EXPLORING THE IMPACT OF BIOGAS QUANTITY AND QUALITY IN DIFFERENT DIGESTER TYPES WITH VARIATIONS IN TEMPERATURE By María Inés Barrios Arosemena A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Biosystems Engineering Master of Science 20 21 ABSTRACT EXPLORING THE IMPACT OF BIOGAS QUANTITY AND QUALITY IN DIFFERENT DIGESTER TYPES WITH VARIATIONS IN TEMPERATURE By María Inés Barrios Arosemena The energy sector in the U.S. has been pushing for policies such as the Renewable Portfolio Standard (RPS) to mitigate the impacts of GHG emissions. Biogas from anaero bic digesters is a viable form of renewable energy, due to its CH 4 composition, it can be used as a replacement for power and heat generation or upgraded and sold as biomethan e. This study analyzed the effects of temperature in biogas quality and quantity of dairy cow manure in order to compare two main systems, a CSTR and a covered lagoon. A biochemical methane potential (BMP) test was performed to determine material biodegradabi lity o f dairy cow manure with respect to temperature. The results show that all samples are anaerobically biodegradable with samples yielding 86, 168 , 440, 475 and 448 L biogas per kg initial VS for 15 ° C, non - mixed; 20 ° C, non - mixed; 30 ° C, non - mixed; 39 ° C, non - mixed; and 39 ° C, mixed, respectively. The BMP results demonstrated so significant difference between 30 ° C, non - mixed; 39 ° C, non - mixed; and 39 ° C, mixed, respectively . In addition, the effects of psychrophilic, unregulated, and mesophilic conditions were tested in small scale lab pilot digesters. Results show that mesophilic condition yielded the highest cumulative biogas production, while the psychrophilic and unregulated conditions presented higher methane yield. A life cycle analysis was performed to c ompare two popular anaerobic digestion systems, a CTSR and a covered lagoon, versus current manure management systems for dairy cow manure . The LCA revealed that both systems have less environmental burdens when compared to current waste management systems and a CSTR has less environmental burdens than a covered lagoon. iii ACKNOWLEDGEMENTS I would like to thank you with the deepest of gratitude to Dr. Dana Kirk, who not only allowed me to complete my graduate studies and expand my love for the bioenergy field, but for allowing me to grow both personally and professionally during his guidance in my undergraduate and graduate years at MSU. I am al so grateful for Dr. Wei Liao and Dr. Christopher Saffron for being great committee members and providing great support during this entire journey. I would like to thank my family and friends for providing immense amounts of love, support, and the strength to keep me going forward and believing in my dreams. I would like to provide a big thank you to my lab partner and fellow graduate student, Fahmi Dwilaksono , for his support, knowledge, and love for Disney during our graduate studies. I would like to thank all of ADREC technicians for their help and support during day - to - day operations for this project. I would also like to thank Lou Faivor and Dalton Brenke for providing not only samples for this project, but knowledge and support. Finally, I would like to thank the Department of Biosystems Engineering for creating some of the best 6 years of my life where I have been able to expand my knowledge and grow into a better engineer. iv TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ........................ vii LIST OF FIGURES ................................ ................................ ................................ ...................... xii KEYS TO ABBREVIATIONS ................................ ................................ ................................ .. xviii 1. INTRODUCTION ................................ ................................ ................................ ...................... 1 1.1 Problem statement ................................ ................................ ................................ ........... 1 1.2 Goal and objectives ................................ ................................ ................................ ......... 2 2. LITERATURE REVIEW ................................ ................................ ................................ ........... 3 2.1 Energy Systems ................................ ................................ ................................ ..................... 3 2.1.1 Fossil Fuels ................................ ................................ ................................ ..................... 4 2.1.2 Renewable Fuels ................................ ................................ ................................ ............. 6 2.1.2.1 Wind, Solar, and Biogas for Electricity ................................ ................................ .. 6 2.1.2.2 Renewable Natural Gas ................................ ................................ ........................... 8 2.2 Policy Drivers ................................ ................................ ................................ ........................ 9 2.2.1 Renewable Portfolio Standards ................................ ................................ ...................... 9 2.2.2 Renewable Fuels Standard ................................ ................................ ........................... 10 2.2.3 Carbon Cap and Trade ................................ ................................ ................................ .. 12 2.2.3.1 Carbon Intensity ................................ ................................ ................................ .... 12 2.2.3.1.1 Model ................................ ................................ ................................ ............. 12 2.2.3.2 California LCFS process ................................ ................................ ....................... 13 2.2.3.3 How Dairy Manure Fits? ................................ ................................ ...................... 14 2.3 Dairy Manure Management Systems ................................ ................................ .................. 14 2.3.1 Overview of Standard Practice ................................ ................................ ..................... 14 2.3.2 Environmental Impacts ................................ ................................ ................................ . 14 2.3.2.1 Emissions generation during lagoon storage ................................ ........................ 15 2.4 Anaerobic Digestion ................................ ................................ ................................ ............ 15 2.4.1 Process of Anaerobic Digestion ................................ ................................ ................... 15 2.4.1.1 Digesters 101 ................................ ................................ ................................ ........ 16 2.4.2 Factors influencing Anaerobic Digestion ................................ ................................ ..... 16 2.4.2.1 Temperature ................................ ................................ ................................ .......... 16 2.4.2.2 Mixing ................................ ................................ ................................ ................... 19 2.4.2.3 Hydraulic Retention Time ................................ ................................ ..................... 20 2.4.2.4 Organic Loading Rate ................................ ................................ ........................... 20 2.4.2.5 Feedstocks ................................ ................................ ................................ ............. 21 2.4.2.6 Digester Types ................................ ................................ ................................ ...... 22 2.4.2.6.1 Complete Mix Stirred Reactors ................................ ................................ ..... 23 2.4.2.6.2 Covered Lagoon ................................ ................................ ............................. 23 2.4.4.2.3 Costs ................................ ................................ ................................ ............... 24 2.4.3 Environmental Benefits ................................ ................................ ................................ 25 2.4.4 Revenue ................................ ................................ ................................ ........................ 26 v 3. MATERIALS AND METHODS ................................ ................................ .............................. 27 3.1 Wast e Collection and Handling ................................ ................................ ........................... 27 3.1.1 Anaerobic Filtrate ................................ ................................ ................................ ......... 27 3.1.2 Liquid Cow Manure ................................ ................................ ................................ ..... 27 3.1.3 Sample Storage ................................ ................................ ................................ ............. 27 3.2 Waste Characterization ................................ ................................ ................................ ....... 27 3.3 Biochemical methane potential Test (BMP) ................................ ................................ ....... 28 3.3.1 Se t - up ................................ ................................ ................................ ............................ 29 3.3.2 Operation and Monitoring ................................ ................................ ............................ 30 3.3.3 BMP Calculations ................................ ................................ ................................ ......... 30 3.4 Pilot systems design, operation, and analysis. ................................ ................................ .... 32 3.4.1 Pilot Vessel/Structure ................................ ................................ ................................ ... 32 3.4.2 Pilot Preparation ................................ ................................ ................................ ........... 33 3.4.3 Pilot Set Up and Seeding ................................ ................................ .............................. 33 3.4.4 Pilot Feeding ................................ ................................ ................................ ............ 34 3.4.4.1 Feeding Volume ................................ ................................ ................................ .... 34 3.4.4.2 Calculations ................................ ................................ ................................ ........... 34 3.4.4.3 Procedure ................................ ................................ ................................ .............. 34 3.4.5 Pilot Monitoring ................................ ................................ ................................ ........... 35 3.4.6 Dige state ................................ ................................ ................................ ....................... 35 3.4.7 Gas ................................ ................................ ................................ ................................ 35 3.4.7.1 Gas Production ................................ ................................ ................................ ...... 35 3.4.7.2 Gas Analysis ................................ ................................ ................................ ......... 35 3.5 Statistical Analysis ................................ ................................ ................................ .............. 36 4. CHARA CTERIZATION AND BIOCHEMICAL METHANE POTENTIAL ........................ 37 4.1 Characteristics of Raw Samples ................................ ................................ .......................... 37 4.2 BMP Test Results ................................ ................................ ................................ ................ 37 4.2.1 Pre and post digestion analysis ................................ ................................ ..................... 38 4.2.2 Gas Production ................................ ................................ ................................ ............. 45 4.2.3 Methane Concentration ................................ ................................ ................................ 49 4.3 Discussion ................................ ................................ ................................ ........................... 53 5. PILOT TESTING ................................ ................................ ................................ ...................... 58 5.1 Purpose and Conditions ................................ ................................ ................................ ....... 58 5.2 Results and discussion ................................ ................................ ................................ ......... 59 5.2.1 Characterization ................................ ................................ ................................ ............ 59 5.2.1.1 Dairy Cow Manure ................................ ................................ ............................... 59 5.2.1.2 Organic Loading Rate ................................ ................................ ........................... 59 5.2. 1.3 pH ................................ ................................ ................................ .......................... 60 5.2.1.2 Total Solids and Volatile Solids Reduction ................................ .......................... 63 5.2.2 Gas Production ................................ ................................ ................................ ............. 71 5.2.3 Gas Quality ................................ ................................ ................................ ................... 85 5.2.3.1 Methane ................................ ................................ ................................ ................. 85 5.2.3.2 Hydrogen Sulfide ................................ ................................ ................................ .. 93 5.3 Summary ................................ ................................ ................................ ............................. 98 vi 6. LIFE CYCLE ANALYSIS ................................ ................................ ................................ ..... 101 6.1 Introduction ................................ ................................ ................................ ....................... 101 6.1.1 Supply Cha in ................................ ................................ ................................ .............. 101 6.1.2 Biorefinery Classification ................................ ................................ ........................... 102 6.2 Goal and Scope ................................ ................................ ................................ .................. 103 6.3 Life Cycle Data Inventory ................................ ................................ ................................ . 106 6.3.1 Raw Material and Handling ................................ ................................ ........................ 107 6.3.2 Process ................................ ................................ ................................ ........................ 110 6.3.3 Outputs and Land Application ................................ ................................ .................... 112 6.3.4 Data Quality Evaluation ................................ ................................ ............................. 114 6.4 Impact Assessment ................................ ................................ ................................ ............ 115 6.4.1 Global Warming Potential (GWP) ................................ ................................ ............. 115 6.4.2 Air Acidification Potential (AAP) ................................ ................................ .............. 119 6.4.3 Water Consumption Potential (WCP) ................................ ................................ ........ 121 6.4.4 Water Eutrophication Potential (WEP) ................................ ................................ ...... 123 6.5 Interpretation ................................ ................................ ................................ ..................... 126 6.5.1 Sensitiv ity Analysis ................................ ................................ ................................ .... 126 6.5.1.1 Global Warming Potential (GWP) ................................ ................................ ...... 126 6.5.1.2 Air Acidification Potential (AAP) ................................ ................................ ...... 128 6.5.1.3 Water Consumption Potential (WCP) ................................ ................................ . 130 6.5.1.4 Water Eutrophication Potential (WEP) ................................ ............................... 131 6.5.2 Consistency and Completeness Check ................................ ................................ ....... 133 6.6 Overall Life Cycle Comparison and System Recommendation ................................ ........ 135 7. OVERALL CONCLUSIONS AND RECOMMENDATIONS ................................ .............. 137 7.1 Biochemical methane potential testing ................................ ................................ .............. 137 7.2 Pilot data ................................ ................................ ................................ ............................ 137 7.3 Life Cycle Analysis ................................ ................................ ................................ ........... 138 7.4 Future Work ................................ ................................ ................................ ...................... 139 APPENDICIES ................................ ................................ ................................ ........................... 140 Appendix A. R - Script and Results for BMP data ................................ ................................ ... 141 Appendix B. R - Scripts and Results for Pilot Data ................................ ................................ .. 178 Appendix C. Additional BMP data ................................ ................................ ......................... 220 Appendix D. Additional Data and Figures for Pilot Data ................................ ....................... 251 Appendix E. Covered Lagoon System Diagram and Table of System Stream Conditions .... 255 Appendix F. CSTR System Diagram and Table of System Stream Conditions ..................... 256 Appendix G. Stoichiometric E quations ................................ ................................ ................... 257 REFERENCES ................................ ................................ ................................ ........................... 258 vii LIST OF TABLES Table 2.1. Overall Comparison of a Complete Mix Digester versus a Covered Lagoon ............. 24 Table 2.2. Case Studies regarding Capital Costs and Payback Period for Different Digester Types ................................ ................................ ................................ ................................ ....................... 24 Table 3.1. Filters utilized during the SCOD process ................................ ................................ .... 28 Table 3.2. Summary of BMP assays with variations in temperature and mixing ......................... 29 Table 3.3. Pilots Temperature Profiles ................................ ................................ ......................... 32 Table 4.1. Raw Characterization ................................ ................................ ................................ ... 37 Table 4.2. Pre - and post - digestion TS content in BMP bottles, Average of Trials 1, 2 & 3 ........ 38 Table 4.3. Pre - and post - digestion VS content in BMP bottles, Average of Trials 1, 2 & 3 ........ 39 Table 4.4. Pre - and post - digestion pH in BMP bottles, Average of Trials 1, 2 & 3 ..................... 41 Table 4.5. One Way ANOVA Results for the Total Solids Reduction in BMP bottles ............... 42 Table 4.6. One Way ANOVA Results for the Volatile Solids Reduction in BMP bo ttles ........... 43 Table 4.7. One Way ANOVA Results for the pH Change in BMP bottles ................................ .. 44 Table 4.8. Cumulative Biogas Production in BMP Bottles, Average of Trials 1, 2 & 3 .............. 45 Table 4.9. One Way ANOVA Results for the Cumulative Biogas Production in BMP bottles ... 46 Table 4.10. Methane Concentration in BMP bottles, Average of Trials 1, 2 & 3 ........................ 49 Table 4.11. One Way AN OVA Results for Methane Content in BMP bottles ............................ 50 Table 5.1. Summary of Pilots Testing Conditions ................................ ................................ ........ 58 Table 5.2. Dairy Cow Manure Characterization ................................ ................................ ........... 59 Table 5.3. Organic Loading Rate for the Project based on HRT ................................ .................. 60 Table 5.4. Average pH Effluent Measurements based on Temperature Profile, Average of 3 ................................ ................................ ................................ ................................ ............ 61 Table 5.5. Average pH Effluent Measurements based on HRT ................................ ................... 61 ............. 63 viii Table 5.7. Average Tot al Solids Reduction based on HRT ................................ .......................... 64 Table 5.8. Two Way ANOVA Results for Total Solids Reduction for each Condition ............... 65 ......... 67 Table 5.10. Volatile Solids Reduction based on HRTs ................................ ................................ 68 Table 5.11. Two Way ANOVA Results for Volatile Solids Reduction for each Condition ........ 69 Table 5.12. Cumulative Biogas 72 Table 5.13. Average Cumulative Biogas Production after each HRT ................................ .......... 73 ......... 75 Table 5.15. Daily Biogas Production based on HRTs ................................ ................................ .. 76 Tabl e 5.16. Two Way ANOVA Results for the Daily Biogas Production for each Condition .... 77 Table 5.17. Two Way ANOVA Results for the Daily Biogas Production per kg Initial VS for each Condition ................................ ................................ ................................ .............................. 79 Table 5.18. Methane Content based on Temperature Profile, Average of ..................... 86 Table 5.19. Methane Content based on HRTs ................................ ................................ .............. 86 Table 5.20. Two Way ANOVA Results for Methane Content for each Condition ...................... 87 Table ...... 94 Table 5.22. Hydrogen Sulfide Content based on HRTs ................................ ................................ 94 Table 5.23. Two Way ANOVA Results for the Hydrogen Sulfide for each Condition ............... 95 Table 5.24. Summary Table for Results obtained based on Temperature Profile ........................ 98 Table 6.1 Data Quality Evaluation Using the Weidema Method (Weidema et al., 2004) .......... 107 Table 6.2. Life Cycle Data Inventory for Raw Material and Handling ................................ ...... 108 Table 6.3. Life Cycle Data Inven tory Anaerobic Digestion Process ................................ .......... 111 Table 6.4. Life Cycle Data Inventory for Outputs and Land Application ................................ .. 113 Table 6.5. Data Quality Evaluation Summary for LCI ................................ ............................... 114 Table 6.6. Global Warming Potential Conversion Values obtained from the TRACI Model and Chen et al., 2015 ................................ ................................ ................................ ......................... 116 ix Table 6.7. Air Acidification Potential Conversion Values for Anaerobic Digestion (Chen et al., 2015) ................................ ................................ ................................ ................................ ........... 119 Table 6.8. Water Eutrophication Potential Conversion Values Obtained from the TRACI Model ................................ ................................ ................................ ................................ ..................... 124 Table 6. 9. Checklist and Inconsistencies based on Data Quality ................................ ............... 133 Table 6.10. Completeness Check for a CSTR ................................ ................................ ............ 134 Table 6.11. Completeness Check for a Covered Lagoon ................................ ............................ 135 Table 6.12. Overall System Comparison for all Impact Categories analyzed for a CSTR, Covered Lagoon and Dairy Cow Manure per Functional Unit ................................ ................................ . 135 Table C.1. Raw Sample Characterization Round 1 ................................ ................................ .... 220 Table C.2. Raw Sample Characterization Trial 2 ................................ ................................ ....... 220 Table C.3. Raw Sample Characterization Trial 3 ................................ ................................ ....... 220 Table C.4. Trial 1 BMP Pre - digestion data for 15 ° C, non - mixed ................................ ............... 221 Table C.5. Trial 1 BMP Post - digestion data for 15 ° C, non - mixed ................................ ............. 221 Table C.6. Trial 1 BMP Total Solids Reduction for 15 ° C, non - mixed ................................ ....... 222 Table C.7. Trial 1 BMP Volatile Solids Reduction for 15 ° C, non - mixed ................................ .. 222 Table C.8. Trial 1 BMP Pre - digestion data for 20 ° C, non - mixed ................................ ............... 223 Table C.9. Trial 1 BMP Post - digestion data for 20 ° C, non - mixed ................................ ............. 223 Table C.10. Trial 1 BMP Total Solids Reduction for 20 ° C, non - mixed ................................ ..... 224 Table C.11. Trial 1 BMP Volatile Solids Reduction for 20 ° C, non - mixed ................................ 224 Table C.12. Trial 1 BMP Pre - digestion data for 30 ° C, non - mixed ................................ ............. 225 Table C.13. Trial 1 BMP Post - digestion data for 30 ° C, non - mixed ................................ ........... 225 Table C.14. Trial 1 BMP Total Solids Reduction for 30 ° C, non - mixed ................................ ..... 