.2< P1.1 means that samples from Phase 2.2 were similar to Phase 1.2 and both were significantly less than Phase 1.1.

30% 1.0 Methane-initial Methane-final OD Methanol 0.8

20%

0.6

content content (%) 4 0.4 10%

0.2

OD and methanol (g/L) methanol and OD Headspace CH Headspace

0% 0.0 0 5 10 15 20 Time (d)

Figure A.1: TBR performance during days 0-17 of Phase 2

251

Appendix B: Parameters and Variables for TBR Models

252

Table B.1: Parameters and variables used for lab scale TBR model verification

Symbol Parameter Value Unit Reference

Reactor

TBR packed bed Sheets et al. H 0.508 m R height (2017) Sheets et al. D Diameter 0.0508 m R (2017) Entrance Sheets et al. A 0.002 m2 R area (2017) Sheets et al. T Temperature 37 °C R (2017) Sheets et al. P Pressure 1 atm R (2017) Packed bed (from (Sheets et al., 2017)) Packing Sheets et al. d 0.00481 m P diameter (2017) Specific Geankopolis a 1247 (=6/d ) m2/m3 surface area P (2003) Apparent Sheets et al. ε 0.2428 m3/m3 reactor void space (2017) Total reactor Sheets et al. V 1.24 L R volume (2017) Sheets et al. VG Total gas volume 1.17 L (2017) per pass gas τ Equation 14 h This study retention time Flow properties Initial mixed gas composition 20.85% CH4, 14.38% O2, 10.62% CO2, 54.15% N2 (biogas:air=1:2.5) Initial mixed gas composition 9.88% CH4, 17.03% O2, 6.93% CO2, 66.16% N2 (biogas/air=1:6.0) Inlet gas Sheets et al. Q 80 mL/min Gin flow rate (2017) Inlet liquid Sheets et al. Q 50 mL/min Lin flow rate (2017)

Inlet gas u 6.58×10-4 m/s This study Gin velocity

Continued

253

Table B.1: Continued

Gas ρ u =u * gin (Eq. 4) uG velocity across G Gin ρ m/s This study TBR g Inlet gas ρ 1.092a, 1.124b kg/m3 This study Gin density Inlet gas μ 1.721×10-5 a, 1.797×10-5 b Pa*s Davidson (1993) Gin viscosity Liquid u 4.11×10-4 m/s This study L velocity Liquid Green and Perry ρ 994 kg/m3 L density (2008) Liquid Green and Perry μ 6.91×10-4 Pa*s L viscosity (2008)

Liquid Reynolds ρL*dp*uL Geankopolis ReL =0.47 - number μL (2003)

Gas Reynolds ρGin*dp*uGin a b Geankopolis ReG =0.201 , 0.198 - number μGin (2003) Mass transfer and gas equilibrium Mass transfer Sheets et al. K a 3.14c 1/h L coefficient (2017) Enhancement E Eq. 10 - Merchuk (1977) F factor Gas phase Marrero and D diffusion 2×10-5 m2/s i,G Mason (1972) coefficient Liquid phase Frank et al. -9 2 Di,L diffusion 2×10 m /s (1996); Han and coefficient Bartels (1996) Henry’s Law 3 HCH4, 25°C coefficient for CH4 71428 Pa*m /mol Sander (2015) at 25°C Henry’s Law 3 HO2, 25°C coefficient for O2 76923 Pa*m /mol Sander (2015) at 25°C Henry’s Law 3 HCO2, 25°C coefficient for CO2 3030 Pa*m /mol Sander (2015) at 25°C

∆solH Heat of solution , CH4 1600 K Sander (2015) R constant for CH4

Continued

254

Table B.1: Continued

∆solH Heat of solution , O2 1500 K Sander (2015) R constant for O2

∆solH Heat of solution , CO2 2400 K Sander (2015) R constant for CO2

Biochemical reaction terms

Sheets et al. X Cell density 0.14 kg DCW/m3 (2017) Lawton and Specific mmol CH /g SA 9.0 4 Rosenzweig MMO MMO activity DCW/h (2016) Lawton and half saturation K 8.3 μM Rosenzweig CH4 constant for CH 4 (2016) half saturation Sipkema et al. KO2 2.0 μM constant for O2 (1998) Methanol Sheets et al. K 9.375 mol/m3 CH3OH inhibition term (2017)

O2 uptake to γO /CH 1.3 - This study 2 4 CH4 uptake ratio a. properties of gas at biogas:air ratio of 1:2.5 b. properties of gas at biogas:air ratio of 1:6.0 c. -1 calculated as measured abiotic KLa*VLact/VLmeas where KLa was the measured KLa (9.55 h ) VLact was the average liquid volume in the TBR packed bed during methanol production experiments (30 mL) and VLmeas was the liquid volume during abiotic KLa testing (90 mL)(Sheets et al., 2017).

255

Table B.2: Parameters and variables used for large scale TBR model

Symbol Parameter Value Unit Reference

Reactor

TBR H 20 m This study R height

DR Diameter 2 m This study

Entrance A 3.14 m2 This study R area

TR Temperature 37 °C This study

PR Pressure 1, 2, 3 atm This study

Packed bed (porous ceramic spheres from Kim and Deshusses, 2008a; Kim and Deshusses, 2008b) Kim and Packing d 0.004 m Deshusses (2008a, P diameter 2008b) Kim and Specific a 2500 m2/m3 Deshusses (2008a, surface area 2008b) Kim and Apparent ε 0.38 m3/m3 reactor Deshusses (2008a, void space 2008b)

Flow properties

Inlet gas uGin 100, 300, 500 m/h This study velocity

Gas ρ u =u * gin (Eq. 4) uG velocity across G Gin ρ m/h This study TBR g Inlet gas ρ variable kg/m3 This study Gin density

∑j μi*xi*√Mi uGin Inlet gas viscosity μG= Pa*s Davidson (1993) ∑j xi*√Mi Liquid u 5 m/h This study L velocity Liquid Green and Perry ρ 994 kg/m3 L density (2008) Liquid Green and Perry μ 6.91×10-4 Pa*s L viscosity (2008)

Continued

256

Tab le B.2: Continued Gas Q u *A m3/h This study G flow rate G R Liquid Q u *A m3/h This study L flow rate L R

Liquid Reynolds ρL*dp*uL Geankopolis ReL =0.8 - number μL (2003)