226 Table C.15. Trial 1 BMP Volatile Solids Reduction for 30 ° C, non - mixed ................................ 226 Table C.16. Trial 1 BMP Pre - digestion data for 39 ° C, non - mixed ................................ ............. 227 Table C.17. Trial 1 BMP Post - digestion data for 39 ° C, non - mixed ................................ ........... 227 x Table C.18. Trial 1 BMP Total Solids Reduction for 39 ° C, non - mixed ................................ ..... 228 Table C.19. Trial 1 BMP Volatile Solids Reduction for 39 ° C, non - mixed ................................ 228 Table C.20. Trial 1 BMP Pre - digestion data for 39 ° C, mixed ................................ .................... 229 Table C.21. Trial 1 BMP Post - digestion data for 39 ° C, mixed ................................ .................. 229 Table C.22. Trial 1 BMP Total Solids Reduction for 39 ° C, mixed ................................ ............ 230 Table C.23. Trial 1 BMP Volatile Solids Reduction for 39 ° C, mixed ................................ ........ 230 Table C.24. Trial 2 BMP Pre - digestion data for 15 ° C, non - mixed ................................ ............. 231 Table C.25. Trial 2 BMP Post - digestion data for 15 ° C, non - mixed ................................ ........... 231 Table C.26. Trial 2 BMP Total Solids Reduction for 15°C, non - mixed ................................ .... 232 Table C.27. Trial 2 BMP Volatile Solids Reduction for 15°C, non - mixed ................................ 232 Table C.28. Trial 2 BMP Pre - digestion data for 20°C, non - mixed ................................ ............ 233 Table C.29. Trial 2 BMP Post - digestion data for 20°C, non - mixed ................................ ........... 233 Table C.30. Trial 2 BMP Total Solids Reduction for 20°C, non - mixed ................................ .... 234 Table C.31. Trial 2 BMP Volatile Solids Reduction for 20°C, non - mixed ................................ 234 Table C.32. Trial 2 BMP Pre - digestion data for 30°C, non - mixed ................................ ............ 235 Table C.33. Trial 2 BMP Post - digestion data for 30°C, non - mixed ................................ ........... 235 Table C.34. Trial 2 BMP Total Solids Reduction for 30°C, non - mixed ................................ .... 236 Table C.35. Trial 2 BMP Volatile Solids Reduction for 30°C, non - mixed ................................ 236 Table C.36. Trial 2 BMP Pre - digestion data for 39°C, non - mixed ................................ ............ 237 Table C.37. Trial 2 BMP Post - digestion data for 39°C, non - mixed ................................ ........... 237 Table C.38. Trial 2 BMP Total Solids Reduction for 39°C, non - mixed ................................ .... 238 Table C.39. Trial 2 BMP Volatile Solids Reduction for 39°C, non - mixed ................................ 238 Table C.40. Trial 2 BMP Pre - digestion data for 39°C, mixed ................................ .................... 239 Table C.41. Trial 2 BMP Post - digestion data for 39°C, mixed ................................ .................. 239 Table C.42. Trial 2 BMP Total Solids Reduc tion for 39°C, mixed ................................ ............ 240 xi Table C.43. Trial 2 BMP Volatile Solids Reduction for 39°C, mixed ................................ ....... 240 Table C.44. Trial 3 BMP Pre - digestion data for 15°C, non - mixed ................................ ............ 241 Table C.45. Trial 3 BMP Post - digestion data for 15°C, non - mixed ................................ ........... 241 Ta ble C.46. Trial 3 BMP Total Solids Reduction for 15 °C, non - mixed ................................ ... 242 Table C.47. Trial 3 BMP Volatile Solids Reduction for 15 °C, n on - mixed ............................... 242 Table C.48. Trial 3 BMP Pre - digestion data for 20°C, non - mixed ................................ ............ 243 Table C.49. Trial 3 BMP Post - digestion data for 20°C, non - mixed ................................ ........... 243 Table C.50. Trial 3 BMP Total Solids Reduction for 20°C, non - mixed ................................ .... 244 Table C.51. Trial 3 BMP Volati le Solids Reduction for 20°C, non - mixed ................................ 244 Table C.52. Trial 3 BMP Pre - digestion data for 30°C, non - mixed ................................ ............ 245 Table C.53. Trial 3 BMP Post - digestion data for 30 °C, non - mixed ................................ .......... 245 Table C.54. Trial 3 BMP Total Solids Reduction for 30°C, non - mixed ................................ .... 246 Table C.55. Trial 3 BMP Volatile Solids Reduction for 30°C, non - mixed ................................ 246 Table C.56. Trial 3 BMP Pre - digestion data for 39°C, non - mixed ................................ ............ 247 Table C.57. Trial 3 BMP Post - digestion data for 39°C, non - mixed ................................ ........... 247 Table C.58. Trial 3 BMP Total Solids Reduction for 39°C, non - mixed ................................ .... 248 Table C.59. Trial 3 BMP Volatile Solids Reduction for 39°C, non - mixed ................................ 248 Table C.60. Trial 3 BMP Pre - digestion data for 39°C, mixed ................................ .................... 249 Table C.61. Trial 3 BMP Post - digestion data for 39°C, mixed ................................ .................. 249 Table C.62. Trial 3 BMP Total Solids Reduction for 39°C, mixed ................................ ............ 250 Table C.63. Trial 3 BMP Volatile Solids Reduction for 39°C, mixed ................................ ....... 250 Table D.1. Biogas Production per kg of Initial VS based on Environment ................................ 251 Table E.1. Stream Conditions for Figure E.1 ................................ ................................ .............. 255 Table F.1. Stream Conditions for Figure F.1 ................................ ................................ .............. 256 xii LIST OF FIGURES Figure 2.1. Mean annual earth temperature observations at individual stations, superimposed on well - water tempera ture contours. ................................ ................................ ................................ .. 18 Figure 2.2. Measured temperature at the side and bottom of sludge line and ambient temperature at a 100 head farm in South Dakota (Darrington & Cortus, 2011) ................................ ............... 19 Figure 2.3. Anaerobic Digesters Operating in the United States from 2000 to 2019 (EPA , 2019) ................................ ................................ ................................ ................................ ....................... 22 Figure 2.4. Designs for Operating Anaerobic Digesters in the United States (EPA, 2019) ......... 23 Figure 3.1. Pilot Set Up ................................ ................................ ................................ ................. 33 Figure 4.1. Percent Average Reductions with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ 39 Figure 4.2. Percent Average Reductions with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ 40 Figure 4.3. Average pH Change with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ................................ ................ 41 Figu re 4.4. Tukey Honest Significant Difference Results for the percent TS reduction, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ........................... 42 Figure 4.5. Tukey Ho nest Significant Difference Results for the percent VS reduction, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ........................... 43 Figure 4.6. Tukey Honest Significant Difference Results for the pH change, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ................................ ............. 44 Figure 4.7. Average Cumulative Biogas Production in BMP bottles, Average of Trials 1, 2 & 3 46 Figure 4.8. Tukey Honest Significant Difference Results for the Cumulative Biogas Producti on, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ............ 47 Figure 4.9. Cumulative Biogas Production (Average of Triplicates) for Trial 1 .......................... 48 Figure 4.10. Cumulative Biogas Production (Average of Triplicates) for Trial 2 ........................ 48 Figure 4.11. Cumulative Biogas Production (Average of Triplicates) for Trial 3 ........................ 49 Figure 4.12. Average Methane Content in BMP bottles, Average of Trials 1, 2 & 3 .................. 50 xiii Figu re 4.13. Tukey Honest Significant Difference Results for the Cumulative Biogas Production, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ............ 51 Figure 4.14. Biogas Methane Content (Average of Triplicates) for Trial 1 ................................ . 52 Figure 4.15. Biogas Methane Content (Average of Triplicates) for Trial 2 ................................ . 52 Figure 4.16. Biogas Methane Content (Average of Triplicates) for Trial 3 ................................ . 53 Figure 5.1. pH measurement for Pilot Effluents ................................ ................................ ........... 62 Figure 5.2. Tukey Honest Significant Difference Results for the Total Solids Reduction based on Temperature ................................ ................................ ................................ ................................ .. 65 Figure 5.3. Tukey Honest Significant Difference Results for the Total Solids Reduction based on HRT ................................ ................................ ................................ ................................ ............... 66 Figure 5.4. Total Solids Reduction f or Pilots during project timeline ................................ .......... 67 Figure 5.5 Tukey Honest Significant Difference Results for the Volatile Solids Reduction based o n Temperature ................................ ................................ ................................ ............................. 69 Figure 5.6. Tukey Honest Significant Difference Results for the Volatile Solids Reduction based on HRT ................................ ................................ ................................ ................................ .......... 70 Figure 5.7. Volatile Solids Reduction for Pilots during project timeline ................................ ..... 71 Figure 5.8. Cumulative Biogas Production for Psychrophilic, Unregulated and Mesophilic Pilots ................................ ................................ ................................ ................................ ....................... 74 Figure 5.9. Tukey Honest Significant Difference Results for the Daily Biogas Production based on Temperature ................................ ................................ ................................ ............................. 77 Figure 5.10. Tukey Honest Significant Difference Results for the Daily Biogas Production based on HRT ................................ ................................ ................................ ................................ .......... 78 Figure 5.11. Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on Temperature ................................ ................................ ................................ .. 80 Figure 5.12. Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on HRT ................................ ................................ ................................ ............... 80 Figure 5.13. Daily Biogas Production for Psychrophilic, Unregulated and Mesophilic Pilots .... 81 Figure 5.14. Daily Biogas Production for Psychrophilic Pilots ................................ .................... 82 Figure 5.15. Daily Biogas Production for Unregulated Pilots ................................ ...................... 84 Figure 5.16. Daily Biogas Production for Mesophilic Pilots ................................ ........................ 85 xiv Figure 5.17. Tukey Honest Significant Difference Results for the Methane Content based on Temperature ................................ ................................ ................................ ................................ .. 88 Figure 5.18. Tukey Honest Significant Difference Resul ts for the Methane Content based on HRT ................................ ................................ ................................ ................................ ............... 88 Figure 5.19. Methane Content from Weekly Gas Chromatography for Psychrophilic, Unregulated and Mesophilic Pilots ................................ ................................ ............................... 89 Figure 5.20. Methane Content for Psychrophilic Pilots ................................ ................................ 90 Figure 5.21. Methane Content for Unregulated Pilots ................................ ................................ .. 92 Figure 5.22. Methane Content for Mesophilic Pilots ................................ ................................ .... 9 3 Figure 5.23. Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on Temperature ................................ ................................ ................................ ................... 96 Figure 5.24. Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on HRT ................................ ................................ ................................ ............................... 96 Figure 5.25. Hydrogen Sulf ide Content from Weekly Gas Chromatography for Psychrophilic, Unregulated and Mesophilic Pilots ................................ ................................ ............................... 97 Figure 6.1. Anaerobic Digestion Supply Chain system ................................ .............................. 102 Figure 6.2. Biorefinery Classification for Both Systems: a CSTR and a Covered Lagoon ........ 103 Figure 6.3. LCA Scope and Associated Boundaries ................................ ................................ ... 105 Figure 6.4. Global Warming Potential for Various Parameters for a CSTR, a Covered Lagoon and Dairy Cow Manure per FU ................................ ................................ ................................ . 117 Figure 6.5. Global Warming Potential for Various Parameters for a CSTR and a Covered Lagoon per FU ................................ ................................ ................................ ................................ ......... 118 Figure 6.6. Air Acidification Potential for Various Parameters in a CSTR, a Covered Lagoon and Dairy Cow Manure per FU ................................ ................................ ................................ ......... 120 Figure 6.7 . Air Acidification Potential for Various Parameters in a CSTR and a Covered Lagoon per FU ................................ ................................ ................................ ................................ ......... 121 Figure 6.8. Contribution Analysis for Water Consumption in a CSTR, a Covered Lagoon and Dairy Cow Manure per FU ................................ ................................ ................................ ......... 123 Figure 6.9. Contribution Analysis for Water Eutrophication Potential in a CSTR, a Covered Lagoon and Dairy Cow Manure per FU ................................ ................................ ..................... 126 Figure 6.10. CSTR Sensitivity Analysis for Globa l Warming Potential per FU ........................ 127 xv Figure 6.11. Covered Lagoon Sensitivity Analysis for Global Warming Potential per FU ....... 128 Figure 6.12. CSTR Sensitivity Analysis for Air Acidification Potential per FU ....................... 129 Figure 6.13. Covered Lagoon Sensitivity Analysis for Air Acidification Potential per FU ....... 129 Figure 6.14. CSTR Sensitivity Analysis for Water Consumption Potential per FU .................. 130 Figure 6.15. Covered Lagoon Sensitivity Analysis for Water Consumption Potential per FU .. 131 Figure 6.16. CSTR Sensitivity Analysis for Water Eutrophication Potential per FU ................ 132 Figure 6.17. Covered Lagoon Sensitivity Analysis for Water Eutrophication Potential per FU 132 Figure A.1. Average Cumulative Biogas Production in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ................................ ..................... 144 Figure A.2. Methane Content in BMP bottles, Average of Trials 1, 2 & 3 ................................ 148 Figure A.3. Average Pre - digestion Content with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .............................. 152 Figure A.4. Average Post - digestion Content with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .............................. 154 Figure A.5. Average Reductions with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ .......... 156 Figure A.6. Percent Average Reductions with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .............................. 158 Figure A.7. Average Pre - digestion Content with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .............................. 162 Figure A.8. Average Post - digestion Content with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .................... 164 Figure A.9. Average Reductions with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ .......... 166 Figure A.10. Percent Average Reductions with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ .............................. 169 Figure A.11. Average Pre - digestion pH with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ............................. 173 Figure A.12. Average Post - digestion pH with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ............................. 175 xvi Figure A.13. Average pH Change with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ................................ ................................ ................................ ................................ ....... 177 Figure B.1. Average Daily Biogas Production based on HRTs, Average of 3 Conditions ........ 182 Figure B.2. Average Daily Biogas Production based on Temperature Profile, Average of 3 ................................ ................................ ................................ ................................ .......... 183 Figure B.3. Tukey Honest Significant Difference Results for the Daily Biogas Production based on HRT and Temperature ................................ ................................ ................................ ........... 184 Figure B.4. Average Daily Biogas Production in L per kg Initial VS based on HRTs, Average of 3 Conditions ................................ ................................ ................................ ................................ 187 Figure B.5. Average Daily Biogas Production in L per kg Initial VS based on Temperature ................................ ................................ ................................ ....... 188 Figure B.6. Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on HRT and Temperature ................................ ................................ ................. 189 Figure B.7. Average Cumulative Biogas Production based on HRTs, Average of 3 Conditions ................................ ................................ ................................ ................................ ..................... 192 Figu re B.8. Average Cumulative Biogas Production based on Temperature Profile, Average of 3 ................................ ................................ ................................ ................................ .......... 193 Figure B.9. Average Methane Conten t based on HRTs, Average of 3 Conditions .................... 198 Figure B.10. Average Methane Content based on Temperature Profile, Average of 3 .. 199 Figure B.11. Tukey Honest Significant Difference Results for the Methane Content based on HRT and Temperature ................................ ................................ ................................ ................ 200 Figure B.12. Average Hydrogen Sulfide Content based on HRTs, Average of 3 Conditions .... 205 Figure B.13. Average Hydrogen Sulfide Content based on Temperature Profile, Average of 3 ................................ ................................ ................................ ................................ .......... 206 Figure B.14. Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on HRT and Temperature ................................ ................................ ................................ . 207 Figure B.15. Average Total Solid Reductions based on HRTs, Average of 3 Conditions ......... 212 Figure B.16. Ave ................................ ................................ ................................ ................................ ..................... 213 Figure B.17. Tukey Honest Significant Difference Res ults for the Total Solid Reductions based on HRT and Temperature ................................ ................................ ................................ ........... 214 xvii Figure B.18. Average Volatile Solid Reductions based on HRTs, Av erage of 3 Conditions .... 217 Figure B.19. Average Volatile Solid Reductions based on Temperature Profile, Average of 3 ................................ ................................ ................................ ................................ .......... 218 Figure B.21. Tukey Honest Significant Difference Results for the Volatile Solid Reductions based on HRT and Temperature ................................ ................................ ................................ . 219 Figure D.1. Daily Biogas Production for Lab, Unregulated and Mesophilic Pilots ................... 251 Figure D.2. Daily Biogas Production for Lab Pilots ................................ ................................ ... 252 Figure D .3. Daily Biogas Production for Unregulated Pilots ................................ ..................... 252 Figure D.4. Daily Biogas Production for Mesophilic Pilots ................................ ....................... 253 Figure D.5. Cumulative Biogas Production for Lab Pilots ................................ ......................... 253 Figure D.6. Cumulative Biogas Production for Unregulated Pilots ................................ ........... 254 Figure D .7. Cumulative Biogas Production for Mesophilic Pilots ................................ ............. 254 Figure E.1. System Diagram for a Covered Lagoon Anaerobic Digester ................................ .. 255 Figure F.1. System Diagram for a CSTR Anaerobic Digester ................................ ................... 256 xviii KEY S TO ABBREVIATIONS AAP Air Acidification Potential AD Anaerobic Digester ADREC Anaerobic Digestion Research and Education Center BMP Biological Methane Potential CH 4 Methane CO 2 Carbon Dioxide COD C hemical O xygen D emand CSTR Continuous Stirred Tank Reactor DQI Data Quality Inventory EPA Environmental Protection Agency FU Functional Unit GWP Global Warming Potential H 2 S Hydrogen Sulfide HRT Hydraulic Retention Time LCA Life Cycle Analysis LCFS Low Carbon Fuel Standard LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment MSU Michigan State University MSU SCAD Michigan State University South Campus Anaerobic Digester N 2 O Nitrous Oxide xix OLR Organic Loading Rate REC Renewable Energy Credit RFS Renewable Fuel Standard RNG Renewable Natural Gas RPS Renewable Portfolio Standards TS Total Solids VS Volatile Solids WCP Water Consumption Potential WEP Water Eutrophication Potential 1 1. INTRODUCTION 1.1 Problem s tatement Temperature is one of the main factors that affects digester performance. Microbial communities inside the digester are highly sensitive to temperature changes. It is well known in literature that digesters cannot handle more than a 2 o C change within 24 hours, or the microbial communities will be highly disturbed (Schnaars, 2012; Meegoda et al ., 2018) . The analysis of different temperature profiles provides an opportunity to evaluate impacts on not only biogas production is affected, but its quality. Increased data on temperature impacts on anaerobic digestion will aid the technology selection and operational strategy for manure - based systems across the country. Diffe rent regions of the country face varied ambient temperatures season al ; information generated during this research will improve manage ment of energy inputs necessary for the system to properly function. With a better understanding of how temperature profiles can affect anaerobic digestion, it can allow for easier implementatio n of different digester types and its variables that might be affected. B etter understanding of temperature impacts in anaerobic digestion performance will allow for system to be better optimized for system cost, performance, and energy output . The capital costs for anaerobic digester projects are broken down into equipment costs and associated markup factors. The initial equipment cost is more expensive for CSTR when compared to a covered lagoon due to additional investments such as a mixing system, heating system and safety features. Additionally, a CSTR has controlled operations and higher maintenance requirements. Another comparison is the main structural unit of a CSTR and a covered lagoon. A CSTR consists of a tank in which depe nding on sizing can range from $0.40 to $1.00 per gallon. In the AD market, the smaller the tank, the higher the cost. For a covered lagoon, the cover price 2 will be roughly $5 per square foot of lagoon surface. An additional 10% of square foot is accounted for in order to anchor the cover to the ground. Covered lagoons are considered passive systems due that it takes the advantage of using existing systems such as aerobic lagoons and placing an impermeable cover through which biogas is collected. This type of system works as a two for one due that it provides storage as well as treatment of waste. 1.2 Goal and o bjectives The goal of my research is to explore the impact of biogas quantity and quality from covered lagoon anaerobic digesters over a range of tempe ratures and temperature changes . The focus is based on understanding temperature impacts on dairy cow manure digestion , in addition to achieving the three following objectives: (1) Analyze the biodegradability of dairy cow manure with variations in temperatur e. (2) Compare the biogas production from cow manure while trying to represent lagoon conditions with variations in temperature and the lack of supplemental mixing. (3) Compare a life cycle analysis for a covered lagoon system and a complete mix digester. 