Gas Reynolds ρG*dp*uG Geankopolis ReG =0-24 - number μG (2003)

Mass transfer and gas equilibrium Kim and Mass transfer K a 122 (Eq. 8) 1/h Deshusses (2008a, L coefficient 2008b) Enhancement E Eq. 10 - Merchuk (1977) F factor Gas phase Marrero and D diffusion 2×10-5 m2/s i,G Mason (1972) coefficient Liquid phase Frank et al. -9 2 Di,G diffusion 2×10 m /s (1996); Han and coefficient Bartels (1996) Henry’s Law 3 HCH4, 25°C coefficient for CH4 71428 Pa*m /mol Sander (2015) at 25°C Henry’s Law 3 HO2, 25°C coefficient for O2 76923 Pa*m /mol Sander (2015) at 25°C Henry’s Law 3 HCO2, 25°C coefficient for CO2 3030 Pa*m /mol Sander (2015) at 25°C

∆solH Heat of solution , CH4 1600 K Sander (2015) R constant for CH4

∆solH Heat of solution , O2 1500 K Sander (2015) R constant for O2

∆solH Heat of solution , CO2 2400 K Sander (2015) R constant for CO2 Biochemical reaction terms

X Cell density 1, 5, 10, 20, 40 kg DCW/m3 This study Lawton and Specific mmol CH /g SA 9.0 4 Rosenzweig MMO MMO activity DCW/h (2016)

Continued

257

Table B.2: Continued Lawton and half saturation K 8.3 μM Rosenzweig M constant for CH 4 (2016) half saturation Sipkema et al. KO 2.0 μM constant for O2 (1998) Methanol Best and Higgins K 937.5 mol/m3 CH3OH inhibition term (1981)

O2 uptake to Petersen et al. γO /CH 1.3 - 2 4 CH4 uptake ratio (2016) Bold values were primary operational factors that were adjusted

258

Appendix C: Process Flow Data for Pressurized Water Scrubbing

259

Figure C.1: Process model for biogas cleaning via pressurized water scrubbing (PWS)

Table C.1: Parameter values for sensitivity analysis of biogas cleaning via PWS

Parameter Unit Low Value Base Value High Value Methane Content in % 55 65 75 Biogas Fluid Moving % 56 70 84 Equip. Efficiency Absorber Recycle % 85 90 95 Ratio Absorber and m 2.24 2.80 3.36 Stripper Diameter

Absorber Pressure atm 8 10 12

260 $/h, Labor Price 24 30 36

base price

Electricity Price $/kWh 0.056 0.07 0.084

Absorber Water m3/h 12 15 18 Flow Rate Heat Exchanger % 80 90 100 Efficiency *only contains values that caused an average relative change in operational costs of 0.025 or higher

Table C.2: Streams report for biogas cleaning via PWS

Streams Purified Rec Water- Materials Air-S Biogas Emit-1 S-101 S-102 S-104 S-115 S-125 CH4 PWS Total Flow MT/h 1.06 5.75 4.31 2.50 134.14 0.18 0.18 1.06 152.50 149.06 Temperature °C 25.00 37.00 19.30 20.20 19.30 20.00 20.00 40.00 20.20 19.80 Pressure atm 1.00 1.00 1.99 10.00 2.00 2.00 1.00 1.99 10.00 1.00 Total Enthalpy kW-h/h 7.46 298.47 225.73 40.07 3019.39 8.16 6.96 11.94 3744.66 3454.81

Total Contents kmol/h 36.79 222.53 110.69 148.63 7446.12 8.06 7.97 36.79 8356.22 8274.35 Carb. Dioxide 0.00 77.77 73.79 3.99 0.00 1.94 1.94 0.00 75.73 0.00 Carbon Monoxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

261 Hydr. Sulfide 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.11 0.00

Hydrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methane 0.00 144.64 0.00 144.64 0.00 6.03 6.03 0.00 6.03 0.00 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nitrogen 29.06 0.00 29.06 0.00 0.00 0.00 0.00 29.06 0.00 0.00 Oxygen 7.72 0.00 7.72 0.00 0.00 0.00 0.00 7.72 0.00 0.00 Sulfur Dioxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water 0.00 0.00 0.00 0.00 7446.12 0.09 0.00 0.00 8274.35 8274.35 Continued

Table C.2: Continued

Streams Water- Water- Materials S-126 S-129 S-132 S-133 WAT-1 to- WW-1 PWS Stripper Total Flow MT/h 149.06 149.05 5.93 5.93 0.00 14.92 152.30 14.90 Temperature °C 19.90 19.30 36.40 40.00 20.00 25.00 20.00 19.30 Pressure atm 10.00 2.00 1.00 10.00 1.00 1.00 2.00 2.00 Total Enthalpy kW-h/h 3471.04 3354.88 305.43 313.86 0.04 435.41 3568.61 335.49

Total Contents kmol/h 8274.35 8273.47 230.50 230.50 0.09 828.23 8347.37 827.35 Carb. Dioxide 0.00 0.00 79.72 79.72 0.00 0.00 73.79 0.00

262 Carbon Monoxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Hydr. Sulfide 0.00 0.00 0.11 0.11 0.00 0.00 0.11 0.00 Hydrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methane 0.00 0.00 150.67 150.67 0.00 0.00 0.00 0.00 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nitrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur Dioxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water 8274.35 8273.46 0.00 0.00 0.09 828.23 8273.46 827.35

Table C.3: Equipment purchase costs for biogas cleaning via PWS

Equipment Purchase Costs Procedure Name Equipment Size Unit Rate ($) G-101 Biogas Compressor 715.4 kW 853,000 C-101 Absorption Tower 216.74 m3 336,000 C-103 Stripper 44.79 m3 120,000 PM-102 Water Pump 54.13 kW 101,000 G-102 Air Compressor 26.92 kW 73,000 V-101 Flash Drum 23.27 m3 49,000 HX-101 Condenser 0.12 m2 34,000 cond. area Section 1,566,000 Total - Unlisted Equip - 392,000

Total Equipment Purchase Cost 1,958,000

263

Table C.4: Breakdown of utility costs for biogas cleaning via PWS

HP Chilled % of Power LP Steam Cooling Water Natural Gas Total Steam Water Section Section: Pressurized Water Scrubbing G-101- 396,618.07 0 0 34,214.25 0 0 430,832.32 62.56% Biogas Compress. PM-102- Water 30,007.86 0 0 0 0 0 30,007.86 4.36% Pump V-101- Flash 0 0 0 0 101,047.32 0 101,047.32 14.67% Unit 264 G-102-

Air 14,925.79 0 0 736.74 0 0 15,662.53 2.27% Compress. HX-101- 0 0 0 0 698.51 0 698.51 0.10% Condens.