3 2. LITERATURE REVIEW 2.1 Energy Systems An energy system is defined as a system composed of various technologies and infrastructures utilized to deliver energy services to end users. Throughout the years, energy systems have been highly affe cted by factors such as resource availability, environmental impacts, and technological innovation (Saundry, 2019). According to MIT Professor Richard Schmalensee, the innovation in energy systems is derived from an economic standpoint where it is not base d on the idea of running out of a fossil fuels such as petroleum, but it is about the price increase with the decrease in source availability. In the past, energy systems have focused only on the supply aspect of energy systems but without considered the e nergy demand. The U.S. is one of the highest energy demand countries in the world. With an estimated 2.1% increase in demand per year and availability to expand the energy sector wit h new forms of energy are now being implemented in order to supply the increasing demand in energy. The sustainability of energy supplies is highly dependent on three fac tors: society, environment, and economy. There is two form of sustainability related t energy to obtain or acquire energy sources without causing an unbalance in the three factors mentioned above; while sustainable energy refers to the energy producing system which has achieved optimum impacts in all the three factors mentioned above. The sustainability of an energy systems depends on its availability to reduce the adverse environmental, societa l and economical aspects associated with such systems. In recent years, the sourcing of materials and production of energy from fossil fuels have provided a bigger picture on the environmental, economic, and societal needs to find sustainable energy sour ces. There is a need to 4 find either forms of reducing the emissions from fossil fuels by investing in technologies to trap GHG or invest in forms of renewable energy ( Dunlap , 2015; Kreith, 2015; Tester et al., 2005) . New forms of renewable energy such as wind, solar and biogas have been gaining popularity in recent years. develop renewable energy and utilize energy conservation measures in building, homes and vehic les (Turner, 1999). Renewable energy systems in the U.S. alone avoid the release of 70 million metric tons of carbon dioxide (CO 2 ) if the same amount of electricity was generated by conventional methods (Pena, 1997). In rece nt years, policies have been im plemented to not only reduce emissions in the energy sector, but also in the transportation sector. In 2018, Elon Musk in an interview with Joe Regan presented that a key aspect to change to a greener world is the reformation of the transportation sector a nd transitioning from gasoline to electric and renewable natural gas ( RNG ) vehicles. In California, 37% of greenhouse gases ( GHG ) emissions are correspondent to the transportation sector , and passenger vehicles such as car and buses account for a quarter o f these emissions . Significant reduction of GHG emissions in the transportation sector can be accomplished by the substitution of fleets utilizing conventional fuel into fleets utilizing low carbon fuels. Low carbon fuels, such as RNG , can be obtained from anaerobic digesters. A Division Renewable , RNG today 70% more a s compared to gasoline or diesel . 2.1.1 Fossil Fuels Fossil fuels, such as natural gas, coal, and oil , are non - r enewable resources that formed through millions of years due to the decay of organic matte r that was buried under sedimentation. Under chan ges in temperature and pressure, the organic matter transformed into complex 5 hydrocarbon chains which are used as fossil fuels today . According to the U.S. Department of Energy (DOE), 80% of the domestic energy production per year originated from fossil fu els over the past decade . F ossil fuel production in the U.S. includes natural gas, oil, and coal. Fossil fuels release some of the highest concentrations of GHG when converted to electricity. These GHG are factors that affect the atmosphere and contributing to climate change. The United States is the second highest emitter of GHG) from the conversion and utilization of fossil fuels. In 2017, the United States was the largest country per capita of GHG emissi ons. According to the EPA, 65% of CO 2 emission is observed from burning fossil fuels and industrial processes. Before the industrial revolution, the CO 2 concentration in the atmosphere was approximately 280 ppm . Today, the CO 2 concentration in the atmosphe re is approximately 47% times higher than before the industrial age. Since 2000, the concentration of CO 2 has increased from approximately 370 parts per million (ppm) to 413 ppm , an 11% change in only two decades (NASA , 2021 ). Electricity is defined as the flow of electrical power or charge. The daily human routine consists of utilizing electricity to run common household items such as microwaves and ovens to running massive operations such as factories, hospitals, and airport terminals. Electricity has bec ome crucial in the development of a thriving economy. In a study conducted by Ferguson et al. (2000), wealthy countries have a higher correlation between economic development and electricity consumption in comparison to underdeveloped or monetary unstable countries. In 2019, the total electricity consumption in the United States alone was 3.9 trillion kilowatt - hours (kWh), from which, approximately 65% was obtained from burning fossil fuels such as coal, natural gas, and petroleum, while the remainder was obtained from nuclear energy and renewables, 6 respectively. Natural gas is the largest electricity production with roughly 38% of all electricity was obtained from the processing of this. Natural gas is predominantly methane (CH 4 ), composed of four hydrocarbon atoms and one carbon atom. In the U.S., natural gas is obtained by the process of hydraulic fracturing, also known as fracking. This process consists of drilling into the rock formation where the natural gas is located . The whole process of dr illing the well takes roughly 3 to 5 months, but natural gas and oil can be extracted from a well for roughly 20 to 40 years after drilling. Approximately, 60% of all the oil and natural gas in the U.S. is obtained through this process. However, natural ga s wells also result in GHG emissions, approximately 29% of CH 4 emissions i n 2018 were from natural gas wells (U.S. Energy Information Administration ( EIA ) , 2 020) . 2.1.2 Renewable Fuels According to the Environmental Protection Agency (EPA), only a small percentage of the , 0.8%, are obtained from renewable energy such as solar, wind or geothermal (2019). Although sustainability goals have been implemented globally , there is an inadequate technological development towards the renewa ble energy field. In the U.S. in 2019, only 11% of electricity generation was from renewable sources. 2.1.2.1 Wind, Solar, and Biogas for Electricity Renewable energy forms such as windfarms or solar panels are highly used worldwide as renewable energy s ystems; but they have negative environmental impacts. Wind power, as the name implies, refers to the process of obtaining electricity from air flow. Wind energy is perceived as one of the cleanest renewable energy production systems due that there are no p ollutants or GHG during their operation, but the issues arise during the installation and disposal of the mechanisms. Wind farms are created by installing wind turbines on open land . These farms are 7 highly dependent on wind ; thus , the optim al locations are limited by average wind speed. The optimal locations are often in unpopulated regions or offshore , resulting in transmission challenges to populated regions . Moreover, o ther concerns with wind energy include disturbance to nearby populat ions due to noise and the effects on animal populations. In the U.S., it is estimated that half a million birds are killed with turbine collisions each year ( U.S. Fish and Wildlife Service) . Once the useful life of 20 to 25 years is reached, there are issu es with blade disposal. A typical wind turbine single blade is 120 feet, which is approximately the size of a commercial plane. All other components of a wind turbine can be reused or recycled while the main issue consists in the disposal of the blades. Th e blades are often buried in landfills or burned through pyrolysis ( EIA , 2020 ). Although the popularity of wind power has increased with time, the negative side effects seem to counteract the benefits (Covert et al., 2016). Solar power, as the n ame infers, is the energy obtained from the sun. Solar panels collect sunlight into a photovoltaic cell. The energy is then passed through an inverter and either stored in batteries or used immediately. Solar power has become tremendously popular since installation c osts have almost halved between 2007 to 2019. By 2018, companies such as Apple, Target, Amazon, and Walmart have summed up 1.1 giga watts of total installed solar capacity (Solar Energy Industry Association). It is estimated that solar power accounts for roughly 72 billion kWh in electricity generation in the U.S. (EIA). Even though solar panels are considered environmentally friendly given the lack of GHG emissions during the energy collection process in comp ared to coal and natural gas. T he harshest environmental impacts rely during the manufacturing and disposal process. Solar panels are made of materials such as silicon and plexiglass. During the manufacturing process, various chemicals 8 are utilized in orde r to build the semiconductors and maintain them . The entire life cycle of a solar panel from material sourcing to end - of - life disposal require vast amounts of energy for material sourcing, transformations, installation, and recycling at the end of lifetime . All this process requires intensive labor and heavy machinery (National Renewable Laboratory, 2012). Although taking the full lifetime of the cell provides negative insights, it is an improvement in comparison to fossil fuels when it comes to GHG reducti ons. An upcoming technology , that has gained popularity as a renewable energy source, is biogas. Biogas is one of the major products of anaerobic digestion. Anaerobic digestion (AD) is a natural occurring process in which microorganisms, such as bacteria, breakdown organic material and transform into biogas and digestate . Biogas has two major components: CH 4 and CO 2 (Weiland, 2010) . Biogas is a viable form of renewable energy, due to its CH 4 composition, it can be used as a replacement for power and heat ge neration or upgraded and sold as biomethan e. The U.S. currently has approximately 2,000 operating biogas systems ( Tanigawa , 2017 ). Wastewater treatment plants, landfills and livestock farms have adapted biogas systems due to the high efficiency to convert the organic material into a usable byproduct while also treating organic waste and controlling emissions . The key aspect of AD is that CH 4 is a form of renewable energy with the reduction of GHG . According to Fehrendbach et al., (2008), biogas production through - efficient and environmentally 2.1.2.2 Renewable Natural Gas Renewable natural gas (RNG), also known as biomethane, is the term used to describe biogas that has been purified into CH 4 concentration of 90% or above. Raw biogas can have a CH 4 concentration of 50 to 70% CH 4 , depending on the process and feedstock. Bio gas can be obtained 9 from landfills, wastewater treatment plants, livestock farms and waste management operations. RNG can be distributed by a natural gas pipeline and converted to electricity, thermal applications, or vehicle fuel. Due to the purification process and molecular form , RNG and fossil natural gas are identical (Wiley, 2018). RNG vehicles in compared to diesel or gasoline vehicles are able to reduce the GHG emissions by approximately 75% (AFLEET Tool). The RNG trend is capable of creating negat ive carbon footprints due to the avoidance of CH 4 emissions from current waste management operations in places such as livestock farms (CARB & California Environmental Protection Agency, 2014). 2.2 Policy Drivers 2.2.1 Renewable Portfolio Standards Renew able portfolio standards (RPS), also referred as renewable electricity standards (RES), are regulatory mandates intended to increase energy production from renewable sources such as wind, solar, biomass, etc. and reduce the energy consumption from other so urces such as fossil fuels and nuclear energy. These standards encourage energy suppliers to diversify the grid by introducing a certain value or percentage of energy obtained from renewable sources. The purpose of these policies is to promote the diversif ication of energy generation towards renewable energy sources and encourage a new market beneficial for the owners of the renewable energy source (AgStar, 2019). These policies have been implemented in states such as Michigan, Arizona, and California. RPS policies have been developed across the states individually but there is no electricity policy current in place at the federal level. Each state has various definitions and goals for their policies which brings to variations in the definitions of terms su - 10 A common feature between state to state for this policy is the renewable energy credit (REC) trading system. Renewable energy credits (RECs) are payments that a utility company or other businesses will provide to a renewable energy provider for the exchange of their energy production being placed into the grid . One REC is equivalent to 1 megawatt - hour of electricity obtained from a renewable energy source (Binkley et al., 2011). Under RFS, utility companies are required and/or expected to obtain a number of RECs during a certain time period. RECs work as a trading me chanism due that can be sold or bought within energy companies in the state in order to meet the standard. Nationa lly, the RPS requirements have played a key role in increasing the renewable energy drive within the country and results can be seen not only at state level but federally. It is estimated that more than 50% increase in renewable energy usage in the last de cade can be attributed to the implementation of RPS policies in the majority of Northeast and Mid - Atlantic states (SEIA). 2.2.2 Renewable Fuels Standard Renewable Fuel Standards (RFS) is a federal program that creates incentives to promote the integratio n of vehicle fuels obtained from renewable resources. According to the Renewable amended to the Energy Polic y Act in 2005 by Congress. This policy was later renewed, and it is now known as the Clean Energy Act (CAA). The Environmental Protection Agency (EPA) is the designated entity to establish targets and implements the policy at federal level. The target vol ume is designated from investigating projected trendlines of gasoline and diesel consumption and reviewing the compliance to reach previous years goals. The renewable 11 fuel in question has to be utilized for sectors such as transportation fuel or jet fuel t o be considered. The biofuel needs to have a reduction in GHG when compared to a 2005 petroleum baseline. In previous years, various factors such as government grants and technology limitations have proven to rise challenges when it comes to reaching targe t volumes in the categories mentioned. EPA is in charge of setting the targets of fuel volume required from the following biofuel categories: total renewable fuel, total advanced biofuel, cellulosic biofuel, and biomass - based diesel. The total renewable fu el requirement is composed of two subcategories: conventional biofuel and advanced biofuel. Conventional biofuel is any fuel obtained from feedstocks such as grain and corn. C onventional bio fuel has to demonstrate a reduction of approximately 20% in lifecy cle GHG emissions. Moreover, advance biofuels are composed of two subcategories: cellulose biofuels and biomass - based diesel. A dvanced biofuels are derived from cellulosic or advanced feedstocks such as sugarcane , sugar beet , vegetable oil and others. Cel lulosic biofuels are any diesel fuel substitute obtained from lignin, hemicellulose, or cellulose . These biofuels most demonstrate an approximate reduction of at least 6 0% in lifecycle GHG emissions. Biomass based diesel is created from renewable feedstock s and must demonstrate approximately 50% reduction in lifecycle GHG emissions. Similar to the RECs, utilized for electricity production at the state level, for RFS we can encounter a similar trading mechanism identified as Renewable Identification Numbers (RIN). RIN is a credit obtained by a company when they produce one gallon of renewable fuels. At the end of a time period, the company must demonstrate enough RIN to be compliant with the RFS policy. In similar manner to RECs, RIN can be sold or bought wit hin renewable fuel production companies ( Brackmort, 2020, EPA, DOE) . 12 2.2.3 Carbon Cap and Trade Carbon cap and trade, as the name implies, is the combination of two components when it comes to carbon emissions into the environment. The cap represents the maximum number of GHG emissions set by the government ; while the trade is where the government sells, or issues permits to businesses or entities that are emitters of GHG. Any company that emits GHG as part of their day - to - day operations nee ds one permit for every ton of GHG emitted. These permits are allowed to be traded between companies, as necessary. Due to associating a monetary incentive, companies have made efforts to reduce their GHG emissions. If a company has more permits than what it needs, it is allowed to sell to other companies, therefore providing a value market associated with environmental impacts (Center for Climate and Energy Solutions). 2.2.3.1 Carbon Intensity Carbon intensity is defined as the measurement of GHG emissions associated with transportation fuel. This metric is usually presented in grams of CO 2 per megajoule of energy. When calculating carbon intensity, it is composed of considering the following pr ocesses within its scope: extraction, refinement, distribution, storage, and combustion. In other words, these scores take into consideration a complete life cycle analysis of a specific fuel. 2.2.3.1.1 Model The current model to calculate a CI score is the CA - GREET 3.0 Model and Tier 1 Simplified Carbon Intensity calculators. GREET is the abbreviation for Greenhouse Gases, Regulated Emissions and Energy Use in Transportation. The GREET model is a tool utilized to input data and perform a life cycle analy sis about the environmental impacts of vehicle technologies, fuels, and energy systems. The GREET model can be used to calculate total energy consumption, emission of GHG and air pollutants and water consumption. Department of Energy 13 and Argonne labs partn ered in order to develop this model and continue to introduce improvements with advancements in industry and technology. 2.2.3. 2 California LCFS process In recent years, states have adopted a vast majority of standards and policies in order to reduce GHG emissions into the environment by providing various forms of incentives. A major topic within policies drivers has been vehicle fuel consumption and usage. Companies such as Amazon and Target have adopted the usage of electric vehicles for local and natio nwide deliveries. Even though a single electric truck can be priced around $70,000, states such as California have adopted a policy that is changing the transportation game for companies (Electrek, 2020). This policy is the Low Carbon Fuel Standards (LCFS) . This is an action established in the state of California by the California Air and Resources Board in 2009. This policy has been adopted by states in a similar realm such as Oregon and Washington . LCFS has even been implemented in international countries such as Canada and Brazil. This standard is implemented to decrease the carbon intensity of transportation fuel by providing benefits and income to low carbon and renewable fuel providers. In other words, any industrial vehicle from which CO 2 emissions f all below the standards implemented by the government, will receive a LCFS credit. One LCFS credit represent 1 metric ton of CO 2 prevented from being released into the environment. LCFS credits are genera tes by low car b on or renewable fuel producers and pu rchased by gasoline and diesel production companies. The main contradictions for this process are the high pricing and resources to maintain records for the generation and retail of these ( U.C. Davis Institute of Transportation Studies , 2020 ) . 14 2.2.3.3 How Dairy Manure Fits? Bi o gas derived from dairy manure fits the majority of the state and federally level programs focused on reduction of GHG emissions and renewable energy and/or fuels. Cow manure contains some of the highest concentrations of CH 4 , but also has significant GHG emissions during long - term storage . Dairy manure , as an AD feedstock, can not only reducing GHG emissi ons, but it is also producing a renewable energy source. 2.3 Dairy Manure Management Systems 2.3.1 Overview of Standard Practice A single dairy cow can produce a rough estimate of 68 kilograms of manure per day (ASABE Standards, 2005). Proper m anure man agement results in the collection and eventual land application as a fertilizer without significant impacts on air, soil, or water quality. Manure is collected from dairy barns or loa d ing pens and either directly land applied or stored in anaerobic ponds. Some farms use solid liquid separation prior to storage to improve management and generate bedding or solid fertilizer products. The liquid portion of the manure can be irrigated as a form of fertilizer and crop water source . 2.3.2 Environmental Impacts Current, waste treatment solutions for manure and slurries systems are unfavorable for natural environments due to possible soil pollution and negative environmental impacts, such as contaminating nearby water streams and food crops with pathological enti ties (US EPA, 2018). When poorly managed, animal handling sectors such as farms account for around 18% of GHG emissions in the world, without including other materials such as nitrous oxide and ammonia (Esfandiari, Khosrokhavar & Sekhavat, 2011). In the Un ited States alone, livestock manure 15 management contributes roughly 14% to ammonia emissions which cause acid rain (Eckert et al. 2018). Anaerobic digestion solves these problems through storage, control, and reduction of waste. 2.3.2.1 Emissions generation during lagoon storage Dairy liquid manure storage is one of the major sources of GHG emissions in the agricultural sector. The major GHG produced is CH 4 . In a study conducted by Leytem et al. (2017), six manure storage lagoons in the Western United St ates were analyzed for GHG emissions. The average CH 4 emissions from the lagoons were 22 to 517 kg per day. The variation was due to the monitoring occurring over a full year span and therefore providing variations due to temperature changes with respect to seasonal changes. Western United States due to low humidity there is a high evaporation rate from liquid waste storages, th erefore reflecting higher GHG emissions in comparison to other locations around the country (Grant & Boehm , 2020 ). 2.4 Anaerobic Digestion 2.4.1 Process of Anaerobic Digestion The process of anaerobic digestion consists of four main stages: hydrolysis, acidogenesis, acetogenesis and methanogenesis. In the hydrolysis process, organic materials such as cellulose and hemicellulose are hydrolyzed to simple monomers and oligomers such as soluble sugars or alcohols. Hydrolysis products will be transformed duri ng the acidogenesis process to yield volatile fatty acids that mainly consist of formic, acetic, propionic, and butyric acid. During the acetogenesis and methanogenesis stages, methanogens will utilize formic and acetic acid, while propionic and butyric ac id will be converted into acetic acid by acetogens (Shen et al., 2018). The anaerobic digestion process is driven by the various microbial communities that carry out the four stages mentioned. Most of these communities are highly dependent various factors such as temperature and material availability. There are vast number of bacterial species involved 16 in the process an d each specie will require individual parameters to thrive within the AD process. Different bacterial communities breakdown complex organic molecules by the process of hydrolysis and fermentation. Once simple molecules , organic acids, are available, a spec ific bacterial community, methanogens, utilize volatile solids and produce the main component of biogas: CH 4 and CO 2 . This natural occurring process is found in different environments such as soil and lakes (Meegod et al., 2018). Anaerobic digestion has be en used by human population since the early starts of civilization. In the 17th century, Jan Baptita Van Helmont discovered that flammable gases could evolve from the decay of organic matter. In 1808, Humphrey Davy determines that CH 4 is naturally produced by cattle manure. Approximately, 51 years later, the first anaerobic digestion plant was built in 1859 in India (Zullo, 2016) . 2.4.1.1 Digesters 101 An anaerobic digester is a controlled system that executes this natural process to collect the biogas for future use in energy production. AD systems produce valuable products such as biogas and digestate. Biogas can be combusted onsite to create electricity and heat or refined and utilized as biomethane or RNG . Digestate provides a substitution for inorganic fertilizer introduced into the environment as it includes stable forms of nitrogen and phosphorus readily accessible for soil absorption (Tambone et al., 2010; Weiland, 2010). 2.4.2 Factors influencing Anaerobic D igestion 2.4.2.1 Temperature Anaerobic digesters operate in one of three temperature regimes: psychrophilic ( 15 23 ° C ) , mesophilic ( 3 5 41 ° C ), and thermophilic ( 52 58 ° C ). Psychrophilic temperatures have been presented in unheated digester systems where the temperatures are below 23 ° C . A ccording to Chen 17 and Neibling (2014), microbial degradation of feedstocks within this temperature range stops therefore reducing both biogas quality and quantity. The most common types of digester studied in the United States are mesophilic digesters. Meso philic digesters have been well studied with the use of cow, swine, or chicken manure. Due that mesophilic digesters have been well studied in digestion of manures, the shift in research has been in the analysis of co - digestion processes for this temperatu re profile. Thermophilic temperatures have been well studied in literature as part of municipal waste management systems. Co - digestion is the mi xing of microbial rich, low energy feedstocks like manures with, energy rich feedstocks like food waste and raw carbon wastes. In various studied presented in academics, temperature in the mesophilic range is considered the ideal temperature to promote bacterial activity within the digester. Thermophilic digester is often seen in literature in order to treat waste waters or highly pathogenic sludges. Heating of a complete mix digester will usually occur through a heated water loop which is heated by the waste heat from the electricity production of the digester. The thermal energy demand by the system is derived by four main factors: ambient temperature , f eed rates , d egree of i nsulation and d igester c over t ype . According to Song et al. (2004), heating provides an ideal system that promotes the bacteria to be continuously active and promoting solids destruction. Acetogens and methanogens are crucial bacterial communities that depend in these heating conditions in order to convert the organic material presented as feed into biogas and digestate. Covered lagoon systems are highly understudied due to the difficulty of evaluating them at pilot scale and a lack of commercial system data that is publicly available. The systems are somewhat unpredictability due to the influence of ambient temperatures. Although covered lagoons may not thrive in colder clima tes or psychrophilic as a biogas producing system, it could 18 possibly thrive in different states across the United States where the ground and air temperatures are higher . For example, shown in F igure 2. 1 , the mean earth temperature in States such as Florid a, Texas or Arizona is above 19 o C . As part of my research is to identify a transition between psychrophilic temperatures and mesophilic temperatures in order to create a possible model for biogas quantity and quality. Figure 2. 1 . Mean annual earth temper ature observations at individual stations, superimposed on well - water temperature contours. In 2011, a study was performed a 100 head dairy farm in South Dakota. The farm utilizes a covered lagoon as a flush system for barn output. The lagoon in this study ha d a synthetic cover that was weight ed down by concrete tubes that also serve as the gas collection system. The study presented preliminary results for variations between ambient temperature and effluent temperature at the side and bottom of the lagoon. As seen in Figure 2. 2 , and presented as a preliminary discussion point, even though, there was a variation in temperature recording , the lagoon sludge seemed to maintain a psychrophilic state during the winter season (Darrington & Cortus, 2011). 19 Figure 2 . 2. Measured temperature at the side and bottom of sludge line and ambient temperature at a 100 head farm in South Dakota (Darrington & Cortus, 2011) 2.4.2.2 Mixing Although mixing will not be taken into consideration due to time constraints, this specific parameter will be discussed for further information and recommendations. Mixing the active sludge within the digester allows for all the diverse microbial communities within the digester to destroy complex polymers into monomers and other bacteria such as methanogens and acetogens that destroy this food and convert into biogas which is formed by CH 4 and CO 2 . There are diverse goals and benefits described in literature; but a lack of comparison between non - mixing scenarios and how specifically mixin g does affect biogas quantity and quality. Some of the goals of mixing are: Exposing the microorganisms to the maximum amount of food available Reducing the volume occupied by material settling Preventing the formation of a floating crust layer that will reduce the percolation of biogas Eliminating stratification of the system 20 Although these are the goals of mixing, there is a vast of literature resources that due provide the pros and cons of mi xing. One well defined benefit of mixing provided in literature is that it does provide faster volatile solid reduction ; due that mixing will provide a homogeneous environment for microbial degradation and reducing the potential for upsets within digester due to matter separation . One con with regards to mixing is the high cost of purchasing and maintaining the equipment as well as the energy consumption required for mixing . 2.4.2.3 H ydraulic Retention Time Hydraulic Retention Time (HRT) is defined as the theoretical average time the substrate remain inside the reactor. This parameter is often represented in literature as and is calculated as a ratio between reactor volume and the reactor feed rate. The HRT of the reactor presents a crucial parameter for proper functioning. The HRT in other ways is correlated to the loading rate. A smaller HRT represents a higher volumetric loading rate, while a larger HRT, smaller loading rate (Kim et al, 2013). There have been various studies in research proposing variat ions depending on feedstock and digester size. A key conclusion presented is that, a t low HRTs, methanogens can be washed out from the reactor therefore producing an unfavorable environment for CH 4 production (Velvizhi, 2019). Longer HRTs although have pro ven efficient for higher biogas yields, it provides additional costs and decreasing the process efficiency to handle larger volumes of wastes (Zhang et al., 2006) 2.4.2.4 Organic Loading Rate The organic loading rate (OLR) refers to the amount of organic material fed to the digester per day. The OLR is a ratio of the mass of volatile solids (VS) or chemical oxygen demand (COD) in the feedstock and the reactor volume. The OLR is an indicator of the overall performance of the digester and represents how much reactor space is actually being utilized. The OLR range will be 21 dependent not only on digester type but also on the type of feedstocks introduced into the digester (Ferguson et al., 2016). Total solids (TS) indicate the total mass of the material that is being added daily to the reactor, while VS is the material available for biogas conversion. TS will impact the volatile fatty acids to alkalinity ratio, OLR and the volume of gas production. A high or low feeding rate can lead to poor performance. High fee ding rates can cause an accumulation of volatile fatty acids which will highly impacts the process of methanogenesis (MSU Anaerobic Digester Operator Training, 2019) . In recent studies, it has been deducted that the microbial community within the digester after an overloading event might be adapting and able to increase the loading rate with succession in loading events (Ferguson et al., 2016). Under feeding a system can starve the microbial community of needed energy, resulting in lower activity and bioga s production. 2.4.2.5 Feedstocks Feedstocks is defined as any organic material that can be introduced into a digester and converted to biogas by the microbial community. There is a great range of feedstock available such as animal manure, municipal waste, lignocellulosic material, and food waste . The end goal is feeding the digester substrates that will provide the maximum CH 4 yield possible. Cow manure and swine manure by themselves will provide a reasonable amount of biogas. C attle manure can provide a range of 0.13 - 0.24 m 3 CH 4 per kg of VS; while swine and poultry manure can produce a range of 0.29 - 0.45 and 0.02 - 0.39, respectively ( Díaz - Vázquez et al., 2020) . Co - digestion is the anaerobic digestion of multiple feedstocks within a s ingle AD system. The effects on biogas potential of a specific feedstock will vary highly on its individual chemical composition. Lipids, carbohydrates, and proteins have biogas potentials of 1.42, 0.83 and 0.95 L biogas per g VS ( Alves et al., 2009) . The addition of 20 to 30% materials such as food waste and crop residues are ale to 22 increase methane production by at least 15% when compared to the digestion of manure b y itself ( Lehtomäki et al., 2007). Some biogas potentials for mixtures with cow manure and slaughterhouse blood, out of date beverages and grease trap waste are 0.40, 0.82 and 0.83 mL CH 4 per mg of initial TS (Ma et al., 2017). Although we compare feedstocks against one another, pretreatments, chemical addition , and other processes can highly vary a single feedstocks biogas production. 2.4.2.6 Digester Types Many large - scale farms have adopted anaerobic digestion systems as wa ste management systems within recent years. In the past two decades, as show in Figure 2. 3 , anaerobic digesters have doubled within the U.S. In 2019, there were 245 operational digesters, 9 newly operational, 1 shut down and 32 under construction. From the se trends, 204 are dairy manure digesters. (EPA, 2019) Figure 2. 3 . Anaerobic Digesters Operating in the United States from 2000 to 2019 (EPA , 2019) Across the United States, as shown in Figure 2. 4 , there are 3 major types of anaerobic digesters used: plug flow, complete mix, and covered lagoons. Plug flow and complete mix (e.g. completely stirred tank rectors) digesters are the two most popular systems due to their high 23 biogas productivity. Both sy stems have supplemental mixing or heating and are usually controlled by an operator (Klinghoffer & Castaldi, 2013). Figure 2. 4 . Designs for Operating Anaerobic Digesters in the United States (EPA, 2019) 2.4.2.6.1 Complete Mix Stirred Reactors Complete mi x stirred reactors , also known as CSTR, are mainly used in the bioenergy industry due to their vast availability of data. This system can accept high variety in feedstock ranges and can be widely implemented in various weathers. The CSTR has cylindrical t anks that are constructed from steel or concrete. This system requires supplemental mixing and heating. The main feature of this system includes the tanks, mixers, covers, and heating systems. It typically has a 5 to 20 days hydraulic retention time (HRT). CSTR systems can handle a wide range of influent total solids and agricultural flush. This digester type provides uniformity of temperature, mixing, and substrate concentration (Usack et al., 2012). CSTR systems can function in a variety of climates due t o the closed heating system that accompanies this digester type. 2.4.2. 6 .2 Covered Lagoon A nother popular type of AD system in the United States are covered lagoons. Covered lagoons are underground systems covered by a turf or lining for biogas collection. Typically, these systems are designed to store diluted wastewaters of sludges with less th an 5% TS . Covered lagoon systems do not require supplemental mixing or heating. These lagoons are more common in 24 warmer climates since they are not normally heated and will be inefficient in temperate or cold climates. These systems are still used in colde r climates for digestate storage and odor control. It typically seen within the United States in states such as Arizona or Texas or outside of the United States in countries such as Ecuador and Chile. Moreover, this type of digester can produce enough quan tity and quality of biogas in climates that have elevated year - round temperatures (Penn State Extension, 2013). Table 2. 1 represents a comparison of this system with respect to a CSTR. Table 2. 1. Overall Comparison of a Complete Mix Digester versus a Cover ed Lagoon CSTR Covered Lagoon Typical HRT 5 to 20 days 40 to 60 days TS Range Variable <5% OLR Range 1 to 10 kg COD/m 3 /day 0 to 0.2 kg COD/m 3 /day Insulation System dependent Not typical Supplemental Heating System dependent Not typical Supplemental Mixing Yes (pump or motor) Not typical 2.4.4.2.3 Costs As presented in Table 2 .2 , there is a case study that analyzed different digester types and compared the capital cost and payback period for each one. Presented are the results for the calculated payback periods of each case study. The paper failed to present if the economics reviewed the monetary benefits within its analysis ( AgSTAR Project Profiles) . Table 2 . 2. Case Stud ies regarding Capital Co s ts and Payback Period for Different Diges ter Types Case Study Digester Type Capital Cost Payback Period Tollenaar Holsteins Dairy Complete Mix (CSTR) $1.7 million 10 years Butler Farms Covered Lagoon $650,000 8 to 10 years Lloyd Ray Farms Covered Lagoon $1.2 million N/A Quasar Energy Group Complete Mix (CSTR) $6 million 4 to 6 years 25 The economics regarding an anaerobic digestion facility are not only based on costs, operation and management, and revenue. As seen in Table 2 .2 , the payback period changes vastly across different digester types and sizes. The capital cost will have a wide range due that it is dependent on digester type, sizing, and feedstock available. The operation and management expenses will include all consumables, workers, and maintenance procedures. Equipment mainten ance should be taking into consideration in the initial stages of project proposal and rather facilitating preventative maintenance to keep the proper functioning of pumps, heaters, valves, and others. All though digester costs are high possible forms of r evenue should be taking into consideration as part of the planning. Different forms of revenue will be discussed in the following section (Sheffler, 2018). 2.4.3 Environmental Benefits There are five major benefits to anaerobic digestion which are the following: emission control, pathogen reduction, odor control, waste stabilization and nutrient availability. Through AD systems, there is a reduction in GHG emissions from livestock manure into the atmosphere. Instead of CH 4 being released, it is used for either electricity or fuel (EPA). Odor control and waste stabilization are achieved by inserting an AD system prior to releasing the manure in the storage lagoon. Through AD, the effluent released into the storage lagoon contains more stable organic material than manure itself and less volatile odorants. Nutrients can be retrieved from nature through the AD process. These nutrients are then available in the digestate utilized for land appli cation. Animal wastes and municipal wastes can contain several forms of pathogen and contaminants. The competition with other microbes within the digester can cause pathogen reduction, in contribution, with the presence of organic acids to inhibit pathogen growth ( Wilkie , 2005) . 26 2.4.4 Revenue Anaerobic digestion is an emerging form of renewable energy across the United States and even though digesters cost is high, there are many forms of revenue throughout the byproducts of anaerobic digestion. There is the existence of different markets for an anerobic digester to receive some form of revenue such as digestate, energy or fuel credits and feeding fees. The average pricing for these revenue streams will vary from digester to digester due to location and o perating procedures. One key example of these is feeding fees. Feeding fees are rates charged to a company disposing of waste materials as digester influent. The feeding rates vary on the water content of the materials. FOG (Fats, Oils and Grease) has high amounts of water content therefore it has a low monetary value of $0.10 per gallon ; but forms of dry material with low water concentration for about $12.00 per gallo n. Additionally, digestate can b e sold to nearby farms or composting facilities for approx imately $7.00 per ton (Dr. Dana Kirk , 2020 ) . Some of the markets mentioned previously are RINs and RECs related to the current policy drives at state or federal level. According to the EPA (2020) , the price range for a RIN is between $0.01 and $ 3.50. This value price will be dependent on the source o f the biofuel. RECs prices due to be highly varying between state to state , the prices are affected by changes in policy and the availability of RECs within the state. Between 2014 and 2017, drops in all the states regarding to REC prices could be noted wi th regards to new introductions of renewable energy policies within those years. Most of the current RECs are met through technologies such as wind and solar therefore creating a deviation in the market for the prices. 27 3 . MATERIALS AND METHODS 3 .1 Waste Co llection and Handling Samples were collected and analyzes by the MSU Anaerobic Digestion Research and Education Center (ADREC). 3 .1.1 Anaerobic Filtrate Seed material , also known as filtrate, was collected from MSU South Campus Anaerobic Digester ( MSU SCAD). MSU SCAD is a complete mix digester. The feedstock of the digester consists of a 50/50 mix of cow manure from MSU Dairy Farms and food waste. Effluent from the MSU SCAD is processed through a screw press to separated solids and liquids (filtrate). Filtrate was collected prior to every BMP round and prior to initial pilot seeding. 3 .1.2 Liquid Cow Manure Liquid manure was collected from a dairy farm near Webberville, Michigan. The cow manure was collected from a storage pit post to the manure passi ng through the sand separation system and rotary solid liquid separator . This manure was chosen due to the low number of total solids (approximately 4% TS), making it suitable to compare to manures utilized in lagoon operations. Fresh manure was collected prior to every BMP round and every two weeks for pilot feeding. 3 .1.3 Sample Storage All samples collected were stored in a refrigerator at 4°C. 3 .2 Waste Characterization The raw sample s obtained w ere characterized for pH, conductivity (EC), total solids (TS), and volatile solids (VS). TS and VS analysis performed during this research utilized the EPA 28 accepted Hach methods 8271 and 8276, respectively. For TS, the procedure was modified from a 6 - hour ov en holding time to 24 hours in order to ensure complete drying. The time was also increased from 1 hour to 6 hours for the VS procedure in order to ensure complete sample combustion. pH and EC measurements were tested using an Accumet Excel XL60 meter by F isher Scientific. The pre - and post - BMP digestion and pre - and post - pilot analysis performed included pH, EC, TS, VS, and soluble chemical oxygen demand (SCOD). The SCOD testing consists of obtaining the soluble portion of the sample and the using EPA accep ted Hach Method 8000. The filters utilized to obtain the soluble portion of the sample are presented in table 3 .1 . Table 3 .1 . Filters utilized during the SCOD process Order Diameter Characteristic 1 47 mm Ashless, 41, Whatman 2 47 mm Qualitative, Whatman 3 42.5 mm Qualitative, Whatman 4 42.5 mm Qualitative, VWR 5 42.5 mm Hardened, Whatman 6 47 mm Glass Microfiber Filters, GF/A, Whatman 7 47 mm Glass Microfiber Filters, GF/F, Whatman 8 0.45 µm Microporous membrane, Whatman 3 . 3 Biochemical methane potential Test (BMP) Three round of BMP assays were performed at ADREC during January 2020 to July 2020. For each round, new filtrate and cow manure samples were collected. Samples were collected throughout 2020 and maintained in refrigeration at 4°C. BMPs were performed to evaluate performance differences related to mixing and temperature with respect to ma terial biodegradability . The assay consisted of 5 different BMPs which had the conditions presented in 29 Table 3.2 . All the bottles were maintained at their respective temperatures and mixing conditions during the duration of the experiment. Table 3.2 . Summary of BMP assays with variations in temperature and mixing Categories Temperature Mixing ( ° C) (Y/N) 1 15 N 2 20 N 3 30 N 4 39 N 5 39 Y 3 . 3 .1 Set - up The BMPs were set up utilizing the procedure and all analyses are obtained from the paper presented by Faivor and Kirk (2011). After performing raw characterization on both filtrate and cow manure, blends were created to have an initial VS:VS ratio of 2:1 in terms of filtrate: cow manure. All blends are set up in triplicates including three bottles only containing seed material, which serve as a control group. For purposes of this experiment, a positive control group was set up. C ellulose microcrystalline was utilized as a positive control group. The filtrate bottles allow us to calculate the biogas production by subtracting the biogas production of inoculum and materials minus inoculum itself. However, a control group such as filtrate does provide enough i nformation to verify inoculum performance; therefore, a positive control must be prepared. The positive control allows to verify the fitness of the inoculum for testing . A total amount of 300 mL per bottle is prepared. From that 300 mL blend, 150 mL was sealed in a bottle with a septum and an aluminum crimp, while remaining 150 mL was retained for pre digestion analysis. The pre digestion samples we re preserved in a refrig erator at 4°C. All blends we re prepared o n a mass basis rather than a volume basis. The bottles are flushed with nitrogen at a flowrate of 750 mL per minute for 10 minutes and placed into their respective temperature 30 profiles. After two hours, gas wa s rele ased from the bottles and time wa s recorded as starting time. The BMP bottles were sealed and monitored for 30 days. 3 . 3 .2 Operation and Monitoring Gas production was measured either daily or every other day using a 10 - , 30 - , 50 - or 100 - mL glass syringe. Syringe volume selection was based on prior day reading or estimated guess between time elapsed between previous reading. Gas composition was analyzed using a HayeSep D column in an SRI 8610 Gas Chromatograph with a flame ionization detector (FID) and ther mal conductivity detector (TCD). Gas chromatography was performed weekly , and the following parameters were measured: CH 4 , CO 2 , N 2 and H 2 S. The sample was taken from each individual performed. After 30 days, the bottles were uncapped, and post digestion analysis were performed in the digested sample. 3 .3 .3 BMP Calculations Raw gas is measured in a lab maintained at 22°C and is assumed saturated. Gas is normalized for standard tempera ture (0°C) and pressure (1 atm) (STP) using the Equation 1. (1) G STP gas normalized for standard temperature and pressure, mL G R raw gas production, mL 0.897 STP conversion factor for conditions in East Lansing, MI Each filtrate and DI water using Equation 2. (2) G N normalized gas production, mL 31 Control 1 biogas production from control 1, mL Control 2 biogas production from control 2, mL Control 3 biogas production from control 3, mL The VS content is calculated for the bottles based on the VS of the raw sample using Equation 3. (3) VS N volatile solids content in the bottle, mg VS R volatile solids content of the raw sample, mg/kg S mass of sample in the bottle, g 1/1000 conversion factor, kg/g The biogas content of the respective bottles (BMP i ) was found by using Equation 4. (4) i bottle number The triplicate bottles are then averaged using Equation 5. (5) BMP biochemical methane potential, L biogas/kg initial VS 1/1000 conversion factor, L/mL 10 6 conversion factor, mg/kg 32 3 . 4 Pilot systems design , operation, and analysis. T hree temperature profiles were explored during the pilot systems. As seen in Table 3.3 , three temperature profiles were evaluated in duplicate . The pilot systems were not mixed . Pilots 1,2, 5 and 6 were maintained in control temperature rooms while pilots 3 and 4 were left in an uncontrolled temperature area. The data was collected for three, 45 - day HRT . Table 3.3 . Pilots Temperature Profiles Pilots Temperature 1 19°C 2 3 9 °C to 28 ° C 4 5 39°C 6 3. 4 .1 Pilot Vessel/Structure T he pilot systems are cylindrical shaped made from polyvinyl chloride (PVC) piping with dimensions of 6 - inch diameter and 7.5 - inch height . A PVC flange socket was fixed as the top of the digester. A gasket was placed on the flange socket and a flange cap was bo l te d down. A wall PVC pipe cap was utilized at the bottom of the digester. In the digester flange cap, three holes were drilled in order to insert U tube waste pressure gage, gas output line and gas bag. Both gas output lines were connected to valves in order to open and close when needed. The gas output line was connected to a Wet Tip Gas Meter with a digital counter. The tips counted represented a specific volume of gas produced . This volume was known due to calibration procedures for these systems. A third ball cap valve was drilled and inserted in the middle of the pilot in order to feed and waste. The set up for the pilot systems is presented in Figure 3.1. 33 Figure 3.1. Pilot Set Up 3. 4 .2 Pilot Preparation All pilots were cleaned with phosphorus free soap and rinsed with DI water. All pilots were tested for water leakage. The tip meters were calibrated utilizing a 150 mL syringe and introducing air until a consistent air volu me would produce a tip. Connecting lines from the pilot to tip meter lines were measured in order to make sure all lines had an equal length of 53 cm. 3 . 4 . 3 Pilot Set Up and Seeding Filtrate was collected from MSU SCAD. The following analysis were performed on the sample: pH, conductivity (EC), total solids (TS) and volatile solids (VS). A volume of 2,000 grams was weighted and utilized as inoculum for all six pilots. The six digesters were filled on Friday, July 24, 2020 . The pilots were allowed to stabilize over the weekend while issues with leakage and tip meters were fixed . On Monday, July 27 , 2020, after all pilots had stable pressures and readings, the pilots received their first feeding. 34 3. 4 . 4 Pilot Feeding Cow ma nure from a local dairy farm was collected bi - week ly and used as feeding during a two - week period. The following analysis were performed on the sample after every collection: pH, EC, TS and VS. Based on a 45 - day HRT, manual feedings were scheduled three ti mes a week, every Monday, Wednesday, and Friday. 3. 4 . 4 .1 Feeding Volume Feeding volume was calculated using Equation 6 . F v feeding volume, mL/day V reactor volume, mL hydraulic retention time, days t days of the week allotted for feeding, days 3. 4.4 .2 Calculations 3. 4.4 .3 Procedure During the first week, raw cow man ure was fed daily. After the first week of monitoring system stability, pH and gas production , feeding was reduced to 3 days a week: Monday, Wednesday and Friday with feedings all occurring roughly around 1 PM. All six pilots were wasted ( 6 ) 35 and respectively f ed 104 g of raw cow manure. The wasting and feeding occurred by weight measurements rather than by volume in order to minimize reading error between lab personnel. As a necessary precaution, the waste was measured for pH during every schedule feed. If pH w as below 7.00, then the respective pilot would be buffered with 5% bicarbonate solution. 3. 4 . 5 Pilot Monitoring Pilots are monitored every day for pressure and gas production . During feedings, time, temperature, number of tips and pressure control was recorded. 3. 4 . 6 Digestate The digestate collected was tested for pH and EC with every feeding. The digestate collected every Wednesday was tested for TS, VS and SCOD. All remain ing diges tates w ere preserved in a refrigerator at 4°C categorized by pilot and by date. 3 . 4 . 7 Gas 3. 4 . 7 .1 Gas Production Cumulative gas production was calculated using Equation 7 . G C cumulative gas production, mL T number of tips C calibration of tip meter, mL 3. 