Unlisted 27,596.98 0 0 0 0 0 27,596.98 4.01%

General Load 82,790.95 0 0 0 0 0 82,790.95 12.02%

Section Total 551,939.65 0 0 34,950.99 101,745.83 0 688,636.47 100.00%

Process Total 551,939.65 0 0 34,950.99 101,745.83 0 688,636.47

Appendix D: Process Flow Data for Bio-Compressed Natural Gas

265

Figure D.1: Process model for biogas upgrading to Bio-CNG

Table D.1: Parameter values for sensitivity analysis of biogas upgrading to Bio-CNG

Parameter Unit Low Value Base Value High Value Methane Content in % 55 65 75 Biogas Fluid Moving % 56 70 84 Equip. Efficiency $/h, Labor Price 24 30 36 base price Absorber Recycle % 85 90 95 Ratio Absorber and m 2.24 2.80 3.36 Stripper Diameter

2 66 Electricity Price $/kWh 0.056 0.07 0.084

CNG exit pressure atm 160 200 240

Absorber Water m3/h 12 15 18 Flow Rate

Chilled Water Price $/MT 8 10 12

Absorber Pressure atm 8 10 12

*only contains values that caused an average relative change in operational costs of 0.025 or higher

Table D.2: Streams report for biogas upgrading to Bio-CNG

Streams Purified Rec Water- Materials Air-S Bio-CNG Biogas Emit-1 S-101 S-102 S-104 S-105 CH4 PWS Total Flow MT/h 1.06 2.50 5.75 4.31 2.50 134.14 0.18 0.18 1.06 2.50 Temperature °C 25.00 25.00 37.00 19.30 20.20 19.30 20.00 20.00 40.00 25.00 Pressure atm 1.00 210.00 1.00 1.99 10.00 2.00 2.00 1.00 1.99 210.00 Total Enthalpy kW-h/h 7.46 47.10 298.47 225.73 40.07 3019.39 8.16 6.96 11.94 47.10

Total Contents kmol/h 36.79 148.63 222.53 110.69 148.63 7446.12 8.06 7.97 36.79 148.63 Carb. Dioxide 0.00 3.99 77.77 73.79 3.99 0.00 1.94 1.94 0.00 3.99 Carbon Monoxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

267 Hydr. Sulfide 0.00 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 Hydrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Methane 0.00 144.64 144.64 0.00 144.64 0.00 6.03 6.03 0.00 144.64 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nitrogen 29.06 0.00 0.00 29.06 0.00 0.00 0.00 0.00 29.06 0.00 Oxygen 7.72 0.00 0.00 7.72 0.00 0.00 0.00 0.00 7.72 0.00 Sulfur Dioxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water 0.00 0.00 0.00 0.00 0.00 7446.12 0.09 0.00 0.00 0.00 Continued

Table D.2: Continued

Streams Water- Water- Materials S-115 S-125 S-126 S-129 S-132 S-133 WAT-1 to- WW-1 PWS Stripper Total Flow MT/h 152.50 149.06 149.06 149.05 5.93 5.93 0.00 14.92 152.30 14.90 Temperature °C 20.20 19.80 19.90 19.30 36.40 40.00 20.00 25.00 20.00 19.30 Pressure atm 10.00 1.00 10.00 2.00 1.00 10.00 1.00 1.00 2.00 2.00 Total Enthalpy kW-h/h 3744.66 3454.81 3471.04 3354.88 305.43 313.86 0.04 435.41 3568.61 335.49

Total Contents kmol/h 8356.22 8274.35 8274.35 8273.47 230.50 230.50 0.09 828.23 8347.37 827.35 Carb. Dioxide 75.73 0.00 0.00 0.00 79.72 79.72 0.00 0.00 73.79 0.00 Carbon Monoxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

268 Hydr. Sulfide 0.11 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.11 0.00 Hydrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methane 6.03 0.00 0.00 0.00 150.67 150.67 0.00 0.00 0.00 0.00 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nitrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur Dioxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water 8274.35 8274.35 8274.35 8273.46 0.00 0.00 0.09 828.23 8273.46 827.35

Table D.3: Equipment purchase costs for biogas upgrading to Bio-CNG

Equipment Purchase Costs Procedure Name Equipment Size Unit Rate ($) Section: Biogas Cleaning G-101 Biogas Compressor 715.4 kW 853,000 C-101 Absorption Tower 216.74 m3 336,000 C-103 Stripper 44.79 m3 120,000 PM-102 Water Pump 54.13 kW 101,000 G-102 Air Compressor 26.92 kW 73,000 V-101 Flash Drum 23.27 m3 49,000 HX-101 Condenser 0.12 m2 34,000 cond. area Section Total 1,566,000 Section: Bio-CNG Compression G-103 CNG Compressor 635.18 kW 764,000 V-102 CNG Storage 1560 m3 142,000 capacity Section Total 906,000

- Unlisted Equip - 618,000

Total Equipment Purchase Cost 3,090,000

269

Table D.4: Breakdown of utility costs for biogas upgrading to Bio-CNG

Air 14,925.79 0 0 736.74 0 0 15,662.53 2.27% Compress. HX-101- 0 0 0 0 698.51 0 698.51 0.10% Condens. Unlisted 27,596.98 0 0 0 0 0 27,596.98 4.01% General Load 82,790.95 0 0 0 0 0 82,790.95 12.02% Section Total 551,939.65 0 0 34,950.99 101,745.83 0 688,636.47 100.00%

Section: Compression to CNG G-103- 352,143.36 0 0 0 232,846.83 0 584,990.19 86.92% CNG Compress. Unlisted 22,008.96 0 0 0 0 0 22,008.96 3.27% General Load 66,026.88 0 0 0 0 0 66,026.88 9.81% Section Total 440,179.20 0 0 0 232,846.83 0 673,026.03 100.00% Process Total 992,118.85 0.00 0.00 34,950.99 334,592.66 0.00 1,361,662.50

Appendix E: Process Flow Data for Thermochemical Conversion of Biogas to Methanol

271

Figure E.1: Process model for thermochemical conversion of biogas to methanol

Table E.1: Parameter values for sensitivity analysis of thermochemical conversion of biogas to methanol

Parameter Unit Low Value Base Value High Value

Methane Content in % 55 65 75 Biogas Syngas Recycle % 92 95 98 Ratea Methane to Syngas % 85 90 95 Conversion Methanol Prod. $M 6.32 7.90 9.48 Reactor Cap. Cost Methanol Prod.