4 . 7 .2 Gas Analysis Gas analysis was performed weekly to record N 2 , CH 4 , CO 2 , and H 2 S. To collect a sample for GC analysis a 5 mL SGE Analytical Science syringe was used. The syringe was connected to the gas sampling port on the gas output line before the tip meter. Once connected, the syringe was ( 7 ) 36 flushed by pulling and plunging slowly three times. Five mL of sample was then drawn into the syringe and the syringe was connected to the gas chromatograph. This procedure was rep eated three times for each individual pilot. 3. 5 Statistical Analysis Statistical analysis was performed in the BMP and pilot data collected. A one - way ANOVA was utilized to calculate statistical parameters in the BMP data, while a two - way ANOVA was performed in the pilot data utilizing the analytical soft ware R - 4.0.3 . For the BMP data, a one - way ANOVA was performed for each temperature profile using the aov funct ion. The following parameters were analyzed utilizing the software for the BMP: cumulative gas production, CH 4 concentration, and pre, post, destruction, and reduction of TS and VS . The codes utilized for pilot data can be found in Appendix A . For the pilo ts, t he analysis was performed for each HRT (1, 2, & 3) and the variations in temperature ( L, B, H) using the R function aov. The following parameters were analyzed utilizing the software for the pilots : daily gas production, daily gas production per kg of vs, CH 4 concentration, hydrogen sulfide concentration, TS reduction and VS reduction. The codes utilized for pilot data can be found in Appendix B. On all the ANOVA results, the Tukey pairwise comparison was performed to find statistically significant differences between the various operational parameters via the R function TukeyHSD . 37 4. CHARACTERIZATION AND BIOCHEMICAL METHANE POTENTIAL 4.1 Characteristics of Raw Samples Filtrate and dairy cow manure were collected prior to every BMP trial between the months of January and June. M icrocrystalline cellulose was utilized during the trials to provide a positive control. Raw samples were tested for TS and VS. Table 4.1 present s the average with the r espective standard deviations for the samples obtained. The number of samples (n) were averaged together to obtain the results presented in T able 4.1 . For the filtrate samples , the TS ranged from 38,655 to 54,900 mg per L, while the VS ranged from 24,770 to 38,780 mg per L. The TS in manure samples ranged from 28,780 to 50,075 mg per L , while the VS ranged from 20,405 to 32,450 mg per L. Table 4.1 . Raw Character ization Sample TS VS TS VS n (mg/L) (mg/L) (mg/kg) (mg/kg) Seed 48,198±5,514 32,497± 5,710 47,922±5,676 32,318±5,799 9 Microcrystalline cellulose 1 ,017,250±31,419 1,017,207± 31,455 958,989±50 958,947±58 3 Cow Manure 37,157±9,102 24,963±5,237 37,035±9,065 24,881±5,214 9 4.2 BMP Test Results BMP was performed in order to compare the anaerobic biodegradability of dairy cow manure at different temperature profiles. The liquid cow manure was tested along with a control and a positive control in three separate BMP trials. The positive control was utilized as a form to assure the appropriate performance of the inoculum for BMP testing. Each BMP was tested in triplicate during each trial. All the statistical analysis A. Additional data tables regarding individual tri als are presented in Appendix C. 38 4.2 .1 Pre and post digestion analysis Pre - and post - digestion analys es w ere carried out individually on every BMP bottle . The pre - and post - digestion analys e s serve d as a comparison for the anaerobic biodegradability of dairy cow manure at different temperature profiles . T able 4.2 shows the pre - and post - digestion TS content, the TS reduction, and the percent reduction . The number of samples (n) were averaged together to obtain the results presented in table 4.2 and tabl e 4.3 . The pre - TS for all runs was approximately 13,000 mg per L . The post - TS ranged between 10,000 to 13,000 mg/L. The percent reduction average for 15°C n on - m ixed , 20 °C n on - m ixed , 30 °C n on - m ixed , 39 °C n on - m ixed , and 39 °C m ixed were 5, 12, 16, 18 and 21%, respectively. Table 4.2. Pre - and post - digestion TS content in BMP bottles , Average of Trials 1, 2 & 3 Sample Pre - digestion Average ± Std. Dev. (mg/L) Post - digestion Average ± Std. Dev. (mg/L) Reduction Average ± Std. Dev. (mg/L) Reduction Average ± Std. Dev. ( % ) n 15 ° C, Non - Mixed 13,255 ± 2,055 12,651 ± 2,110 604 ± 260 5 ± 2 18 20 ° C, Non - Mixed 13,804 ± 2,042 12,229 ± 2,245 1,575 ± 606 12 ± 5 18 30 ° C, Non - Mixed 13,342 ± 1,659 11,142 ± 1,449 2,200 ± 448 16 ± 2 18 39 ° C, Non - Mixed 13,356 ± 1,547 10,910 ± 1,138 2,446 ± 667 18 ± 4 18 39 ° C, Mixed 13,371 ± 1,795 10,507 ± 1,210 2,864 ± 846 21 ± 4 18 Figure 4.1 presents the p ercen t average reduction with the respective standard deviation for the TS reduction presented in Table 4. 2 . The letter category A through E represent 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39°C mixed , respectively. Mesophilic temperatures provided a greater TS reduction than the psychroph ilic temperatures. The highest reduction was on the 39 ° C mixed BMP followed by 39 °C non - mixed and 30 ° C non - mixed, respectively. 39 Figure 4.1. Percent Average Reductions with Standard Deviations for Total Solids in BMP bottles , Average of Trials 1, 2 & 3 Ta ble 4.3 shows the pre - and post - digestion VS content, the VS reduction, and the percent reduction. The pre - VS for all runs was approximately 9,400 mg per L. The post - VS ranged between 6,000 to 9,000 mg per L. The percent reduction average for 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39°C mixed were 9, 17, 25, 28 and 32 %, respectively. Table 4.3. Pre - and post - digestion V S content in BMP bottles , Average of Trials 1, 2 & 3 Sample Pre - digestion Average ± Std. Dev. (mg/L) Post - digestion Average ± Std. Dev. (mg/L) Reduction Average ± Std. Dev. (mg/L) Reduction Average ± Std. Dev. ( % ) n 15°C, Non - Mixed 9,498 ± 1,678 8,741 ± 1,842 757 ± 232 9 ± 4 18 20°C, Non - Mixed 9,871 ± 1,752 8,279 ± 1,807 1,591 ± 411 17 ± 5 18 30°C, Non - Mixed 9,430 ± 1,490 7,032 ± 1,161 2,398 ± 401 25 ± 2 18 39°C, Non - Mixed 9,446 ± 1,393 6,719 ± 1,046 2,727 ± 427 28 ± 2 18 39°C, Mixed 9,490 ± 1,600 6,447 ± 944 3,043 ± 746 32 ± 3 18 40 Figure 4. 2 presents the percent average reduction with the respective standard deviation for the V S reduction presented in Table 4. 3 . The letter category A through E represent 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39°C mixed, respective ly. Similar observations are presented with respect to TS reduction, t he highest reduction was on the 39°C mixed BMP followed by 39°C non - mixed and 30°C non - mixed, respectively. Figure 4.2. Percent Average Reductions with Standard Deviations for Volatile Solids in BMP bottles , Average of Trials 1, 2 & 3 Table 4.4 contains the pre - and post - digestion average pH characteristics and change presented between samples for all three trials . The ideal pH for anaerobic digestion is between 6.8 and 7. 2. Any pH below 6.8 indicates inhibition in biogas quantity and quality due to the presence of an acidic environment unfavorable towards the methanog enic microbial community . The pre - and post - digestion samples for all three trials had pH readings in the optimal range for anaerobic digestion . T herefore, indicating a stable environment during the BMP test , which indicates no occurrence of inhibition correlated to temperature and lack of mixing (Liu et al., 2008) . 41 Table 4.4 . Pre - an d post - digestion pH in BMP bottles , Average of Trials 1, 2 & 3 Sample Pre - digestion Average ± Std. Dev. Post - digestion Average ± Std. Dev. Change Average ± Std. Dev. n 15°C, Non - Mixed 7.62 ± 0.09 7.17 ± 0.19 0.44 ± 0.11 9 20°C, Non - Mixed 7.57 ± 0.08 7.14 ± 0.15 0.43 ± 0.08 9 30°C, Non - Mixed 7.59 ± 0.11 7.30 ± 0.20 0.30 ± 0.16 9 39°C, Non - Mixed 7.58 ± 0.13 7.33 ± 0.17 0.25 ± 0.08 9 39°C, Mixed 7.55 ± 0.15 7.29 ± 0.21 0.26 ± 0.10 9 Figure 4. 3 presents a visual representation of the average pH change with the respective standard deviations for the VS reduction presented in Table 4 . 4 . The letter category A through E represent 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39 °C mixed, respectively. Figure 4.3. Average pH Change with Standard Deviations in BMP bottles , Average of Trials 1, 2 & 3 The statistical analysis presented in Appendix A , Section A.3 , Section A.4 and Section A.5 , provide a better insight in the correlation between the data obtained for all trials and the 42 characteristics analyzed. Table 4.5 contains the results of the one - way ANOVA performed for the percent TS reduction for all three BMP trials. There was a signifi cant effect of TS reduction on the BMP trials based on temperature at the p< 0 .05 level for the five conditions [F ( 4 , 40 ) = 26.46 , p = 0.000 ] . Table 4. 5 . One Way ANOVA Results for the Total Solids Reduction in BMP bottle s Df Sum Sq Mean Sq F Value Pr ( > F ) Temperature 4 1,517.6 379.4 26.46 0.000 Residuals 40 573.6 14.3 Figure 4. 4 shows the results of the Tukey statistical tests utilized as a pairwise comparison of TS reductions between BMP conditions . The Tukey analysis for the TS reductions presen t no significant difference (p value>0.05) when comparing 30°C non - mixed; 39°C non - mixed; and 39°C mixed . Any confidence intervals that do not contain 0 provide evidence of a statistical difference in the groups. Figure 4. 4 . Tukey Honest Significant Difference Results for the percent TS reduction , Average of Trials 1, 2 & 3 43 Table 4. 6 presents the results of the one - way ANOVA performed for the VS reduction for all three BMP trials . There was a significant effect of VS reduction on the BMP trials based on temperature at the p< 0 .05 level for the five conditions [F ( 4 , 40 ) = 63.7 , p = 0.000 ] . Table 4.6. One Way ANOVA Results for the Volatile Solids Reduction in BMP bottles Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 4 3,263 815.6 63.7 0.00 0 Residuals 40 512 12.8 Figure 4. 5 provides the results of the Tukey statistical tests when comparing each BMP condition with respect to one another for V S reductions. The Tukey analysis for the V S reductions provided in summary no significant difference (p - value >0.05) when comparing 30°C non - mixed to 39°C non - mixed; and 39°C non - mixed to 39°C mixed. There was a statistical significance when comparing 30°C non - mixed to 39°C mixed . Figure 4. 5 . Tukey Honest Significant Difference Results for the percent VS reduction , Average of Trials 1, 2 & 3 44 Table 4.7 presents the results of the one - way ANOVA performed for the pH change for all three BMP trials. There was a significant effect of pH on the BMP trials based on temperature at the p< 0 .05 level for the five conditions [F ( 4 , 40 ) = 6.069 , p = 0 .000 ] . Table 4. 7 . One Way ANOVA Results for the pH Change in BMP bottles Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 4 0.3082 0.07706 6.069 0.00 0 Residuals 40 0.5079 0.01270 Figure 4 .6 presents the results of the Tukey statistical tests when comparing each BMP condition with respect to one another for pH changes . The Tukey analysis for the pH change provided no significant difference (p value >0.05) for the pairwise comparison of 30°C n on - mixed; 39°C non - mixed; and 39°C mixed. Figure 4. 6 . Tukey Honest Significant Difference Results for the pH change , Average of Trials 1, 2 & 3 45 4. 2.2 Gas Production The average cumulative biogas production from the BMP test demonstrated that during a 30 - day test is presented in T able 4. 8 . The average cumulative biogas production in L per kg initial VS for 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39°C mixed were 86, 168, 440, 475 and 448 , respectively. As presented in T able 4. 8 , 39 ° C non - mixed produced relatively closely the same biogas volume as 39 ° C mixed. The 30 ° C non - mixed BMP produced approximately 80 mL less of biogas volume when compared to 39 ° C either mixed or non - mixe d. Table 4. 8 . Cumulative Biogas Production in BMP Bottles , Average of Trials 1, 2 & 3 Sample Cumulative Biogas Production ± Std. Dev. (mL ) Cumulative Biogas Production ± Std. Dev. (L/ kg Initial VS ) n 15°C, Non - Mixed 37 ± 20 86 ± 8 58 20°C, Non - Mixed 71 ± 43 168 ± 8 58 30°C, Non - Mixed 232 ± 138 440 ± 14 58 39°C, Non - Mixed 316 ± 133 475 ± 40 61 39°C, Mixed 307 ± 130 448 ± 29 61 Figure 4. 7 presents the average cumulative biogas prod uction in m L with the respective standard deviations for the values presented in Table 4. 5 during all three trials . The letter category A through E represent 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C no n - mixed, and 39°C mixed, respectively. 46 Figure 4. 7 . Average Cumulative Biogas Production in BMP bottles , Average of Trials 1, 2 & 3 Table 4. 9 presents the results of the one - way ANOVA performed on the data for the three BMP trails . There was a significant effect o n the cumulative biogas production for the BMP trials based on temperature at the p< 0 .05 level for the five conditions [F ( 4 , 276 ) = 92.67 , p = 0.000 ] . Table 4. 9 . One Way ANOVA Results for the Cumulative Biogas Production in BMP bottles Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 4 4 175 , 174 1 043 , 794 92.67 0.000 Residuals 276 3 108 , 698 11 , 263 Figure 4.8 presents the Tukey statistical analysis with respect to the cumulative biogas production. From the analysis , there seems to be no statistical significance (p - value>0.05) between the mesophilic conditions with or without mixing. 47 Figure 4.8. Tukey Honest Significant D ifference Results for the Cumulative Biogas Production , Average of Trials 1, 2 & 3 Figures 4. 9 , 4. 10 and 4. 11 present the cumulative biogas plot line s for each of BMP trials performed . Gas volumes are corrected for change in temperature. Gas is counted at 22 ° C and corrected to STP (0 ° C, 1 atm) by utilizing Equation 1 in Section 3 . As shown in the figures, the plot line s for 30 ° C non - mixed , 39 ° C non - mixed and 39 ° C mixed provided very similar results with respect to biogas production. Therefore, pr ovide intriguing results if whether 39 ° C is actually necessary for the ultimate production of biogas. 48 Figure 4. 9 . Cumulative Biogas Production (Average of Triplicates) for Tri a l 1 Figure 4. 10 . Cumulative Biogas Production (Average of Triplicates) for Trial 2 0 50 100 150 200 250 300 350 0 100 200 300 400 500 600 700 800 Cumulative Biogas Production (mL) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non Mixed) 39 C (Mixed) 0 100 200 300 400 500 600 0 100 200 300 400 500 600 700 800 Cumulative Biogas Production (mL) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non Mixed) 39 C (Mixed) 49 Figure 4. 11 . Cumulative Biogas Production (Average of Triplicates) for Trial 3 4. 2.3 Methane Concentration The CH 4 concentration was measured using gas chromatography as explained in Se ction 3.3. 2. Table 4. 10 presents the average CH 4 concentrations , and the standard deviations collected during the 30 - day BMP trial. The average methane content for 15°C non - mixed, 20°C non - mixed, 30°C non - mixed, 39°C non - mixed, and 39°C mixed were 21, 34, 53, 54 and 54 %, respectively. Table 4. 10 . Methane Concentration in BMP bottles , Average of Trials 1, 2 & 3 Sample Methane ± Std. Dev. ( % ) M in (%) M ax (%) n 15°C, Non - Mixed 21 ± 10 3 39 12 20°C, Non - Mixed 34 ± 12 16 54 12 30°C, Non - Mixed 53 ± 3 44 57 12 39°C, Non - Mixed 54 ± 2 51 58 12 39°C, Mixed 54 ± 2 51 58 12 0 50 100 150 200 250 300 350 400 450 500 0 100 200 300 400 500 600 700 800 Cumulative Biogas Production (mL) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non Mixed) 39 C (Mixed) 50 Figure 4.8 presents the average methane content the average CH 4 concentrations, and the standard deviations collected during the 30 - day BMP trial s . The BMPs samples at mesophilic temperatures with or without mixing (30 ° C non - mixed, 39 ° C non - mixed and 39 ° C mixed) produced the highest values of CH 4 concentrations with similar standard deviations, while the BMPs at psychrophilic temperatures without mixing provided lower concentrations of CH 4 . Figure 4. 12 . Average Methane Content in BMP bottles, Average of Trials 1, 2 & 3 Table 4.11 presents the re sults of the one - way ANOVA performed on the data for the three BMP trails. There was a significant effect of methane content on the BMP trials based on temperature at the p< 0 .05 level for the five conditions [F ( 4 , 55 ) = 51.7 , p = 0.000 ] . Table 4. 11 . One Way ANOVA Results for Methane Content in BMP bottles Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 4 10,725 2,681.3 51.7 0.000 Residuals 55 2,825 51.9 Figure 4.13 presents the results of the Tukey statistical analysis performed between BMP conditions for methane content. Based on the results shown from the Tukey analysis, there seems 51 to be no statistical significance (p - value>0.05) between the mesophilic conditions with or without mixing; additionally, there is a significant difference (p value<0.05) between 15 ° C non - mixed and 20 ° C non - mixed. Figure 4.13. Tukey Honest Significant Difference Results for the Cumulative Biogas Production , Average of Trial s 1, 2 & 3 Figures 4. 14 , 4. 1 5 , and 4. 1 6 present the average CH 4 concentrations with standard deviations for each of the BMP trials. As shown in the figures, the plot line s for 30 ° C non - mixed, 39 ° C non - mixed and 39 ° C mixed provided very similar results with respect to CH 4 concentration . The 15 ° C non - mixed and 20 ° C non - mixed provided an increasing trend with respect to time. 52 Figure 4. 14 . Biogas Methane Content (Average of Triplicates) for Tri a l 1 Figure 4. 1 5 . Biogas Methane Content (Average of Triplicates) for Tr ia l 2 0 10 20 30 40 50 60 70 0 100 200 300 400 500 600 700 800 Methane Concentration (%) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non Mixed) 39 C (Mixed) 0 10 20 30 40 50 60 70 0 100 200 300 400 500 600 700 800 Methane Concentration (%) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non mixed) 39 C (Mixed) 53 Figure 4. 1 6 . Biogas Methane Content (Average of Triplicates) for Tri a l 3 4.3 Discussion Performance data is limited on unheated and unmixed covered lagoon digesters . The lack of supplemental heating or mixing has created a misconception that there is reduction in biogas quantity and quality , therefore favoring other digester types such as the CSTR . Digesters such as the CSTR require supplemental mixing and heating systems according to standard practice. B y including supplem ental heating and mixing systems , the capital and operating costs can escalate t o a great extent therefore creating deter to investors . The vast monetary investment provides unattractiveness to this waste management solution if covered lagoons are not pres ented as a possible solution. Digester temperatures as discussed previously have three major categories: psychrophilic, mesophilic, and thermophilic. Thermophilic temperature was not studied in this research due that is mainly utilized in wastewater treat ment plants. Mesophilic digesters have been heavily studied 0 10 20 30 40 50 60 70 0 100 200 300 400 500 600 700 Methane Concentration (%) Lapsed Time (Hours) 15 C (Non Mixed) 20 C (Non Mixed) 30 C (Non Mixed) 39 C (Non Mixed) 39 C (Mixed) 54 in literature and throughout literature 37 ° C to 39 ° C has been presented not only as the ideal temperature for digester operations but it has become standard practice in the field, resulting in the lack of research for temperature variations. Standard BMP protocols are considered ideal scenario situations due that they are constantly mixing and maintained at constant temperatures of approx. 39 ° C during the 30 - day trail . BMP trials have been used mainly to determine the ideal biodegradability of the material. For purposes of this analysis, two parameters were modified: temperature and mixing. During the trials, constant mixing for was maintained for one BMP category while the remaining four ca tegories were maintained non - mixed. The key reason for doing this is analyzing the impacts of temperature and mixing in the biodegradability of dairy cow manure . Therefore, creating a preliminary analysis about whether the effects of temperature and mixing in anaerobic digestion do support the ideology of 39 ° C and constant mixing is ideal for biogas acquisition. Four factors were analyzed between the five categories: pH, TS and VS reduction, biogas quantity and quality. All samples demonstrated to be anaero bically biodegradable , but there were key differences discovered between psychrophilic and mesophilic temperatures with respect to biogas quality and quantity. For all the samples, pH was maintained in the ideal ranges and therefore discarding the idea of negative effects on pH based on temperature. TS and VS reduction s provide clear answer with respect to the biodegradability of the material. T he reduction in TS was above 10% for four categories: 20°C non - mixed, 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. The highest TS reduction occurred at 39°C mixed. The statistical analysis demonstrated no significant difference (p - value>0.05) between 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. TS redu ction did occur in all the samples, therefore, providing the basis for biogas to be produced. It can be concluded that under anaerobic conditions 55 between psychrophilic and mesophilic temperatures, destruction of TS can occur. There is variation in the conc entrations destroyed ; but above 20°C, the destruction seems to be relatively similar even with higher variations in temperature. The VS reduction was highest for 39 ° C mixed an d followed subsequently by 39 ° C non - mixed, 30 ° C non - mixed, 20 ° C non - mixed, 15 ° C n on - mixed, respectively. The reduction in VS was above 10% for four categories: 20°C non - mixed, 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. The highest VS reduction was presented in 39°C mixed. The statistical analysis demonstrated no significant differ ence (p value>0.05) between 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. All the bottles precented a percentage of VS reduction and it presented an increase with increase in temperature and the addition of mixing. The destruction of VS demonstrates the ability to produce biogas; therefore, biogas production is still possible between mesophilic and psychrophilic variation Cumulative biogas production was observed for the scenarios during this experiment. The categor y with highest production was 39°C non - mixed followed by 39°C mixed and 30°C non - mixed, respectively. The psychrophilic temperatures, 15°C and 20°C, presented approx. 25% or less biogas production than bottles maintained at mesophilic temperatures with or without mixing. According to the statistical analysis, there was no statistical significance (p - value>0.05) when comparing 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. This indicates that the cumulative biogas production was following similar performanc e. The plot line s presented in this section also provide a visual perspective of both numerical and statistical analysis. The biogas quality observed in all the mesophilic bottles was above 45% CH 4 , which is considered the ignitable minimum for biogas. Wh en comparing 30°C non - mixed, 39 °C non - mixed, and 39°C mixed, all these bottle categories produced an average of approx. 53% of CH 4 56 concentration with standard deviation of approx. 2%. P sychrophilic bottles, 15°C and 20°C, pro duced average CH 4 concentrations below the 45% threshold. Although CH 4 is still available within the sample, lower quality biogas would require higher energy inputs to upgrade into a gas sample of 90% CH 4 . A parameter altered from the standard BMP scenarios was mixing. Wh en comparing the results obtained from this analysis, in an ideal scenario such as BMPs, mixing appears to not provide a difference in biogas quantity and quality when compared between 30 ° C and 39 ° C with or without mixing. mixing appeared to provide a greater reduction in TSVS concentrations at higher temperatures. Mixing for purposes for this experiment was treated as a binary scenario. In industry, mixing throughout digesters depending on type and operational parameters is treated as an individualized operational parameter specific to that individual digester. It is difficult from three trials and treatment as a binary component to produce a final decision on whether mixing is crucial or not for biogas quality and quantity. Due to industry scenarios, expert advice and time constraints , mixing will not be utilized as a parameter in the pilot scale testing. Mixing might not have an influence in small ideal scenarios; but it might be a parameter to consider in larger scale rese arch with additional testing parameters and requirements. For purposes of subsequent sections, mixing was not considered or implemented during the testing to in the end be able to compare and focus on temperature variations in systems without supplemental heating systems to represents similarities to covered lagoons. With the results presented in section 4, the idea that an additional 9 ° C within digester temperature might not be necessary in order to obtain higher biogas quantity and quality. The BMPs provided no significance difference (p - value>0.05) when comparing temperature profiles within the mesophilic range. For a C ST R, h eating req uirements for a digester is an economic 57 intensive activity and therefore if 39 ° C are not necessarily required for ultimate biogas quality and quantity , lowering the digester to a lower temperature would reduce the energy inputs and operational costs of the system . The energy requirement for h eat requirements would be reduce d by 4 0% if a digester were run from 35 ° C to 22 ° C ( Arikan, Mulbry, & Lansing, 2015) . In comparison, to heat one liter of water from 5 ° C to 39 ° C, it would require an energy input of 0.0 40 kWh; while to heat it instead to 30 ° C would require 0.0 29 kWh. This would represent a theoretical energy reduction of approximately 3 0 % for a change in 9 degrees . On the other hand, it could provide incentives to opt for covered la goons in environments that will still provide similar biogas quantity and qualities as a CSTR in the same location. Covered lagoons have been believed to not be able to produce the same biogas quality and quantity as a CSTR ; but through this BMPs , an initi al hypothesis can be presented with respect to this idea. Covered lagoons might be able to provide the same biogas quality and quantity i f an operating temperature of 30 o C ca n be maintained to promote the growth of methanogens at the lower end of the mesop hilic range. The next chapter will introduce the testing of small - scale pilots to analyze this hypothesis. The following chapter and testing will provide a greater insight on the actual variations in biogas quantity and quality when a non - ideal scenario i s introduced . Instead of performing batch testing like BMPs, the pilot testing will function as a continuous reactor where fresh feedstock is introduced to the pilots at regular intervals . The end benefit is analyzing the efficiency t o operate a lagoon at lower temperatures than presented in literature while still obtaining the benefits of biogas and GHG emissions reductions. In additions, CSTR s could reduce operational cos ts by running the system at lower te mperatures and directing biogas to other valuable uses or processes. 58 5. PILOT TESTING 5.1 Purpose and Conditions There is a lack of data availability explaining the variations in temperature with respect to biogas production and biogas quality . The BMP results presented in Chapter 4 provide an opportunity to investigate the effects of temperature with respect to these topics . The BMPs testing was utilized to investigate the biodegr adability of the dairy manure under ideal con ditions at different temperatures and mixing regimen s . The pilot testing allowed for comparisons of biogas production from cow manure while trying to represent lagoon conditions with variations in temperature and the lack of supplemental mixing. Three temperature profiles were analyzed during the pilot studies: constant 20 ° C and 39 ° C , and unregulated , ambient . Duplicated pilots were operated at each temperature profile. The unregulate d pilot s w ere allowed to fluctuate with ambient temperature so biogas production and quality could be analyze d without a controlled environment. Both 20 ° C and 39 ° C were maintained in environments with controlled temperature. All six pilots were operated as non - mixed systems du e to the results presented in Chapter 4 with the BMPs and due to time constraints. Mixing is a multifaceted process and due to time constraints and the inability to consider mixing as a binary process, it was not considered for aspects of this research. The pilot characteristics and environments are summarized in Table 5.1 . Additional supplementary data and graphs presenting biogas production can be found in Appendix D. Table 5.1 . Summary of Pilots Testing Conditions Pilots Condition Temperature 1 Psychrophilic 20 °C 2 3 Unregulated 9 °C to 28 ° C 4 5 Mesophilic 39 °C 6 59 5.2 Results and discussion 5.2.1 Characterization In this section, the results and discussion for the material characteristics from the pilots such as raw material, effluent pH, TS , and VS reduction will be provided. The number of samples collected (n) were averaged together in order to obtain results delivered in the tables within this section . 5.2.1.1 Dairy Cow Manure Dairy cow manure was collected biweekly in order to provide sample variety and freshness throughout the duration of the project. A total of 9 manure collections occurred for the project. An average and standard deviation of the raw characterization results are presented in Ta b le 5. 2 . The pH for the liquid manure ranged from a minimum of 7.34 to a maximum of 8.57. The pH average presented is 7.77. The average TS collected was 44,374 mg per L or 44,026 mg per kg, while average VS collected was 29,005 mg per L or 28,773 mg per kg. The manure collected , with a TS percentage between 3% and 6%, represents similarly the concentrations that would be available in farms interested in utilizing an anaerobic covered lagoon. Table 5. 2 . Dairy Cow Manure Characterization S ample pH TS VS TS VS n (mg/L) (mg/L) (mg/kg) (mg/kg) Liquid Dairy Cow Manure 7.77±0.4 44,374 ± 6,907 29 , 005± 3,665 44,026 ± 6,978 28,773 ± 3,705 9 5.2.1.2 O rganic Loading Rate As discussed previously, OLR is the amount of organic material fed into the digester . All the pil ots were fed equal mass volume per feeding of 104 grams of liquid cow manure . Due to biweekly collections of fresh feedstocks , variations in OLR occurred over the timeline of the 60 project. Table 5. 3 presents the OLR variations per HRT. During the project timeline , the average OLR was 1.45 ± 0.23 g VS /L per day. The minimum OLR was 1 g VS /L per day, while the maximum was 1.78 g VS /L per day. Table 5. 3 . Organic Loading Rate for the Project based on HRT HRT Average OLR ± Std. Dev. Min Max n ( kg VS/m 3 /day ) (g/L/day) (g/L/day) 1 1.45±0.26 1.00 1.68 23 2 1.51 ± 0.23 1.30 1.78 19 3 1.39 ± 0.18 1.15 1.55 19 T ypical HRT for CSTR digesters is between 1 to 10 kg VS /m 3 /day . It can be noted that for a CSTR, which is typical at mesophilic temperatures between 37 ° C and 39 ° C, the organic loading rate utilized during this experiment was at the lower end of the scale. Any effects due to this will be discussed in the following sections with respect to biogas quantity and quality. For a covered lagoon, typical OLR are between 0 to 0.2 kg VS /m 3 /day , indicating that there was a higher intr oduction of solids for our systems when compared to typical covered lagoons. A high or low feeding rate can lead to poor performance. High feeding rates can cause an accumulation of volatile fatty acids, while under feeding a system can starve the microbia l community of needed energy. Both causing effects in the methanogenic community. 5.2. 1. 3 pH pH was measured for every effluent collected during feedings for each of the pilots. Table 5. 4 presents the average pH collected for each pilot category. The average pH for psychrophilic (20 o C) , unregulated and mesophilic (39 o C) conditions were 7.42, 7.46, and 7.81, respectively. The min imum pH for all the pilot categories was approximately 7.10 ; while the maximum was approx. 7.90. 61 Table 5. 4 . Average pH Effluent Measurements based on Temperature Profile , Average of 3 Condition Average pH ± Std. Dev. Min Max n Psychrophilic 7.42 ± 0.17 7.11 7.91 120 Unregulated 7.46 ± 0.17 7.14 7.98 120 Mesophilic 7.81 ± 0.12 7.09 8.17 120 Table 5. 5 presented the average pH for psychrophilic, unregulated, and mesophilic conditions with respect to HRTs. For the psychrophilic condition, the pH was maintained between 7.30 and 7.50 during the timeline of the project. The second HRT demonstrates slight de creases in pH, but it did not present a concern with regards to inhibition. For both , the unregulated and mesophilic conditions, the pH decreased over the time between HRTs. Overall, none of the pilots during the HRTs fell below a pH of 7.00 indicating the presence of inhibitory conditions. Table 5. 5 . Average pH Effluent Measurements based on HRT HRT Average pH ± Std. Dev. Min Max n Psychrophilic 1 7.48±0.23 7.11 7.91 46 2 7.34 ± 0.11 7.18 7.79 36 3 7.44 ± 0.03 7.39 7.53 38 Unregulated 1 7.63 ± 0.16 7.42 7.98 46 2 7.41 ± 0.05 7.31 7.56 36 3 7.31 ± 0.05 7.14 7.39 38 Mesophilic 1 7.84 ± 0.17 7.09 8.17 46 2 7.80 ± 0.06 7.72 8.01 36 3 7.79 ± 0.05 7.72 7.91 38 62 Figure 5.1 present s the pH measurements collected for each pilot. As shown in Table 5. 4 , Table 5. 5 and Figure 5.1, the pH for none of the pilots fell below the 7.0 threshold . The psychrophilic pilots presented a decrease during the first HRT that was associated due to the slow growt h of the methanogenic community, but th e pH increases and stabilized during the second and third HRT. For the unregulated condition, the pH slowly increased during the timeline of the project ; but the pH was maintained above 7.0. If the unregulated conditi on had been studied for longer HRTs, pH measurements below 7.0 would have probably been detected. The mesophilic pilots presented a stable pH during the timeline of the project. Overall, all the pilots never reached an inhibitory condition and microbial co mmunities were stable during the duration of the project. Temperature did not indicate to cause effects on the pH of the pilots; moreover, disregarding the idea of inhibition occurring and creating a reduction on biogas quality or quantity. Additionally , t he results indicate that dairy cow manure has the buffering capacity to maintain pH in the systems without the addition of substrates. Figure 5.1. pH measurement for Pilot Effluents 7.00 7.20 7.40 7.60 7.80 8.00 8.20 8.40 0 500 1,000 1,500 2,000 2,500 3,000 3,500 pH Lapsed Time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 63 5.2.1.2 Total Solids and Volatile Solids Reduction TS and VS were measured for every effluent sample collected during the duration of the project . Appendix B.4. For the statistical analysis, a two - way ANOVA was performed to understand th e overall statistical significance of HRT and temperature, while a Tukey Honest Significant Difference statistical analysis was performed in order to demonstrate the pairwise differences between temperature and HRT. The TS and VS reductions were calculated by comparing the TS and VS present in the feedstock to the effluent collected during each feeding. TS and VS reduction is correlated to biogas production and settling . Table 5. 6 represents the average TS reductions for the timeline of the project for eac h condition . All the pilots indicated an average TS reduction of above 45 % . The TS reductions observed for psychrophilic , unregulated, and mesophilic conditions were 48, 57 and 65 % , respectively. Table 5. 6 . Total Solids Reduction based on Temperature Profile Condition Total Solids Reduction ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 48±12 18 69 40 Unregulated 57±6 46 71 40 Mesophilic 65±5 56 76 40 Table 5. 7 presents the variations in TS reduction w ith respect to HRT for the three condition s . The psychrophilic condition presented a n increased in TS reduction between HRTs. The TS re duction for t he psychrophilic condition was an average of 45% by the first HRT and increased to an average of 54% by the third HRT. The unregulated and mesophilic condition presented relatively stable reductions between all the HRTs. The unregulated pilots maintained an 64 average TS reduction between 55 % and 62% over the timeline of the project. The mesophilic pilots presented average TS reduction of above 60% for all the HRTs. The psychrophilic pilots presented the lowest TS reduction when compared to unregulated and mesophilic pilots. Table 5. 7 . Average Total Solids Red uction based on HRT HRT Total Solids Reduction ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 1 45 ± 7 33 54 14 2 44 ± 10 26 61 12 3 54 ± 14 18 69 14 Unregulated 1 55 ± 5 46 63 14 2 61 ± 6 53 71 12 3 56 ± 5 47 62 14 Mesophilic 1 66 ± 4 59 72 14 2 69 ± 4 63 76 12 3 62 ± 5 58 68 14 Table 5. 8 presents t he results of the two - way ANOVA performed for the TS reductions for each condition with respect to temperature and HRT . There was a significant effect with respec t to TS reduction on the pilots based on temperature at the p< 0 .05 level for the three conditions [F ( 2 , 111 ) = 52.276 , p = 0.000 ] . Moreover, there was no sign ificant effect with respect TS reduction on the pilots based on HRT at the p > 0 .05 level for the three conditions [F ( 2 , 111 ) = 1.624 , p = 0 .202 ] . This indicates that there are differences in the value of TS reductions associated to variations in temperature, but not necessarily to HRT. The results demonstrated a significant interaction (p - value<0.05) between temperature and HRT [F ( 4 , 111 ) = 5.012 , p = 0.000 ] . 65 Table 5. 8 . Two Way ANOVA Results for Total Solids Reduction for each Condition Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 2 6,148 3,074.0 52. 276 0.000 HRT 2 191 95.5 1. 624 0.2 02 Temperature: HRT 4 1,179 294.7 5.0 12 0.00 0 Residuals 111 6,527 58.8 The letter category L, B and H represent the psychrophilic, unregulated, and mesophilic conditions , respectively. The values 1 , 2, and 3 represent the first, second and third HRT, respectively. The Tukey analysis performed for the three conditions with respect to TS reduction is presented in Figure 5.2 and 5.3 . Figure 5. 2 . Tukey Honest Significant Difference Results for the Total Solids Reduction based on Temp er ature Figure 5.2 presents the results of the Tukey analysis with respect to temperature. The statistical analysis indicated a statistical significance (p - value<0.05) between all the conditions 66 presented when comparing temperature profiles. This indicates that there are differences in the value of TS reductions associated to variations in temperature. Figure 5.3 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there were no statistical differ ences (p - value>0.05) when comparing TS reductions between all of the HRTs. This indicates that there are no differences in the value of TS reductions associated to variations in HRT. Figure 5. 3 . Tukey Honest Significant Difference Results for the Total Solids Reduction based on HRT Figure 5. 4 represents a timeline of the reduction over all the pilots. In Figure 5. 4 , it is observed that throughout HRTs every pilot category performed relatively equal. In the second HRT, the psychrophilic pilots (1 & 2), as observed in Figure 5. 4 , presented a sudden decrease in reduction. This decrease in reduction could have been associated to a sudden change in solids present in the feed when compared to previous weeks or the occurrence of a settling event . Overall, the mesophilic condition had the highest TS reduction, indicating higher gas production see in those pilots. 67 Figure 5. 4 . Total Solids Reduction for Pilots during project timeline Ta b le 5. 9 presents the average VS reduction s measured during the project timeline. All the pilots presented an average reduction of above 50 % . The average VS reduction for psychrophilic , unregulated, a nd mesophilic were 52, 62 , and 70 % , respectively. Table 5. 9 . Volatile Solids Reduction based on Temperature Profile Condition Volatile Solids Reduction ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 52±13 22 76 40 Unregulated 62±6 42 74 40 Mesophilic 70±5 57 80 40 Table 5.10 presents the changes in VS reduction with respect to HRT presented for each condition. The psychrophilic condition presented and increased in VS reduction between HRTs. The psychrophilic VS reduction was an average of 46% by the first HRT and in creased to an average of 61% by the third HRT. The unregulated and mesophilic condition presented relatively stable reductions between all the HRTs. The unregulated pilots maintained an average VS - 10 20 30 40 50 60 70 80 90 100 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Total Solids Reduction (%) Lapsed Time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 68 reduction between 58 and 66% over the timeline of the proje ct. The mesophilic pilots presented average VS reduction of above 69% for all the HRTs. Table 5. 10 . Volatile Solids Reduction based on HRTs HRT Volatile Solids Reduction ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 1 46 ± 7 34 56 14 2 47 ± 12 22 61 12 3 61 ± 14 22 76 14 Unregulated 1 58 ± 7 42 68 14 2 66 ± 5 60 74 12 3 61 ± 4 53 66 14 Mesophilic 1 69 ± 6 57 77 14 2 74 ± 3 69 80 12 3 69 ± 4 63 75 14 Table 5.1 1 presents the results of the two - way ANOVA performed for the VS reductions for each condition with respect to temperature and HRT. There was a significant effect on V S reduction on the pilots based on temperature at the p< 0 .05 level for the three condition s [F ( 2 , 111 ) = 53.539 , p = 0.000 ] . Additionally , there was significant effect on V S reduction on the pilots based on HRT at the p< 0 .05 level for the three conditions [F ( 2 , 111 ) = 5.338 , p = 0. 000 ] . The results demonstrated a significant interaction (p - va lue<0.05) between temperature and HRT [F ( 4 , 111 ) = 6.334 , p = 0.000 ] . The Tukey analysis performed for the three conditions with respect to VS reduction is presented in Figure 5.5 and 5.6. Figure 5.5 presents the results of the Tukey analysis with respect to temperature. The Tukey statistical analysis indicated statistical significance (p - value<0.05) 69 between all the conditions presented when comparing temperature profiles. This indicates that there are differences in the value of VS reductions associated t o variations in temperature. Figure 5.6 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there were statistical differences (p - value<0.05) when comparing VS reductions between the first and the third HRT . Table 5.1 1 . Two Way ANOVA Results for Volatile Solids Reduction for each Condition Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 2 7,041 3,521 53. 539 0.000 HRT 2 702 351 5.3 38 0. 006 Temperature: HRT 4 1,666 417 6.3 34 0.00 0 Residuals 111 7,299 66 Figure 5. 5 Tukey Honest Significant Difference Results for t h e Volatile Solids Reduction based on Temperature 70 Figure 5. 6 . Tukey Honest Significant Difference Results for the Volatile Solids Reduction based on HRT Figure 5. 7 represents a timeline of the reduction for the individual pilots. In both, Figure 5. 4 and 5. 7 , there is a noticeable decrease in both TS and VS reduction for the psychrophilic pilots during the second HRT. This phenomenon could have been associated to the introduction of a dairy manure sample with approx. 25% more TS and VS than previous samples collected or the occurrence of a settling event . Overall, the mesophilic had the highest TS and VS reduction, indicating higher gas production for pilots maintaine d at that condition. 71 Figure 5. 7 . Volatile Solids Reduction for Pilots during project timeline 5.2.2 Gas Production Gas production was measured with a tip meter, as mentioned in the Material and Methods ( Section 3 ) . Gas volumes were counted using a tip counter and conversions were made based on the calibration volumes before initializing the experiment. The number of measurements collected (n) were averaged together in order to obtain results delivered in the tables within this section. The statistical analysis corresponding to biogas quantity are presented in Appendix B .1 . Additionally, s upplementary data and graphs corresponding to pilots have been included in A ppendix D. The cumulative biogas production for each condition is presented in T able 5. 1 2 . Average cumulative biogas production for psychrophilic , unregulated, and mesophilic condition was 25 L, 31 L and 56 L, respectively. The mesophilic pilots produced the highest cumulative daily biogas production, followed by the unregulated and psychrophilic conditions, respectively. - 10 20 30 40 50 60 70 80 90 100 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Volatile Solids Reduction (%) Lapsed Time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 72 Table 5. 1 2 . Cumulative Biogas Production based on Temperature Profile , Average of 3 Condition Cumulative Biogas Production ± Std. Dev. (L ) n Psychrophilic 25±17 125 Unregulated 31±18 124 Mesophilic 56±36 125 Table 5.1 3 presents the cumulative biogas production for each condition by each HRT. The psychrophilic condition presented an increase of approximately 12 L between the first and second HRT. During the second and the third HRT, the psychrophilic condition presented and increase of approximately 26 L which is roughly double when compared to the first and the second HRT. The unregulated condition presented an increase of approximatel y 21 L between the first and second HRT. During the second and the third HRT, the unregulated condition presented and increase of approximately 15 L which was lower when compared to the first and the second HRT. This would indicate in a reduction of biogas production due to temperature changes that can be noted with lower temperatures presented in the second and third HRT. The mesophilic condition presented an increase of approximately 36 L between the first and second HRT. During the second and the third H RT, the mesophilic condition presented and increase of approximately 60 L which is roughly double when compared to the first and the second HRT. The mesophilic condition presented greater biogas production throughout the timeline of the project when compar ed to the psychrophilic and unregulated conditions. 73 Table 5. 1 3 . Average Cumulative Biogas Production after each HRT HRT Cumulative Biogas Production ± Std. Dev. (L ) n Psychrophilic 1 8 ± 4 43 2 20 ± 5 38 3 46 ± 8 46 Unregulated 1 12 ± 7 44 2 3 3 ± 5 38 3 48 ± 4 46 Mesophilic 1 18 ± 11 44 2 54 ± 9 38 3 114 ± 15 46 Figure 5. 8 presents the cumulative biogas production for the individual pilots. The three highest producing pilots were 5, 6 and 4, respectively. For the p sychrophilic pilots d uring the first HRT , there is an initial low biogas production that stabilizes. During the second HRT , a steady increase in biogas production can be observed continuing into the third HRT. The pilots 3 and 4, during the first 500 hours, present close plot line s and similar volumes with regards to biogas production. During the second HRT, both pilots steady increased their cumulative production; and once the third HRT is achieved, both pilots appear to achieve a plateau in biogas production. For pilots 5 and 6, during the first HRT, the pilots presented close plot line s and similar volumes with regards to biogas production. The plot line s for these pilots continued to increase at a stable rate, therefore providing no indication of plateau or inhibition with respe ct to biogas production. Pilot 6 presents a lower plot line when compared to pilot 5 during the duration of the second and third 74 HRT. This could have been attributed to a leak or issues that could not be detected with day - to - day operations or with measurem ents such as pH and gas chromatography. It can also be noted that the steady increase between the first and second HRT for all pilots could have been associated to higher organic loading rates introduced in the second HRT in comparison to the first HRT. Figure 5. 8 . Cumulative Biogas Production for Psychrophilic , Unregulated and Mesophilic Pilots The daily biogas production for each condition is presented in T a b le 5. 1 4 . Average daily biogas production for psychrophilic , unregulated, and mesophilic condit ion was 0.9 L, 0. 9 L and 1.8 L, respectively. The daily biogas production in L per initial VS for psychrophilic, unregulated, and mesophilic conditions was 324, 291 and 604, respectively. The mesophilic pilots produced the highest average daily biogas prod uction, while the psychrophilic and unregulated conditions produced approximately half the biogas volume as mesophilic condition. - 20 40 60 80 100 120 140 160 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Cumulative Biogas Production (L) Lapsed Time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 75 Table 5. 1 4 . Daily Biogas Production based on Temperature Profile Condition Daily Biogas Production ± Std. Dev. Daily Biogas Production ± Std. Dev. Min Max n (L/kg Initial VS ) (L/day) (L/day) (L/day) Psychrophilic 324 ± 161 0.9±0.5 0.16 2.24 12 7 Unregulated 291 ± 194 0.9 ± 0.6 0.10 3.00 12 6 Mesophilic 604 ± 276 1.8 ± 0.8 0.20 4.20 12 6 Table 5.1 5 presents the daily biogas production based on HRTs presented by each condition. For the psychrophilic condition, the daily biogas production increased at a relative steady pace between each HRT . For the unregulated condition, we can notice changes and decrease in biogas production as time pro gressed. The decreases in biogas production seem to be correlated to the temperature variations in the unregulated conditions associated to seasonal changes . It can also be noted that the unregulated pilots had higher daily biogas production than pilots in the psychrophilic condition when warmer temperatures were presented for the unregulated pilots . During the second HRT, the psychrophilic and unregulated pilots had relatively similar daily biogas production; and in the third HRT, the psychrophilic pilots had higher daily biogas production than the unregulated pilots. During the third HRT, the unregulated pilots were experiencing temperatures around or below 15 ° C. The mesophilic pilots produced a relatively stable daily biogas production across all HRTs. 76 Table 5. 1 5 . Daily Biogas Production based on HRTs HRT Daily Biogas Production ± Std. Dev. Daily Biogas Production ± Std. Dev. Min Max n (L/kg Initial VS ) (L/day) (L/day) (L/day) Psychrophilic 1 205 ± 137 0.6 ± 0.6 0. 16 1.96 43 2 316 ± 122 1.0 ± 0.5 0 .32 2.24 38 3 429 ± 130 1.2 ± 0.4 0.24 2.04 46 Unregulated 1 387 ± 193 1.1 ± 0.5 0.48 3.00 44 2 284 ± 131 0.9 ± 0.4 0.38 2.00 37 3 189 ± 190 0.5 ± 0.5 0.10 2.70 44 Mesophilic 1 593 ± 250 1.7 ± 0.7 0.20 4.20 43 2 603 ± 295 1.8 ± 0.7 0.21 3.80 38 3 600 ± 291 1.7 ± 0.6 0.20 3.40 45 Table 5.1 6 presents the results of the two - way ANOVA performed for the daily biogas production for each condition with respect to temperature and HRT. There was a significant effect on daily biogas production for the pilots based on temperature at the p< 0 .05 level for the three conditions [F ( 2 , 365 ) = 77.236 , p = 0.000 ] . This indicates that there are differences in daily biogas production associated to variations in temperature. Moreover , there was no significant effect on daily biog as production for the pilots based on HRT at the p > 0 .05 level for the three conditions [F ( 2 , 365 ) = 0.321 , p = 0. 726 ] . The results demonstrated a significant interaction (p - value<0.05) between temperature and HRT [F ( 4 , 365 ) = 10.478 , p = 0.000 ] . The Tukey analysis performed for the three conditions with respect to daily biogas production is presented in Figure 5. 9 and 5. 10 . Figure 5. 9 presents the results of the Tukey analysis with respect to temperature. The Tukey statistical analysis indicated statistical significance (p - 77 value<0.05) when comparing psychrophilic and unregulated conditions to the mesophilic condition ; but there was no statistical significance (p - value>0.05) between psychrophilic and unregulated conditions with respect to temperature. Figure 5. 10 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there were no statistical difference s (p - value > 0.05) when comparing daily biogas pr oduction between HRTs. Table 5. 1 6 . Two Way ANOVA Results for the Daily Biogas Production for each Condition Df Sum Sq Mean Sq F Value Pr (0 . 05 level for the three conditions [F ( 2 , 365 ) = 0. 645 , p = 0. 525 ] . The results demonstrated a significant interaction (p - value<0.05) between temperature and HRT [F ( 4 , 365 ) = 12.274 , p = 0.000 ] . 79 Table 5. 1 7 . Two Way ANOVA Results for the Daily Biogas Prod uction per kg Initial VS for each Condition Df Sum Sq Mean Sq F Value Pr (0.05) between psychrophilic and unregulated conditions with respe ct to temperature. Figure 5.12 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there were no statistical differences (p - value>0.05) when comparing daily biogas production per kg initial VS between HRTs. 80 Figure 5. 