272 °C 200 250 300 React. Temp

Syngas to Methanol % 20 25 30 Conversion Steam Meth. $M 3.6 4.5 5.4 Reformer Cap Cost Heat Exchanger % 80 90 100 Efficiency $/h, Labor Price 24 30 36 base price Fluid Moving % 56 70 84 Equip. Efficiency

Chilled Water Price $/MT 8 10 12

Continued

Table E.1: Continued Steam Meth. Reformer °C 800 850 875 Temperature

HP Steam Price $/MT 16 20 24

Methanol Prod atm 80 100 120 React. Pressure

Electricity Price $/kWh 0.056 0.07 0.084

Absorber Recycle % 85 90 95 Ratio

273 Absorber and m 2.24 2.80 3.36 Stripper Diameter

a. syngas recycled from methanol production reactor (Rec Syngas in Figure E.1) *only contains values that caused an average relative change in operational costs of 0.025 or higher

Table E.2: Streams report for thermochemical conversion of biogas to methanol

Streams Materials Air-S Air-SG Air-SG2 Ash-1 Ash-3 Biogas Biogas-SG Cr. Methanol Emit-1 Emit-2 Total Flow MT/h 1.06 7.26 15.42 0.00 0.00 4.83 1.00 3.62 3.79 8.26 Temperature °C 25.00 25.00 25.00 250.00 250.00 37.00 37.00 20.00 19.40 200.00 Pressure atm 1.00 1.00 1.00 1.00 1.00 1.00 1.00 98.69 1.99 1.00 Total kW-h/h 7.46 51.04 108.44 0.00 0.00 250.86 51.97 57.94 190.80 1258.36 Enthalpy

Total kmol/h 36.79 251.60 534.59 0.00 0.00 187.03 38.75 120.17 98.92 290.21 Contents Carb. 0.00 0.00 0.00 0.00 0.00 65.37 13.54 5.85 62.03 38.77 Dioxide

274 Carbon 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Monoxide

Hydr. 0.00 0.00 0.00 0.00 0.00 0.09 0.02 0.00 0.09 0.00 Sulfide Hydrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methane 0.00 0.00 0.00 0.00 0.00 121.57 25.19 0.00 0.00 0.00 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 92.91 0.00 0.00 Nitrogen 29.06 198.78 422.34 0.00 0.00 0.00 0.00 0.00 29.06 198.78 Oxygen 7.72 52.83 112.24 0.00 0.00 0.00 0.00 0.00 7.72 2.52 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 Dioxide Water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 21.42 0.00 50.12

Continued

Table E.2: Continued

Streams LP- LP- Rec Purified Raw Rec- Materials Emit-3 Steam- Steam- METHANOL Water- S-101 S-102 CH Syngas Syngas 1 2 4 PWS Total Flow MT/h 16.71 10.00 16.44 2.88 2.10 8.69 134.14 14.39 0.14 0.14 Temperature °C 200.00 152.30 152.30 25.00 20.30 850.00 19.40 240.00 20.00 20.00 Pressure atm 1.00 5.00 5.00 1.00 10.00 5.00 2.00 98.69 2.00 1.00 Total kW-h/h 3479.51 7900.88 12552.72 50.93 33.71 8878.45 3038.22 6326.93 5.84 4.88 Enthalpy

Total kmol/h 639.66 555.09 912.60 89.89 124.91 709.74 7446.29 3085.70 6.45 6.37 Contents Carb. 275 34.17 0.00 0.00 0.00 3.33 3.33 0.00 83.04 1.31 1.31 Dioxide

Carbon 0.00 0.00 0.00 0.00 0.00 109.42 0.00 176.64 0.00 0.00 Monoxide Hydr. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfide Hydrogen 0.00 0.00 0.00 0.00 0.00 328.24 0.00 2822.87 0.00 0.00 Methane 0.00 0.00 0.00 0.00 121.57 12.16 0.00 0.00 5.07 5.07 Methanol 0.00 0.00 0.00 89.68 0.00 0.00 0.00 3.03 0.00 0.00 Nitrogen 422.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 5.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 177.80 555.09 912.60 0.21 0.00 256.59 7446.29 0.13 0.07 0.00

Continued

Table E.2: Continued

Streams Materials S-103 S-104 S-105 S-107 S-108 S-111 S-112 S-113 S-114 S-115 Total Flow MT/h 8.69 1.06 14.39 10.00 6.59 8.69 4.10 3.90 0.20 151.94 Temperature °C 100.00 40.00 21.50 25.00 25.00 100.00 20.00 20.00 20.00 20.30 Pressure atm 4.94 1.99 98.69 5.00 5.00 5.00 4.94 4.94 4.94 10.00 Total kW-h/h 5068.01 11.94 766.84 292.31 192.73 1269.87 109.73 80.67 2.34 3719.25 Enthalpy

Total kmol/h 490.91 36.79 3085.70 555.09 366.00 709.74 455.12 442.96 12.16 8343.02 Contents Carb. 3.33 0.00 83.04 0.00 0.00 3.33 3.17 3.17 0.00 63.34 Dioxide

276 Carbon 0.00 0.00 176.64 0.00 0.00 109.42 109.42 109.42 0.00 0.00 Monoxide Hydr. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 Sulfide Hydrogen 0.00 0.00 2822.87 0.00 0.00 328.24 328.24 328.24 0.00 0.00 Methane 121.57 0.00 0.00 0.00 0.00 12.16 12.16 0.00 12.16 5.07 Methanol 0.00 0.00 3.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nitrogen 0.00 29.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 7.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 366.00 0.00 0.13 555.09 366.00 256.59 2.13 2.13 0.00 8274.52

Continued

Table E.2: Continued

Streams Materials S-117 S-118 S-119 S-120 S-121 S-124 S-125 S-126 S-127 S-129 Total Flow MT/h 3.90 18.30 18.77 15.15 0.76 0.33 149.07 149.07 3.29 149.05 Temperature °C 250.00 241.20 250.00 20.00 21.50 40.00 19.90 20.00 40.00 19.40 Pressure atm 100.00 98.69 100.00 98.69 98.69 1.00 1.00 10.00 1.00 2.00 Total kW-h/h 937.21 7262.96 8724.12 807.20 40.36 48.84 3473.64 3489.88 100.01 3375.80 Enthalpy

Total kmol/h 442.96 3528.06 3368.28 3248.11 162.41 8.29 8274.52 8274.52 111.89 8273.65 Contents Carb. 3.17 86.21 93.26 87.41 4.37 5.85 0.00 0.00 0.00 0.00 Dioxide

277 Carbon 109.42 286.05 185.93 185.93 9.30 0.00 0.00 0.00 0.00 0.00 Monoxide Hydr. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfide Hydrogen 328.24 3150.52 2971.44 2971.44 148.57 0.00 0.00 0.00 0.00 0.00 Methane 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methanol 0.00 3.03 96.10 3.19 0.16 2.32 0.00 0.00 90.59 0.00 Nitrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 2.13 2.26 21.55 0.14 0.01 0.11 8274.52 8274.52 21.30 8273.65

Continued

Table E.2: Continued

Streams Materials S-130 S-132 S-133 S-137 Steam-in Waste Gases WAT-1 WAT-2 Water-PWS Water-SG Total Flow MT/h 2.88 4.97 4.97 16.44 6.59 1.29 0.00 4.59 14.92 6.59 Temperature °C 64.80 36.40 40.00 25.00 152.30 40.70 20.00 20.00 25.00 25.00 Pressure atm 1.00 1.00 10.00 5.00 5.00 1.00 1.00 4.94 1.00 1.00 Total kW-h/h 132.08 255.74 262.76 480.56 5034.30 91.54 0.03 107.30 435.41 192.41 Enthalpy

Total kmol/h 89.89 193.41 193.41 912.60 366.00 182.85 0.07 254.62 828.23 366.00 Contents Carb. 0.00 66.68 66.68 0.00 0.00 10.22 0.00 0.16 0.00 0.00 Dioxide

278 Carbon 0.00 0.00 0.00 0.00 0.00 9.30 0.00 0.00 0.00 0.00 Monoxide Hydr. 0.00 0.09 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfide Hydrogen 0.00 0.00 0.00 0.00 0.00 148.57 0.00 0.00 0.00 0.00 Methane 0.00 126.64 126.64 0.00 0.00 12.16 0.00 0.00 0.00 0.00 Methanol 89.68 0.00 0.00 0.00 0.00 2.48 0.00 0.00 0.00 0.00 Nitrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 0.21 0.00 0.00 912.60 366.00 0.12 0.07 254.46 828.23 366.00

Continued

Table E.2: Continued

Streams Materials Water-to-Stripper Wat-SG2 Wat-SR WW-1 WW-2 Total Flow MT/h 151.78 16.44 10.00 14.91 0.41 Temperature °C 20.00 25.00 25.00 19.40 98.50 Pressure atm 2.00 1.00 1.00 2.00 1.00 Total kW-h/h 3555.01 479.77 291.82 337.58 45.62 Enthalpy

Total kmol/h 8335.78 912.60 555.09 827.37 21.99 Contents Carb. 62.04 0.00 0.00 0.00 0.00 Dioxide

279 Carbon 0.00 0.00 0.00 0.00 0.00 Monoxide Hydr. 0.09 0.00 0.00 0.00 0.00 Sulfide Hydrogen 0.00 0.00 0.00 0.00 0.00 Methane 0.00 0.00 0.00 0.00 0.00 Methanol 0.00 0.00 0.00 0.00 0.91 Nitrogen 0.00 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 Dioxide Water 8273.65 912.60 555.09 827.37 21.09

Table E.3: Equipment purchase costs for thermochemical conversion of biogas to methanol

Equipment Purchase Costs Procedure Name Equipment Size Total Cost ($) Section: Biogas Cleaning G-101 Biogas Compressor 600.34 kW 725,000 C-101 Absorption Tower 216.74 m3 336,000 C-103 Stripper 44.79 m3 120,000 PM-102 Water Pump 54.13 kW 101,000 G-102 Air Compressor 26.92 kW 73,000 V-101 Flash Drum 23.27 m3 49,000 HX-101 Condenser 0.12 m2 34,000 cond. area Section Total 1,438,000 Section: Steam-CH4-Reforming 3 PFR-101 Steam-CH4 Reformer 40 m 7,184,000 SG-101 Steam 6.59 MT/h 124,000 Generation throughput PM-101 Water Pump- 1.07 kW 19,000 Steam Generation Section Total 7,327,000 Section: Syngas-to-Methanol Conversion PFR-102 Methanol Prod. Reactor 14 m3 8,506,000 G-103 Syngas Compressor 1852.12 kW 2,100,000 HX-102 (4 units @ $84,000/unit) Rec. Syngas Heater 97.56 m2 336,000 h.t. area CSP-101 CH4 Removal 4.10 MT/h 229,000 Membrane throughput HX-103 Steam Recovery 80.01 m2 74,000 Heat Exchanger h.t. area HX-105 (2 units @ $34,000/unit) Crude Methanol 62.53 m2 68,000 Condenser cond. area HX-104 Raw Syngas 32.48 m2 34,000 Condenser cond. area PM-104 Steam Recovery Pump 1.62 kW 23,000 Section Total 11,370,000 Section: Methanol Purification C-102 Methanol Distillation 4.36 m3 100,000 HX-106 Product Cooler 1.74 m2 11,000 h.t. area V-102 Flash Drum- 0.41 m3 4,000 Crude Methanol Section Total 115,000 Section: Energy Recovery SG-102 Steam Generation- 16.44 MT/h 248,000 Energy Recovery throughput PM-103 Water Pump- 2.66 kW 28,000 Energy Recovery Section Total 276,000 - Unlisted Equip - 5,132,000 Total Equipment Purchase Cost 25,658,000

280

Table E.4: Breakdown of utility costs for thermochemical conversion of biogas to methanol