11 . Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on Temperature Figure 5. 12 . Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on HRT 81 Figure 5. 13 presents the daily biogas production for the pilots during the project timeline. The 39 o C pilots produced roughly twice the volume of b iogas when compared to psychrophilic and unregulated pilots. Figure 5. 13 . Daily Biogas Production for Psychrophilic , Unregulated and Mesophilic Pilots Figure 5.14 presents the daily biogas productions for pilot 1 and 2, which were maintained at 20 ° C. Th e daily biogas production for these pilots was an average of 0.9 L/day. The slow increase in biogas production might have been related to the effects of temperature in the microbial community. Methanogenesis is one of the major rates limiting processes wit hin anaerobic digestion. At lower temperatures, there is the speculation that the process of hydrolysis does not occur as rapidly as higher temperatures and therefore limiting the material availability for the acetogenic and methanogenic bacteria (Patel, P andit, & Chandrasekhar, 2017). Although the TS and VS reduction for these pilots was lower than unregulated and mesophilic pilot, these pilots managed to produce the same volume of biogas as the unregulated pilots. - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L) Lapsed Time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 82 Figure 5. 14 . Daily Biogas Production fo r Psychrophilic Pilots Pilots 3 and 4 were operated with no temperature control . Figure 5. 15 represents the daily biogas production for each pilot in the primary axis and the temperature measurements for the room during the duration of the project in the secondary axis . During the first HRT, temperature range d from 20 ° C to 28 ° C, corresponding to the months of July to September. During the second HRT, temperature range d from 14 ° C to 23 ° C, which had fluctuations between the mesophilic and psychrophil ic temperature ranges , correspond ing to the months of September to October. During the third HRT, we observed temperatures from 9 ° C to 23 ° C, corresponding to the months of October to December. In Figure 5. 15 , clear decrease in temperature can be seen from the transition of the seasons of summer to fall and beginning of winter. The overall average daily biogas production for unregulated pilots was 0. 9 L/day. The biogas production was maintained relatively similar between the first and second HRT. During the third HRT there was a significant decrease in the daily biogas production which can be related to the transitions of mesophilic temperatures to psychrophilic temperatures between the second and - 0.50 1.00 1.50 2.00 2.50 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L) Lapsed Time (hrs) Pilot 1 Pilot 2 83 third HRT. The statistical analysis (Appendix B.1) supported this idea by demonstrating no statistical significance (p - value>0.05) between the first and second HRT, but statistical significance (p - value<0.05) between the first and third HRT. During the first HRT of the psychrophilic and unregulated , the un regulated pilots produced higher daily volumes. During the first HRT, the unregulated pilots were maintained at higher temperatures than 20 ° C, therefore suggesting that an advantage might have been provided in the microbial development . During the second H RT, psychrophilic and unregulated pilots produced relatively similar daily biogas volumes due to a similar temperature profile in the unregulated to the psychrophilic area . During the third HRT, with sudden changes in temperature due to seasonal changes, t he unregulated produced less daily biogas volumes than psychrophilic pilots. The data indicates that biogas production was slowly decreasing through HRTs with a decrease in temperature. In a study presented by Wan g et al. (2019), biogas production and CH 4 concentration were affected by disturbances in temperature from 35 ° C to 20 ° C. It was observed during the study that severe changes did not occur with biogas quality and quantity until the reactors were maintained at temperatures below 25 ° C. According to the results presented, although changes were presented to the reactors from 35 ° C to 20 ° C, biogas production efficiency and operation stability was maintained by the methanogenic community that had been developed at higher temperatures; but onc e the reactors were maintained below 20 ° C, severe decrease in biogas production was observed. This study provides a possibility of the similar occur rences in the unregulated pilots during the third HRT . As observed in Figure 5. 15 , once a temperature of 15 ° C was reached, biogas production became compromised in relationship to temperature changes. During the first and second HRTs, the methanogenic community was a b le to maintain biogas production and operation parameters, 84 while during the third HRT, the sudden decrease in biogas production can be associated to the inhibition of the metabolic activity of the methanogens. Figure 5. 15 . Daily Biogas Production for Unregulated Pilots Figure 5. 16 represents the daily biogas production for pilots 5 and 6 maintained at 39 ° C. The average daily biogas production for mesophilic pilots was 1.8 L/day, which was approx. double the biogas volume of psychrophilic and unregulated pilots. There was no statistical significance (p - value>0.05) between the HRTs . Biogas p roduction stabilized since the initial weeks of operation. In similarity to the cumulative biogas production graph (Figure 5.4) , p ilot 6 presents a lower plot line when compared to pilot 5 ; and as mentioned previously, t his could have been attributed to a leak or issues that could not be detected with day - to - day operations or with measurements such as pH and gas chromatography. In conjunction with the finding for TS and VS reduction, it supports the idea that at 39 ° C , higher reduction correlate s to higher b iogas production. 0 5 10 15 20 25 30 - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Temperature ( ° C) Biogas Production (L) Lapsed Time (hrs) Pilot 3 Pilot 4 Temperature 85 Figure 5. 16 . Daily Biogas Production for Mesophilic Pilots 5.2.3 Gas Quality Gas quality was obtained through weekly gas chromatography. The two gas quality parameters being considered are CH 4 and hydrogen sulfide. The number of samples collected (n) were averaged together in order to obtain results delivered in the tables within t his section. The presented in Appendix B.2 and Appendix B.3 for methane and hydrogen sulfide, respectively . 5.2.3.1 Methane Table 5. 1 8 present the average, mi nimum and maximum CH 4 concentrations obtained for the samples collected. All the pilots provided an average CH 4 concentration of above 50 % during the entire timeline of the project . The unregulated digesters reached an average of 62 % followed by psychrophi lic and mesophilic with 61 and 58 % , respectively. The unregulated pilots produced the highest methane concentration of 67% over the lifetime followed b y psychrophilic and mesophilic with 66% and 62% respectively. - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L) Lapsed Time (hrs) Pilot 5 Pilot 6 86 Table 5. 1 8 . Methane Content based on Temperature Profile Condition Methane Content ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 61±3 54 66 42 Unregulated 62 ± 2 56 67 42 Mesophilic 58 ± 2 53 62 42 Table 5. 1 9 present s the methane content over each HRT for each condition. Overall, the methane content for each condition o ver each HRT averaged to be relatively the same throughout the timeline of the project. The psychrophilic condition and the unregulated condition provided relatively similar methane content of approximately 60% during the three HRTs. The mesophilic pilot in comparison to the other two conditions produced lower methane content of approximately 58% over the timeline of the project. Table 5. 1 9 . Methane Content based on H RTs HRT Methane Content ± Std. Dev. Min Max n (%) (%) (%) Psychrophilic 1 61±1 54 63 14 2 62 ± 2 57 66 14 3 61 ± 4 59 66 14 Unregulated 1 63 ± 2 60 66 14 2 62 ± 2 58 64 14 3 63 ± 3 56 67 14 Mesophilic 1 58 ± 2 53 62 14 2 57 ± 2 53 60 14 3 58 ± 1 56 60 14 87 Table 5. 20 presents the results of the two - way ANOVA performed for the CH 4 content for each condition with respect to temperature and HRT. There was a significant effect CH 4 content for the pilots based on temperature at the p< 0 .05 level for the three conditions [F ( 2 , 117 ) = 46.881 , p = 0.000 ] . This indicates that there are differences in CH 4 content associated to variations in temperature. Moreover, there was no significant effect on CH 4 content for the pilots based on HRT at th e p >0 .05 level for the three conditions [F ( 2 , 117 ) = 0. 236 , p = 0. 790 ] . The results demonstrated no significant interaction (p - value > 0.05) between temperature and HRT [F ( 4 , 117 ) = 0.767 , p = 0 . 549 ] . Table 5. 20 . Two Way ANOVA Results for Methane Conte nt for each Condition Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 2 554.2 277.09 46.881 0.000 HRT 2 2.8 1.40 0.236 0.790 Temperature: HRT 4 18.1 4.54 0.767 0.549 Residuals 117 691.5 5.91 The Tukey analysis performed for the three conditions with respect to CH 4 content is presented in Figure 5.1 7 and 5.1 8 . Figure 5.1 7 presents the results of the Tukey analysis with respect to temperature. The Tukey statistical analysis indicated statistical significance (p - value<0.05) when comparing psychrophilic and unregulated conditions to the mesophilic condition; but there was no statistical significance (p - value>0.05) between psychrophilic and unregulated conditions with respect to temperature. Figure 5.18 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there were no statistical difference s (p - value>0.05) when comparing to CH 4 content between HRTs. 88 Figure 5. 17 . Tukey Honest Significant Difference Results for the Methane Content based on Temperature Figure 5. 18 . Tukey Honest Significant Difference Results for the Methane Content based on HRT 89 Figure 5. 1 9 presents the CH 4 concentrations collected per pilot during the project timeline. All the pilots presented CH 4 contents between 50 and 70 % . As an overall comparison, as shown in Figure 5. 1 9 , psychrophilic and unregulated pilots maintained higher CH 4 content than mesophilic pilots during the second and third HRT. The statistical analysis demonstrated that there is a significance difference between mesophilic pilots with respect to unregulated and psychrophilic pilots with respect to temperature. There was no statistical significance when comparing HRTs within categories. Figure 5. 1 9 . Methane Content from Weekly Gas Chromatography for Psychrophilic, Unregulated and Mesophilic Pilots Figure 5. 2 0 presents the CH 4 content for pilots 1 and 2 maintained at 20 ° C. The psychrophilic pilots presented a higher CH 4 concentration than the mesophilic pilots. The psychrophilic pilots presented an average CH 4 concentration of 61 % , with a minimum of 54 % and a maximum of 66 % . Even though psychrophilic pilots had a lower biogas quantity in comparison to mesophilic pilots, t hese pilots had a higher CH 4 content with respect to mesophilic pilots. During 50 52 54 56 58 60 62 64 66 68 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Methane (%) Lapsed time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 90 the first HRT, there is a decrease from approx. 65 % CH 4 to 54 % . This initial decrease during the first HRT could have also provided an additional form of information to infer the idea that at lower tempera ture ranges , there is a slower formation of methanogens and therefor e influencing both biogas quantity and quality. After the first HRT, an increase and stabilization in CH 4 production occurs during the second and third HRT. From the statistical analysis, no statistical significance (p - value>0.05) was presented between HRTs . Figure 5. 2 0 . Methane Content for Psychrophilic Pilots Figure 5. 2 1 presents the CH 4 content for pilots 3 and 4 maintained in the uncontrolled temperature room. The average CH 4 content for the unregulated was 62 % with a minimum of 56 % and a maximum of 67 % . The unregulated pilots produced the highest average CH 4 content when compared to psychrophilic and mesophilic pilots. Pilots at 3 7 ° C to 39 ° C are typically recommended for CH 4 production. Hawkes et al. (1984) presented results similar to the ones obtained during this research. The results reported presented less than a 10% difference in CH 4 production between psychrophilic scales pil ots maintained at 20 ° C, 25 ° C, 30 ° C and 35 ° C. Other 50 52 54 56 58 60 62 64 66 68 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Methane (%) Lapsed time (hrs) Pilot 1 Pilot 2 91 studies suggest equal CH 4 production can be achieved at psychrophilic and mesophilic temperatures. Similar CH 4 productions were accredited to the higher biomass yield in digesters operated at lower mesophilic temperatures (Guo et al., 2013; Pandey and Soupir , 2012) . Additionally, research has continued to explore the possibility of syntropy occurring within microb ial communities in digesters. The CH 4 , CO 2 and hydrogen formed between acetogens and methanogens . S pecialized communities of methanogens are able to convert the hydrogen and CO 2 molecules into CH 4 molecules by proton reduction. Therefore, creating biogas w ith higher CH 4 to CO 2 ratios ( Dyksma, Jansen, & Gallert, 2020 ; Shimada et al., 2011 ) . Even though these studies have been presented and demonstrated by the identification of microbial communities through RNA analysis, evidence has only supported this ideol ogy in thermophilic digesters or in two stage anaerobic processes. As show in Figure 5. 2 1 , CH 4 content fluctuated between measurements and can be correlated to sudden temperature changes from the seasonal change. From the statistical analysis, there was no statistical significance (p - value>0.05) between the HRTs for the unregulated . The TS and VS reduction was relatively similar to mesophilic pilots , probably indicating that even though not as high of biogas production can be achieves at lower mesophilic temperatures, similar biogas quality can be achieve d . 92 Figure 5. 2 1 . Methane Content for Unregulated Pilo ts Figure 5. 2 2 represents the CH 4 content for pilots 5 and 6 maintained in the mesophilic at 39 ° C. The mesophilic pilots produced an average CH 4 content of 58 % , with a minimum of 53 % and a maximum of 62 % . The mesophilic pilots although had the highest biogas production, contained the lowest average CH 4 concentration. At higher temperatures, it is estimated th at reaction rates are faster than at lower temperatures and therefore achieving TS and VS reductions in a shorter timeline ( Kim et al., 2006) . There could the possibility of insufficient organic material for the pilots to maintain a high CH 4 concentration and therefore, although TS and VS reduction and high daily biogas production can be achieved, there is a limitatio n on the gas quality. 50 52 54 56 58 60 62 64 66 68 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Methane (%) Lapsed time (hrs) Pilot 3 Pilot 4 93 Figure 5. 2 2 . Methane Content for Mesophilic Pilots 5. 2.3.2 Hydrogen Sulfide According to MSU extension, hydrogen sulfide (H 2 S) digestion . CH 4 and CO 2 are usually the main components discussed about biogas. Typical biogas will contain CH 4 , CO 2 , water vapor and H 2 S . Any other material apart from CH 4 will have to be removed or scrubbed in order to obtain renewable natural gas. H 2 S has to be removed whethe r biogas is being upgraded or converted to electricity due to wear and tear on the engine and air quality concerns. The cleanup of H 2 S can b e costly and therefore brings a concern to the possibility of increasing or decreasing with changes in temperature. One of the sources of sulfide production in digesters is the biological concentration of sulfates in the influent. Studies have presented a proportional correlation between organic loading rates and H 2 S concentration , and an inversely proportional correlat ion between pH and H 2 S . Research discovered that the higher the initial pH of the digester seed, the lower the survival of sulphate reducing bacteria present and therefore a reduction in the production of H 2 S (Chen et al., 2014). Table 5. 2 1 presents the average 50 52 54 56 58 60 62 64 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Methane (%) Lapsed time (hrs) Pilot 5 Pilot 6 94 H 2 S present in each category . The H 2 S production for psychrophilic , unregulated, and mesophilic was 817 , 1, 448 and 1 ,271 , respectively. Table 5. 2 1 . Hydrogen Sulfide Content based on Temperature Profile Condition Hydrogen Sulfide Content ± Std. Dev. Min Max n (ppm) (ppm) (ppm) Psychrophilic 817±847 26 2,891 38 Unregulated 1,448 ± 1,370 5 4,390 42 Mesophilic 1,271 ± 1,029 4 3,881 42 Table 5. 2 2 presents the H 2 S content per HRT for each condition. The H 2 S in the psychrophilic condition had the highest change in production between the second HRT. The unregulated and mesophilic conditions had steady increases in H 2 S content throughout the project. The unregulated condition had higher H 2 S content in each HRT when compared to mesophilic and psychrophilic conditions . Table 5. 2 2 . Hydrogen Sulfide Content based on HRTs HRT Hydrogen Sulfide Content ± Std. Dev. Min Max n (ppm) (ppm) (ppm) Psychrophilic 1 122 ± 129 26 394 10 2 160 ± 212 193 1,002 14 3 806 ± 900 423 2,0 91 14 Unregulated 1 223 ± 260 5 707 11 2 695 ± 749 76 2,327 14 3 914 ± 1,162 389 4,390 14 Mesophilic 1 188 ± 201 4 536 7 2 427 ± 486 146 1,720 14 3 672 ± 807 654 3,881 14 95 Table 5.2 3 presents the results of the two - way ANOVA performed for the H 2 S content for each condition with respect to temperature and HRT. There was a significant effect H 2 S content for the pilots based on temperature at the p< 0 .05 level for the three conditions [F ( 2 , 1 03 ) = 7.963 , p = 0.000 ] . This indicates that there are differences in H 2 S content associated to variations in temperature. Additionally , there was a significant effect on H 2 S content for the pilots based on HRT at the p < 0 .05 level for the three conditions [F ( 2 , 103 ) = 79.964 , p = 0.000 ] . The results demonstrated no significant interaction (p - value>0.05) between temperature and HRT [F ( 4 , 1 03 ) = 1.962 , p = 0.106 ] . Table 5. 2 3 . Two Way ANOVA Results for the Hydrogen Sulfide for each Condition Df Sum Sq Mean Sq F Value Pr ( > F) Temperature 2 8 090 , 288 4 045 , 144 7.963 0.000 HRT 2 81 238 , 768 40 619 , 384 79.964 0.000 Temperature: HRT 4 3 986 , 279 996 , 570 1.962 0.106 Residuals 103 52 321 , 088 507 , 972 The Tukey analysis performed for the three conditions with respect to H 2 S content is presented in Figure 5. 23 and 5. 24 . Figure 5. 23 presents the results of the Tukey analysis with respect to temperature. The Tukey statistical analysis indicated statistical significance (p - value<0.05) when comparing unregulated and mesophilic conditions to the psychrophilic condition; but there was no statistical significance (p - value>0.05) between unregulated and mesophilic conditions with respect to temperature. Figure 5. 2 4 represents the results of the Tukey analysis with respect to HRT, which demonstrates that there was a statistical difference (p - value<0.05) when comparing to H 2 S content between HRTs. 96 Figure 5. 2 3 . Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on Temperature Figure 5 .2 4 . Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on HRT 97 Figure 5.25 present the hydrogen sulfide content from weekly gas chromatography for the individual pilots. Every pilot presented levels above detection limits after 300 hours of operation. As shown in figure 5.14, hydrogen sulfide had a steady increase during the first and second HRT for all the pilots independently of temperature profile. There is statistical significance presented for all pilots for HRTs indicating drastic changes in H 2 S concentrations as time progressed. The development of hydrogen sulfide is not uncommon for digester. At lower concentrations, methods such as oxygen injections within digesters are availability to reduce this component. It has to be taken into consideration for digester design in order to remove from biogas and diminish the damage to mechanical components. Fig ure 5.2 5 . Hydrogen Sulfide Content from Weekly Gas Chromatography for Psychrophilic, Unregulated and Mesophilic Pilots 50 550 1,050 1,550 2,050 2,550 3,050 3,550 4,050 4,550 5,050 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Hydrogen Sulfide (ppm) Lapsed time (hrs) Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 98 5. 3 Summary Table 5. 2 4 presents a summary of the results presented in his section. The main purpose of the pilot testing was to compar e the biogas production from cow manure while trying to represent lagoon conditions with variations in temperature and the lack of supplemental mixing. Three temperature profiles were analyzed during the pilot studies: constant 20 ° C and 39 ° C, and unregulat ed, ambient. Table 5. 2 4 . Summary Table for Results obtained based on Temperature Profile Parameters Condition 20 ° C Unregulated 39 ° C Temperature (°C) 20 9 to 28 39 Cumulative Biogas Production ± Std. Dev. (L) 25±17 31±18 56±36 pH 7.42±0.17 7.46±0.17 7.81±0.12 Total Solids Reduction ± Std. Dev. (%) 48±12 57±6 65±5 Volatile Solids Reduction ± Std. Dev. (%) 52±13 62±6 70±5 Daily Biogas Production ± Std. Dev. (L/day) 0.9±0.5 0.8 ± 0.6 1.8 ± 0.8 Methane Content ± Std. Dev. (%) 61±3 62 ± 2 58 ± 2 Hydrogen Sulfide Content ± Std. Dev. (ppm) 739±850 1,344 ± 1,389 1,059 ± 1,065 During the duration of the trial, measurements, such as pH, TSVS and GC, were performed in order to compare anaerobic reactions and verify the proper functioning of the digesters throughout the duration of the project. For all the pilots, pH was maintained in the ideal ranges and therefore discarding the idea of negative effects on pH based on temperature. In both the BMP trials and the pilots, low pH was never an issue encountered, therefor e, disregarding all idea of 99 inhibition in gas production. In pilot testing, the pH did lower across time in the psychrophilic and unregulated conditions so inhibition might have occurred around a fourth to fifth HRT possibly. All the pilots presented TSVS reductions during the testing. There was an increase in both TSVS reductions with respect to temperature . All the pilots presented TS reduction of above 45% and VS reductions of above 50%. VS reduction is correlated to biogas production and settling. A hi gher VS reduction indicates a proportional relationship to biogas production and enhanced digestion. The mesophilic pilots produced the highest reduction in comparison to the other two conditions. Although the other two conditions, psychrophilic and unregu lated, produced lower TSVS reductions, biogas production was still observed . For biogas production, the mesophilic digesters had by almost doubled the biogas production of the other two conditions, psychrophilic and unregulated. The cumulative biogas produ ction of the mesophilic pilots presented stable increase throughout the timeline of the project with a stable daily biogas production. A very interesting aspect was comparing the plot lines for biogas production between the psychrophilic and unregulated co nditions. The unregulated pilots were able to provide higher biogas production during the first HRT due that the temperature was higher when compared to psychrophilic pilots. During the second HRT, both conditions were presented with similar temperature pr ofiles and biogas production was relatively similar. During the third HRT, the unregulated pilots were experiencing temperatures below the ones for the psychrophilic pilots and therefore biogas production plummeted for the unregulated pilots. It could be n oted that the biogas production for the unregulated pilots was dropping slowly with temperature but once a temperature of 15 ° C was reached, the biogas production experienced more drastic reduction in biogas production. 100 For gas quality, two components were analyzed: methane and hydrogen sulfide. The methane content between the conditions presented intriguing results. The unregulated and psychrophilic conditions presented higher methane content when compared to the mesophilic condition. The TS and VS reductio n was relatively similar to mesophilic pilots, probably indicating that even though not as high of biogas production can be achieves at lower mesophilic temperatures, similar biogas quality can be achieved. There have been studies presenting the idea that biogas quality can be similar at lower temperatures other than 37 to 39 ° C. Certain studies have explained mechanisms on why this can occur but there is a lack of general consensus on why this occurs. Temperature has been studied scarcely in literature and this research was able to provide insight on the idea that digesters without supplemental heating are still viable. Temperature had greater influence in biogas quantity when temperatures below 20 ° C were presented. There is background to establish the viability of covered lagoons efficiency at lower temperatures than presented in literature while still obtaining the benefits of biogas and GHG emissions reductions. These data have opened the idea to explore more variability in the temperature range for d igesters to something other than 39 ° C which has become standard practice. 101 6. LIFE CYCLE ANALYSIS 6.1 Introduction L ife cycle analysis (LCA) is a tool utilized to evaluate the environmental impacts of goods, processes, or services. ISO 14040 defines the approach by which the environmental impacts and burdens associated with a product can be presented to create an enviro nmentally conscious decision. The approach analyzes the environmental tradeoffs associated with a process or system . T his tool can be used to identify the system components that have the highest environmental impact and replace them with solutions, alternatives , or processes that are sustainable and environmentally friendly (Azapagic et al., 2006). Throughout this assessment, two anaerobic digestion systems will be compared : a continuous stirred tank reactor (CSTR) and a covered lagoon. A dditional ly , the impact of current manure management systems , which do not use a digester , will be presented during the discussion of impacts. The comparison of a CSTR and a covered lagoon system are based on the biogas and digestate production, water cons umption, and electricity consumption. These inputs and outputs represent the major variables that lead to environmental impacts for both scenarios. 