LP HP Cooling Chilled Natural % of Power Total Steam Steam Water Water Gas Section Section: Pressurized Water Scrubbing G-101- 332,829.27 0 0 28,716.11 0 0 361,545.38 60.51% Biogas Compress. PM-102- 30,009.66 0 0 0 0 0 30,009.66 5.02% Water Pump V-101- 0 0 0 0 95,321.93 0 95,321.93 15.95% Flash Unit G-102- 14,925.79 0 0 736.74 0 0 15,662.53 2.62% Air Compress. HX-101- 281 0 0 0 0 559.39 0 559.39 0.09% Condens. Unlisted 23,610.29 - - - - - 23,610.29 3.95%

General Load 70,830.88 - - - - - 70,830.88 11.85%

Section Total 472,205.89 0 0 29,452.85 95,881.32 0 597,540.06 100.00%

Section: Steam-CH4 Reforming PM-101- 591.05 0 0 0 0 0 591.05 0.06% St.-Water Pump PFR-101- 997.92 0 0 0 0 1,031,861.17 1,032,859.09 99.90% SMR Reactor Unlisted 99.31 - - - - - 99.31 0.01% General Load 297.93 - - - - - 297.93 0.03% Section Total 1986.21 0 0 0 0 1,031,861.17 1,033,847.38 100.00%

Continued

Table E.4: Continued

Section: Methanol Prod. PM-104- 896.41 0 0 0 0 0 896.41 0.01% Heat Rec. Pump HX-104 0 0 0 0 634,933.07 0 634,933.07 7.24% Condens. G-103- Syngas 1,026,815.00 0 0 15,657.69 0 0 1,042,472.69 11.88% Compress. PFR-102- Methanol Prod. 349.27 0 0 92,601.33 0 0 92,950.60 1.06% Reactor HX-105- 0 0 0 0 4,739,503.22 0 4,739,503.22 54.02% Condens. HX-102- 0 0 2,006,489.41 0 0 0 2,006,489.41 22.87% 282 Syngas Heater

Unlisted 64,253.79 - - - - - 64,253.79 0.73%

General Load 192,761.38 - - - - - 192,761.38 2.20%

Section Total 1,285,075.85 0.00 2,006,489.41 108,259.02 5,374,436.29 0.00 8,774,260.57 100.00%

Section: Methanol Purification V-102- Product 0 16,400.02 0 0 0 0 16,400.02 4.74% Flash Unit C-102- 0 201,480.40 0 79,320.74 0 0 280,801.14 81.12% Distillation HX-106- 0 0 0 0 48,939.16 0 48,939.16 14.14% Product Cooler Section Total 0.00 217,880.42 0.00 79,320.74 48,939.16 0.00 346,140.32 100.00%

Continued

Table E.4: Continued Section: Energy Recovery PM-103- Steam- 1,473.73 0 0 0 0 0 1,473.73 80.00% Water Pump Unlisted 92.11 - - - - - 92.11 5.00% General Load 276.33 - - - - - 276.33 15.00% Section Total 1,842.17 0.00 0.00 0.00 0.00 0.00 1,842.17 100.00%

Process Total 1,761,110.12 217,880.42 2,006,489.41 217,032.61 5,519,256.77 1,031,861.17 10,753,630.50

Note: Unlisted equipment (5% of section power) and general load (15% of section power) make up residual power requirements

283

Appendix F: Process Flow Data for Biological Conversion of Biogas to Methanol

284

Figure F.1: Process model for biological conversion of biogas to methanol

Table F.1: Parameter values for sensitivity analysis of biological conversion of biogas to methanol

Parameter Unit Low Value Base Value High Value

Centrifuge Recycle % 94 95 96 Rate

Methanol Tolerance g/L 24 30 36

Methane Content in % 55 65 75 Biogas Methane and Formate % 77.5, 62 80.0, 64 82.5, 66 Conversiona Methane Oxidation

285 g/g cell/h 0.28 0.35 0.42 Rate

Liquid holdup % 24 30 36

TBR Cap Cost Term $ 10,400 13,000 15,600

Electricity Price $/kWh 0.06 0.07 0.08

Formate Price $/MT 160 200 240

$/h Labor Price 24 30 36 base price

AMS flow rate m3/h 32 40 48

Relative Volatility - 8 10 12

Continued

Table F.1 Continued

Recycle Ratiob % 40 50 60

Fluid Moving Equip. % 56 70 84 Efficiency Centrifuge Micron μm 1.60 2.00 2.40 Size

LP Steam Price $/MT 9.60 12.00 14.40

Centrifuge Recycled Biomass g/L 240 300 360 Composition

286 Phosphate Price $/MT 400 500 600

Sterilizer °C 130 140 150 Temperature

Reactor Temperature °C 35 37 39

Centrifuge Sedimentation. % 40 50 60 Efficiency

R/Rmin - 1.0 1.25 1.50

Cooling Water Price $/MT 0.04 0.05 0.06

a. CH4 and formate-to-methanol conversion both adjusted simultaneously b. water recycled from distillation unit *only contains values that caused an average relative change in operational costs of 0.025 or higher

Table F.2: Streams report for biological conversion of biogas to methanol

Streams Materials Air-B Air-M Air-S AMS Ash BG-B BG-M Biomass Crude Methanol Emission-BP Total MT/h 3.07 27.69 1.05 39.92 0.00 0.60 5.39 40.47 87.46 3.13 Flow Temperature °C 25.00 25.00 25.00 25.00 250.00 37.00 37.00 37.00 37.00 37.00 Pressure atm 1.00 1.00 1.00 1.00 1.00 1.00 1.00 5.00 2.00 5.00 Total kWh/h 21.62 194.65 7.40 1159.59 0.00 31.09 279.81 1739.96 3685.91 74.37 Enthalpy

Total kmol/h 106.58 959.63 36.50 2203.29 0.00 23.18 208.62 2235.88 4726.53 102.60 Contents Amm. Sulfate 0.00 0.00 0.00 1.51 0.00 0.00 0.00 0.73 1.47 0.00

287 Biomass 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.96 7.53 0.00 Carb. Dioxide 0.00 0.00 0.00 0.00 0.00 8.10 72.91 0.00 0.00 15.23

Formic Acid 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9.23 0.00 Hydr. Sulfide 0.00 0.00 0.00 0.00 0.00 0.01 0.10 0.00 0.00 0.01 Magne 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.16 0.32 0.00 Sulfate Methane 0.00 0.00 0.00 0.00 0.00 15.07 135.60 0.00 0.00 0.18 Methanol 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 71.75 0.00 Nitrogen 84.20 758.14 28.84 0.00 0.00 0.00 0.00 0.00 0.00 84.20 Oxygen 22.38 201.49 7.66 0.00 0.00 0.00 0.00 0.00 0.00 2.97 Phosphate 0.00 0.00 0.00 0.15 0.00 0.00 0.00 0.15 5.17 0.00 RO water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.86 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 0.00 0.00 0.00 2201.48 0.00 0.00 0.00 2226.88 4625.20 0.00 Continued

Table F.2: Continued

Streams Emission Formic LP- Rec Rec RO- Materials Inoculum Methanol Phosphate S-101 -ER Acid Steam Biomass Water water-F Total MT/h 34.68 5.00 0.01 10.99 2.26 0.40 31.80 42.14 0.10 119.26 Flow Temperature °C 200.00 25.00 25.00 152.30 25.00 25.00 37.00 37.00 25.00 37.00 Pressure atm 1.00 1.00 1.00 5.00 1.00 1.00 2.00 2.00 1.00 2.00 Total kWh/h 3884.93 74.46 0.15 8389.51 40.10 2.58 1362.91 1793.55 2.92 5055.82 Enthalpy

Total kmol/h 1128.63 108.63 0.20 609.93 70.79 2.44 1700.66 2303.86 5.55 6438.88

288 Contents Amm. Sulfate 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.73 0.00 1.61

Biomass 0.00 0.00 0.20 0.00 0.00 0.00 143.12 0.00 0.00 151.08 Carb. Dioxide 248.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Formic Acid 0.00 108.63 0.00 0.00 0.00 0.00 0.89 4.62 0.00 2.43 Hydr. Sulfide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Magne Sulfate 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.16 0.00 0.36 Methane 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methanol 0.00 0.00 0.00 0.00 70.33 0.00 6.92 0.36 0.00 78.78 Nitrogen 786.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oxygen 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Phosphate 0.00 0.00 0.00 0.00 0.00 2.44 0.50 2.58 0.00 5.67 RO water 0.00 0.00 0.00 0.00 0.00 0.00 0.57 5.94 5.55 6.50 Sulfur 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 92.37 0.00 0.00 609.93 0.46 0.00 1548.49 2289.47 0.00 6192.46

Continued

Table F.2: Continued

Streams Materials S-102 S-103 S-104 S-105 S-106 S-107 S-108 S-112 S-115 S-117 Total Flow MT/h 31.80 5.39 0.60 3.07 84.35 27.69 5.00 42.17 2.26 42.14 Temperature °C 37.00 40.00 40.00 40.00 100.00 40.00 25.00 99.90 65.00 99.70 Pressure atm 1.00 2.00 5.00 5.00 1.00 2.00 2.00 1.00 1.00 1.00 Total Enthalpy kWh/h 1362.53 286.21 31.80 34.60 9695.82 311.52 74.51 4847.91 104.21 4834.47

Total Contents kmol/h 1700.66 208.62 23.18 106.58 4609.25 959.63 108.63 2304.62 70.79 2303.86 Amm. Sulfate 0.14 0.00 0.00 0.00 1.47 0.00 0.00 0.73 0.00 0.73 Biomass 143.12 0.00 0.00 0.00 7.53 0.00 0.00 3.77 0.00 0.00 Carb. Dioxide 0.00 72.91 8.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00

289 Formic Acid 0.89 0.00 0.00 0.00 9.23 0.00 108.63 4.62 0.00 4.62

Hydr. Sulfide 0.00 0.10 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Magne 0.03 0.00 0.00 0.00 0.32 0.00 0.00 0.16 0.00 0.16 Sulfate Methane 0.00 135.60 15.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Methanol 6.92 0.00 0.00 0.00 0.72 0.00 0.00 0.36 70.33 0.36 Nitrogen 0.00 0.00 0.00 84.20 0.00 758.14 0.00 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 22.38 0.00 201.49 0.00 0.00 0.00 0.00 Phosphate 0.50 0.00 0.00 0.00 5.17 0.00 0.00 2.58 0.00 2.58 RO water 0.57 0.00 0.00 0.00 5.86 0.00 0.00 2.93 0.00 5.94 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 1548.49 0.00 0.00 0.00 4578.95 0.00 0.00 2289.47 0.46 2289.47

Continued

Table F.2: Continued

Streams Materials S-122 S-124 S-126 S-131 S-134 S-136 S-138 S-139 Unreacted Gases Water-SG Total Flow MT/h 3.11 10.99 39.92 0.01 0.40 39.92 42.14 87.46 33.62 10.99 Temperature °C 78.70 25.00 25.00 25.00 25.00 37.00 99.70 37.00 37.00 25.00 Pressure atm 1.00 5.00 5.00 5.00 2.00 5.00 2.00 3.00 2.00 1.00 Total Enthalpy kWh/h 202.88 321.18 1161.52 0.15 2.58 1713.30 4835.00 3686.98 909.85 320.65

Total Contents kmol/h 117.29 609.93 2203.29 0.20 2.44 2203.29 2303.86 4726.53 1092.29 609.93 Amm. Sulfate 0.00 0.00 1.51 0.00 0.00 1.51 0.73 1.47 0.00 0.00 Biomass 0.00 0.00 0.00 0.20 0.00 0.00 0.00 7.53 0.00 0.00

290 Carb. Dioxide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 202.49 0.00

Formic Acid 0.00 0.00 0.00 0.00 0.00 0.00 4.62 9.23 0.00 0.00 Hydr. Sulfide 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 Magne 0.00 0.00 0.16 0.00 0.00 0.16 0.16 0.32 0.00 0.00 Sulfate Methane 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 46.24 0.00 Methanol 71.04 0.00 0.00 0.00 0.00 0.00 0.36 71.75 0.00 0.00 Nitrogen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 758.14 0.00 Oxygen 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 85.31 0.00 Phosphate 0.00 0.00 0.15 0.00 2.44 0.15 2.58 5.17 0.00 0.00 RO water 0.00 0.00 0.00 0.00 0.00 0.00 5.94 5.86 0.00 0.00 Sulfur 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Dioxide Water 46.25 609.93 2201.48 0.00 0.00 2201.48 2289.47 4625.20 0.00 609.93

Continued

Table F.2: Continued

Streams Materials WW-1 WW-2 WW-3 Total Flow MT/h 42.17 0.85 0.14 Temperature °C 99.90 99.50 99.70 Pressure atm 1.00 1.00 1.00 Total Enthalpy kWh/h 4847.91 97.13 16.07

Total Contents kmol/h 2304.62 46.50 6.31 Amm. Sulfate 0.73 0.00 0.00 Biomass 3.77 0.00 3.77 Carb. Dioxide 0.00 0.00 0.00

291 Formic Acid 4.62 0.00 0.00

Hydr. Sulfide 0.00 0.00 0.00 Magne 0.16 0.00 0.00 Sulfate Methane 0.00 0.00 0.00 Methanol 0.36 0.71 0.00 Nitrogen 0.00 0.00 0.00 Oxygen 0.00 0.00 0.00 Phosphate 2.58 0.00 0.00 RO water 2.93 0.00 2.54 Sulfur 0.00 0.00 0.00 Dioxide Water 2289.47 45.79 0.00

Table F.3: Equipment purchase costs for biological conversion of biogas to methanol

Equipment Purchase Costs Procedure Name Equipment Size Total Cost ($) Section: Biomass Production ST-101 Heat 40 m3/h 755,000 Sterilizer throughput FR-101 Biomass 267.87 m3 552,000 Production Unit G-102 Air 204.35 kW 266,000 Compressor G-101 Biogas 46.23 kW 73,000 Compressor PM-102 AMS 6.43 kW 41,000 Pump PM-103 Inoculum 0.00 kW 9,000 Pump Section Total 1,696,000 Section: Biogas to Methanol Conversion FR-102 (3 units @ $2,462,000/unit) Trickle Bed 500 m3/ 7,386,000 Reactors reactor DS-101 Disk-Stack Centrifuge 120.09 m3/h 1,578,000 throughput G-104 Air 709.56 kW 847,000 Compressor G-103 Biogas 160.47 kW 213,000 Compressor HX-102 Rec Water 68.66 m2 97,000 Cooler h.t. area PM-107 Rec Water 1.74 kW 23,000 Pump PM-106 Rec. Biomass 1.28 kW 20,000 Pump PM-104 Formate 0.17 kW 9,000 Pump PM-105 Phosphate 0.01 kW 9,000 Pump Section Total 10,182,000 Section: Methanol Purification C-101 Primary 19.18 m3 244,000 Distillation RVF-101 Rotary Vacuum 86.12 m2 231,000 Filter filter area C-102 Secondary 3.41 m3 86,000 Distillation PM-108 Crude Methanol Pump 3.55 kW 32,000 HX-101 Product 1.37 m2 10,000 Cooler h.t. area Section Total 603,000

Continued

292

Table F.3: Continued Section: Energy Recovery SG-101 Steam Generation- 10.99 MT/h 183,000 Energy Recovery throughput PM-103 Water Pump- 1.78 kW 24,000 Energy Recovery Section Total 207,000

- Unlisted - 3,172,000 Equip

Total Equipment Purchase Cost 15,859,000

293

Table F.4: Breakdown of utility costs for biological conversion of biogas to methanol

LP HP Cooling Chilled Natural % of Power Total Steam Steam Water Water Gas Section Section: Biomass Production G-101- 25,630.72 0 0 2,202.00 0 0 27,832.72 2.73% Biogas Compress. G-102- 113,291.06 0 0 7,025.19 0 0 120,316.25 11.80% Air Compress. PM-102- 3,566.64 0 0 0 0 0 3,566.64 0.35% AMS Pump ST-101- 0 224,963.43 0 56,825.47 0 0 281,788.90 27.63% Heat Sterilizer PM-103- 294 0.42 0 0 0 0 0 0.42 0.00% Inoculum Pump

FR-101- 400,974.46 0 0 49,534.57 0 0 450,509.03 44.17% Biomass Prod Unlisted 33,966.46 0 0 0 0 0 33,966.46 3.33% General Load 101,899.37 0 0 0 0 0 101,899.37 9.99% Section Total 679,329.14 224,963.43 0 115,587.23 0 0 1,019,879.80 100.00%

Section: Biogas to Methanol Conversion G-103- 88,962.72 0 0 7,375.42 0 0 96,338.14 10.21% Biogas Compress. G-104- 393,380.25 0 0 19,499.64 0 0 412,879.89 43.76% Air Compress. PM-104- 91.84 0 0 0 0 0 91.84 0.01% Formate Pump

Continued

Table F.4: Continued

PM-105- 3.52 0 0 0 0 0 3.52 0.00% Phosphate Pump FR-102- 24,948.00 0 0 2,533.18 0 0 27,481.18 2.91% TBR DS-101- 36,130.05 0 0 2,221.79 0 0 38,351.84 4.06% Centrifuge PM-106- 711.09 0 0 0 0 0 711.09 0.08% Rec. Pump PM-107- 962.11 0 0 0 0 0 962.11 0.10% Wat. Rec. Pump HX-102-

295 0 0 0 230,422.30 0 0 230,422.30 24.42% Cooler

Unlisted 34,074.35 0 0 0 0 0 34,074.35 3.61% General Load 102,223.05 0 0 0 0 0 102,223.05 10.83% Section Total 681,486.97 0 0 262,052.34 0 0 943,539.31 100.00%

Section: Methanol Purification PM-108- 1,965.81 0 0 0 0 0 1,965.81 0.06% Prod. Pump C-101- 0 2,297,023.20 0 541,155.77 0 0 2,838,178.97 83.52% Primary Distillation C-102- Secondary 0 155,076.42 0 65,233.28 0 0 220,309.70 6.48% Distillation

Continued

Table F.4: Continued

RVF-101- 238,728.13 0 0 0 0 0 238,728.13 7.03% Rotary Filter

HX-101- 0 0 0 0 38,667.68 0 38,667.68 1.14% Product Cooler

Unlisted 15,043.37 0 0 0 0 0 15,043.37 0.44%

General Load 45,130.11 0 0 0 0 0 45,130.11 1.33%

Section Total 300,867.43 2,452,099.62 0 606,389.05 38,667.68 0 3,398,023.78 100.00%

296

Section: Energy Recovery

PM-101 984.96 0 0 0 0 0 984.96 80.00%

Unlisted 61.56 0 0 0 0 0 61.56 5.00%

General Load 184.68 0 0 0 0 0 184.68 15.00%

Section Total 1,231.20 0 0 0 0 0 1,231.20 100.00%

Process Total 1,662,914.73 2,677,063.05 0 984,028.61 38,667.68 0 5,362,674.09