6.1.1 Supply Chain The supply chain s (Figure 6. 1) are closed loop s that start with cultivation. The crops are consumed by both humans and cattle. From cattle farming, the harvest meat and dairy products that proceed to human consumption, while cattle manure is treated through an anaerobic digester. Anaerobic digestion produce s biogas and digestate; biogas will be purified into RNG. The RNG c an be utilized by multiple sectors , such as transportation , where it is used to fuel trucks and city buses. The digestate would be separated into liquid and solid fractions . The liquid can be used as fertilizer, while the solids can be used as compost or animal bedding. 102 Figure 6. 1. Anaerobic Digestion Supply Chain system 6.1.2 Rural Biorefinery Classification Under the Cherubini biorefinery classification, the biorefinery presented (Fi gure 6.2 ) is classified as two - platform biogas and digestate biorefinery that produces renewable natural gas , fertilizer, and animal/compost bedding from cow manure through anaerobic digestion , upgrading and separation . This classification system helps to differentiate biorefineries based on key components and functions. This comparison reveals which AD type has the lowest environmental impacts for bioenergy production . There have been various studies about CH 4 production from the AD process; however, comparing the environmental impacts of a CSTR and a covered lagoon have 103 not been studied. This study provides a new perspective for decision maker s to present AD systems as a waste management solution to mitigate t he environmental impacts of dairy cow manure . In the end, this study intends to provide more insight about how to utilize livestock manure at its maximum value by producing valuable products, mitigating environmental and health risks, and generating more r evenue for the livestock farm industry. Figure 6.2 . Biorefinery Classification for Both Systems: a CSTR and a Covered Lagoon 6.2 Goal and Scope The goal is to compare the environmental impacts of a CSTR anaerobic digester to a covered lagoon as a waste management solution for dairy cow manure. The spatial, temporal, and geographical scopes are selected to ensure a fair comparison between the two systems. 104 The temporal scope cover s the waste management needed in twenty years due to the typical lifetime of this system. The geographic scope is the county of Maricopa, AZ. This geographic area is selected due to the favorable temperature conditions for a covered lagoon system. The spat ial scope includes many components that span from manure collection to purification and digestate production as shown in Figure 6 .3 . The system designs for both a covered lagoon and a CSTR can be found in Appendix E and App endix F, respectively . The scope does not include cattle management, sand and bedding recycling, solids to compost, digestate treatment and digestate application , long term storage lagoon , and the utilization of RNG in transportation . Cattle management and sand and bedding recycling are n ot included in the scope as they are similar for both systems. Solids for compost , long - term lagoon storage , and d igestate treatment are not included in the scope due to time constraints and complexity , in conjunction, an assumption is made that the digestate will be applied as - is. In order to compare manure management strategies, the emissions associated with manure storage were accounted only in the system boundary for the current waste management system. The functional unit (FU) is 95,813 kg ( TS ) per day. It is useful for the functional units to be based on T S because TS can be used to reflect the biodegradable material in the waste stre am. The reference flow is equal to the functional unit because it refers to the flow of dry manure that requires treatment. The data used to conduct this LCA were acquired from government annual reports, scientific articles, technical consulting reports, laboratory measurements, and assumptions based on previous research. The data are ranked from 1 (highest) to 5 (lowest) based on the Weidema method which has 6 key categories: acquisition method, independence of data supplier, 105 representativeness, data age, geographical correlation, and technological correlation. DQI s will be further discussed in Section 6.3. Figure 6 .3 . LCA Scope and Associated Boundaries Sinc e the system creates digestate that can be used for agricultural applications, air acidification and water eutrophication have been chosen as impact categories to assess. Water consumption is assessed as it is consumed throughout certain processes such as the milking parlor and slope screens . Lastly, untreated cow manure produces CH 4 that is often released into the atmosphere; therefore, global warming potential has been assessed between these systems in comparison to untreated manure . All systems considered , including the CSTR, lagoon , and current waste management practices, are compared using the same functional unit. All systems are being compared as a waste management solution for dairy cow manure, due that all the impacts are evaluated on the basis of kg T S treated, there is no need for allocation with respect to this LCA. 106 6.3 Life Cycle Data Inventory The life cycle inventory (LCI) has been split in to three key sections. The raw material and handling section provide s information regarding the material input into the system and processes before the manure enters the digester. The raw material and handling section also include s the emissions associated with manure storage if a digester was not in place. The processes section provide s data regardi ng the process of anaerobic digestion for a CSTR and a covered lagoon. Furthermore, the third section will cover the processes and outputs after anaerobic digestion has occurred , such as digestate land application . The overall LCI presented in this section includes various data sets that have been identified as key parameters for mass balances (Appendix E & F ). The LCI has been formatted to easily divide key components of data as inputs, processes, or outputs; theref ore, allowing to easily understand the transition between all components and identify necessary data and data gaps to calculate the impacts associated with each scenario. Data quality was evaluated using the Weidema method. There are six indicators to evaluate for data quality : acquisition method, independence of data supplier, representativeness, data age, geographical correlation, and technological correlation. The score ranges from one to five, where one is the best quality and five is the most uncertain. Table 6.1 presents how to apply the indicators based on the pedigree matrix. 107 Table 6. 1 Data Quality Evaluation Using the Weidema Method (Weidema et al., 2004) 6.3. 1 Raw Material and Handling Table 6. 2 presents the inventory for raw material and handling. The first component of the inventory table includes the information and values associated with dairy cow manure production and chemical compositions. Cow manure was chosen as the input for the digester, a process that has been well researched in literature and offers vast data availability from ASABE standards, governmental organizations, and scholarly articles. Key data such as the amount of manure produced per cow per day, the herd size for the study, and average TS and moisture content per kilogram of manure were used to calculate multiple parameters such as tank sizing, daily manure treatment volumes, and energy required to handle a wet ton of manure. These parameters have been utilized to back - calculate initial ene rgy requirements for the sand separation system and pumps necessary to move the manure through the system. 108 For the impact assessments, a key parameter identified are the emissions from cow manure if left untreated. These parameters play key roles in two im pact categories: air acidification potential and global warming potential. In both impact categories, the effects of manure emission s are being compared to a CSTR and a covered lagoon ; therefore , providing a different perspective to this LCA. Other key par ameters are the phosphorus and nitrogen concentrations within the cow manure. Phillys2 online biomass database and ASABE Standards were used to identify the chemical composition of cow manure and digestate to perform the stoichiometric calculations for the presented scenarios. One key assumption that will affect the scenarios analyzed is that all manure is introduced to the digester per day. Most AD systems within farms have a pump system from the storage tanks to the digester, but if the farm scales up, al l the manure will not be possible to introduce to the digester every day. Table 6. 2 . Life Cycle Data Inventory for Raw Material and Handling Component Value Unit Source DQI Dairy Cow Manure Cows 10,000 Head Assumption 4,1,2,3,2,1 Manure 68 kg/day/cow ASABE Standards 2,1,1,4,2,2 Total Solids 8.9 kg/day/cow Volatile Solids 7.5 kg/day/cow TS:VS Ratio 0.8 D imensionless Calculated 2,1,1,4,2,2 Carbon 42.8 % Engler et al. (2010) 1,2,5,4,3,3 Hydrogen 6.1 Nitrogen 2.2 Oxygen 47.7 Sulfur 0.6 Phosphate 0.6 CO 2 emissions 0.31 t CO 2 - e/t TS/y Rotz et al. (201 2 ) 2,1,2,3,2,2 109 Table 6.2 (continued). CH 4 emissions 78 kg CH 4 / cow/yr Rotz (2018) 3,1,5,1,5,1 N 2 O emissions 0.1 kg N 2 O/kg N excreted NH 3 emissions 0.265 k g NH 3 /kg N excreted Bai et al. (2020) 1,1,5,1,4,3 P 2 O 5 emissions 15.9 lbs /ton land applie d McGuire (2017) 3,1,1,1,2,5 Milking Parlor Flow Rate 12 gal/day/cow Dr. Dana Kirk 4,1 ,5,1,3,1 Moisture 98.50 % MWPS (2004) 1,1,1,4,2,1 Total Solids 1.50 % Volatile Solids 1.20 % Recycled Water for Free Stall Barn Volume 100 gal/day/cow Dr. Dana Kirk 4,1,5,1,3,1 Pump 50 HP Running Time for a CSTR 12 hrs Running Time for a lagoon 24 hrs Slope Screen Water Consumption 1 . 0 gal/min Dr. Dana Kirk 4,1,5,1,3,1 Pump 35 HP Running Time for a CSTR 12 hrs Running Time for a lagoon 24 hrs Sand Lane Volume 50 % Tier 1 Model (2018) 2,1,1,1,2,1 TS 25 % VS 20 % 110 Table 6.2 (continued). Slope Screen Thickening for a CSTR Volume 20 % Tier 1 Model (2018) 2,1,1,1,2,1 TS 6 % VS 5 % Slope Screen for Solids Separation for a Lagoon Volume 20 % Tier 1 Model (2018) 2,1,1,1,2,1 TS 6 % VS 5 % Water Consumption 0.5 gal/min 6.3. 2 Process Table 6. 3 holds information and values associated with the anaerobic digestion process for the CSTR and covered lagoon systems. Both systems were scaled to receive the same input of total solids per day. In practice, most digesters are loaded based on total solids, volatile solids, or chemical oxygen demand ratios (AD Operator Training, 2019). As the temporal scope covers the lifetime of these systems, the inventory and impacts associated with building the system were not measured or taken into consideration. Data h a ve been gathered from environmental agencies such as the U.S. Environmental Protection Agency (EPA) and U.S. Department of Agriculture (USDA). From EPA, the Tier 1 Model has provided insight in calculations for certain aspects of the system design. CSTR d igesters are widely studied in literature and the access to various forms of data ha ve allowed the required calculations to be made for this LCA. On the other hand, valuable covered lagoon data has been difficult to obtain since these systems are less properly studied in the anaerobic digestion field. Covered lagoons lack mixing, and this should be considered as it relates 111 to particle settling . This characteristic of covered lagoons will affect the availability of total solids and volatile solids for methanogenic and acetogenic bacteria during the biogas conversion process. Within the impact categories chosen for this LCA, electricity will have a higher impact for the CSTR than the lagoon. CSTR digesters require electricity for supplemental mixing and heating systems, while these systems are rarely seen in covered lagoons. For air acidification potential and global warming potential, part of the impact will be associated with biogas leakage as these are not perfectly sealed systems. Additionally, water consumption has been obtained as part of the design processes or data recovered from standard practice in the free stall barn and the milking parl ors. Table 6. 3 . Life Cycle Data Inventory Anaerobic Digestion Process CSTR HRT 20 days Assumption 4,1,1,1,3,1 Feed Rate 971,633 kg/day Calculated 2,1 ,2,1,2,2 58,298 kg TS/day 52,420 kg VS/day Heating 6,658,400 kWh/yr Calculated 2,1,2,1,2,2 Mixing 224,290 kWh/yr Biogas to RNG 7 0 % VS Assumption 2,1,5,1,3,1 Recycle Liquid 0.42 % TS Dr. Dana Kirk 4,1,5,1,3,1 0.34 % VS Covered Lagoon HRT 30 days Assumption 4,1,1,1,3,1 Feed Rate 4,839,407 kg/day Calculated 2,1,2,1,2,2 67,026 kg TS/day 61,964 kg VS/day Heating 0 kWh/yr Assumption 4,1,1,1,2,1 Mixing 0 kWh/yr 112 Table 6.3 (continued). Biogas to RNG 50 % Assumption 2 ,1, 5 ,1,3,1 Recycle Liquid 1.5 % TS Dr. Dana Kirk 4,1,5,1,3,1 1 % VS 6.3. 3 Outputs and Land Application Ta b le 6. 4 contains relevant information and values regarding the two main anaerobic digestion products: digestate and biogas. Digestate has many different uses such as animal bedding, compost bed ding , and land application. In the LCA analysis, digestate is considered as a direct land application fertilizer for calculations in impact categories. When the digestate is applied, it is replacing the need for a chemical fertilizer, and with soil application , water eutrophication potential will be considered. As part of this potential, the availability of key elements in digestate, such as nitrogen and phos phorus, are presented in the LCI . These values will be compared to the values obtained from the stoichio metric calculations (Appendix G ). By using the stoichiometric calculations presented in Appendix G , nitrogen, and phosphorus conversions from the original cow manure concentrations to digestate concentrations are identified and found to be converted approx imately 25% to ammonia and phosphate forms readily available for soil. Digestate will also have an impact when it comes to air acidification potential and global warming potential. Digestate produc es SO 2 - equivalents and CO 2 - equivalents. Biogas production will be calculated based on the stoichiometric equations and TS availability for both systems. Since both systems are in Arizona as described in the goal and scope section, a key assumption is that both systems will remain relatively with in the mesophilic range (68 °F to 113 °F). Although temperature profiles affect biogas quantity, the pilot research presented in S ection 5 showed that biogas quality remained relatively similar between both systems . One of the highest global warming potent ial parameters within our biogas is methane. According to the 113 EPA, methane has a GWP value of 2 5 kg CO 2 - e q. /kg CH 4 . In both scenarios, biogas is considered a renewable form of energy, so it is displacing the use of natural gas or fossil fuels . Although dis placement of fossil fuels is occurring, it was not accounted for in this LCA as the comparisons are based on input manure rather than products. Table 6. 4 . Life Cycle Data Inventory for Outputs and Land Application Biogas CH 4 40 - 75 % Estefandari et al., (2011) 1,1,2,3,5,1 CO 2 25 - 40 % Nitrogen 0.5 - 2.5 % Oxygen 0.1 - 1 % Hydrogen Sulfide 0.1 - 0.5 % Hydrogen 1 - 3 % Separated Solids to Compost Volume 70 % Tier 1 Model (2018) 2,1,1,1,2,1 TS 20 % VS 17 % Land Application Digestate Desired 43.5 lbs N/acre Wrap (2016) 4,1,4 ,2,2,3 Gasoline Used 0.28 gal/acre Parsons ( 1980 ); Downs (1998) 1,1,1,5,2,2 Fuel Emission 0.00236 t CO 2 - eq./L EPA (2011) 1,1,1,3,2,1 Digestate Carbon 19.7 % Phyllis 2 Database, McCarty et al. (2011) 1,1,1,3,2,3 Hydrogen 2.44 Nitrogen 1.25 Sulfur 0.2 Oxygen 19.6 114 Table 6.4 (continued). Phosphate 0.005 % Phyllis 2 Database, McCarty et al. (2011) 1,1,1,3,2,3 6.3. 4 Data Quality Evaluation For data acquisition methods, the data w ere acquired from valid sources, such as research publications, technical consulting reports, government annual reports, and personal laboratory results. Calculations were performed for a lagoon system based on parti al assumptions from consulting data and lab results due to the difficulty to find a reliable source for this specific scenario. The data quality evaluation for the life cycle inventory is shown in Table 6. 5 . According to the evaluation, the independence of data supplier reached a DQI score o f 1; while the other categories reached a DQI score between 2 and 3. The data were supplied from verified institutions, such as EPA, USDA, ASABE, and research institutions. For data age, there were two sources from Ameri can Society of Agricultural and Biological Engineers (ASABE) and EPA which are less than 20 years old. These data were used because the current research related to this topic still refers to those data sets. Laboratory experiments suggest the data continue s to be used as a standard within the bioenergy field. Most of the data is geographically in the US, which included the southern US. Table 6. 5 . Data Quality Evaluation Summary for LCI Indicator DQI Score Discussion Acquisition Method 2 Calculated data based on measurements Independence of Data Supplier 1 Verified data, information from public or other independent source Representativeness 3 Representative data from smaller number of sites, but from shorter periods 115 Table 6.5 (continued). Data Age 2 Less than five years Geographical Correlation 3 Data from similar production conditions Technological Correlation 2 Data from processes and materials under study but from different enterprises 6.4 Impact Assessment Four impact categories were chosen for Life Cycle Impact Assessment (LCIA): Global Warming Potential (GWP ), Air Acidification Potential (AAP), Water Consumption Potential (WCP) and Water Eutrophication Potential (WEP ) . The classification of each category i s defined by the ISO 1998. The LCIA phase provides an examination of the impact categories mentioned previously as a form to analyze environmental impacts of both scenarios in comparison to the emissions of current waste management systems for dairy cow ma nure . The LCIA provides the analysis of the environmental effects due to processes or products associated with the systems in question. 6.4.1 Global Warming Potential (GWP) GHG s such as CO 2 , N 2 O , and CH 4 . Our current world energy systems are maintained running by fossil fuels such as coal and oil. As a rough approximation , 65% of GHG emission s are due to the utilization of fossil fuels (EPA , 2019 ) . There is a concern across the world to find new renewable energy forms. Cattle management produces roughly 18% GHG emissions around the world ( Esfandiari, Khosrokhavar & Sekhavat, 2011 ) . AD systems have been promoted as a renewable energy system that can reduce global warming potential caused by fossil fuel uses and control emissions from cow manure. 116 Global warming potential (GWP) is the amount of GHG released during the life cycle of a process. This potential can be presented as a metric to compare the various greenhouse impacts to a reference gas , t he most common being CO 2 (Shine, 2009). From the data obtained for both systems, the variables analyzed are electricity consumption, leakage, fuel for land application , and effects of digestate in land appli cation . Conversions utilized for these variables can be found in Table 6. 6 . Table 6. 6 . Global Warming Potential Conversion Values obtained from the TRACI Model and Chen et al., 2015 Components Global Warming Potentials Value Units Biogas CH 4 2 5 kg CO 2 - e q. /kg CH4 CO 2 1 kg CO 2 - e q. /kg CO2 Nitrous Oxide 0.03 kg CO 2 - e q. /kg TS Land Application CH 4 0.000308 kg CO 2 - e q. / kg TS/y CO 2 0.000671 Nitrous Oxide 0.000154 Electricity Consumption 0.707 kg CO 2 - e q. /kWh Fuel Emission 0.00236 t CO 2 - e q. /L The contribution analysis for both scenarios in comparison to the impacts associated with a typical manure management system is presented in Figure 6. 4 . The results are presented in tons of CO2 - equivalents (CO2 - e q. ) . A key assumption proposed before analyz ing the systems was that for both the CSTR and covered lagoon systems, all manure will be treated through the digester and without storage , while for current manure management strategies, the manure will be stored before land applying. Through current waste management practices, the majority of emissions will be released through manure storage , recognizing that manure can produce approximately 74 kg of 117 CH4 per cow per year during storage conditions (Rotz, 2018). Manure storage was only considered for current waste management practices , which is outside of the system boundary, and disregarded for the CSTR and the covered lagoons , i.e., the storage of manu re or digestate was not included in the system boundary. The emissions associated with manure storage could possibly be reduced by the CSTR and covered lagoon digesters , as potent GHG s like methane are captured in biogas and converted to electricity . Figure 6. 4 . Global Warming Potential for Various Parameters for a CSTR, a Covered Lagoon and Dairy Cow Manure per FU 1 Figure 6.5. is introduced to compare only the impacts of a CSTR and a covered lagoon as waste management systems. The least impactful par ameter is the land application of digestate for a CSTR and a covered lagoon. The parameter with the highest impact when considering a CSTR is electricity consumption. CSTR systems require vast energy requirements to sustain operations 1 The scenario for current waste management practices includes storage which is not within the system boundary . CSTR Lagoon Cow Manure Manure Storage 0 0 896,322 Land Application 241 277 0 Electricity Consumption 102,866 7,854 30,275 Fuel for Land Application 778 895 1,468 Leakage 32,459 93,296 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 Global Warming Potential (ton CO2 - eq.) 118 such as mixing and he ating. In comparison, for a covered lagoon, the most impactful parameter is the leakage rate. As demonstrated in the contribution analysis, the potential emissions for a CSTR and a covered lagoon are approximately 80% or below when compared to dairy cow ma nure. In a similar LCA, the impacts of common waste management versus anaerobic digestion or algae treatment for cow manure were presented the GWP for the scenario of anaerobic digestion presented 337 - ton CO 2 - eq. per 100 cows per 20 years. When that value is converted for this LCA, it would provide an approximate value of 33,700 - ton CO 2 - e. This value is lower f in the study as they accounted for carbon sequestration, electricity produced was consumed in site for operations and the offset of commercial fertil izer by utilizing digestate (Zhang et al., 2013). Overall, a CSTR system has higher GWP when compared to a covered lagoon, and both systems mitigate the harmful impacts of manure compared to leaving it untreated. Figure 6. 5 . Global Warming Potential for Various Parameters for a CSTR and a Covered Lagoon per FU CSTR Lagoon Manure Storage 0 0 Land Application 241 277 Electricity Consumption 102,866 7,854 Fuel for Land Application 778 895 Leakage 32,459 93,296 - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 Global Warming Potential (ton CO2 - eq.) 119 6.4. 2 Air Acidification Potential (AAP) oxidation of sulfur, nitrogen , 1985). When absorbed by the atmosphere, these acids can lead to conditions such as acid rain. Air a cidification potential (AAP) is an impact category used to convert processes or materials that form acid rain into common units of sulfur dioxide equivalents ( SO2 - e q. ). Some of the inputs and outputs included in AAP are electricity consumption, digestate production , and biogas leakage. AAP is computed using the conversion values Chen et al. (2015) presented in Table 6. 7 . The energy requirements and TS destruction calculations were utilized paired with the conversions provided by Chen et al. (2015) to convert to acidification values for electricity, leakage, fuel consumption to apply digestate and the effects of digestate on land application. Table 6 . 7 . Air Acidification Potential Conversion Values for Anaerobic Digestion (Chen et al., 2015) Processes Air Acidification Potentials Value Units 1 kWh electricity consumed 0.067 g SO 2 - e q. 1 kg of fuel consumed 0. 00054 kg SO 2 - e q. 1 dry ton during AD process 0.17 kg SO 2 - e q. 1 dry ton AD effluent in land application 0.073 kg SO 2 - e q. T he contribution analysis for AAP is presented in Figure 6.6 in kilograms of sulfur dioxide equivalents ( SO 2 - e q . ). Figure 6.6 presents the impacts of both systems when compared to current manure management strategies . It can be denoted from the contribution analysis that current 120 manure ma nagement systems provide significant AAP when compared to a CSTR and a covered lagoon. Manure can be land applied as an organic fertilizer and the main parameter for AAP is its land application. For purposes of this analysis, the emissions related to manur e storage in current manure management systems was not included in the system boundary for the CSTR and the covered lagoon. Manure itself provides a significant amount of emissions to AAP if not captured by a CSTR or a covered lagoon and converted to elect ricity or RNG. Figure 6.6 . Air Acidification Potential for Various Parameters in a CSTR, a Covered Lagoon and Dairy Cow Manure per FU 2 Figure 6.7 is presented to compare the impacts between a CSTR and a covered lagoon closely. The least impactful parameter for both systems presented is the fuel utilized for land application. In both systems , the two most impactful parameters are leakage a nd the land application of digestate, respectively. The covered lagoon has a larger air acidification potential in 2 The scenario for current waste management practices includes storage which is not within the system boundary . CSTR Lagoon Cow Manure Fuel for Land Application 178 205 336 Electricity Consumed 9,748 744 2,869 Land Application 15,821 18,190 5,724,393 Leakage 37,763 108,542 - 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 Air Acidification Potential (kg SO2 - eq.) 121 digestate application due that a lagoon produces larger volumes of digester with higher TS concentrations. For both systems, the most impactf ul category is biogas leakage. Overall, the covered lagoon has approximately twice the impact in this category when compared to a CSTR. Figure 6.7 . Air Acidification Potential for Various Parameters in a CSTR and a Covered Lagoon per FU 6.4. 3 Water Consu mption Potential (WCP) Only a rough approximation of 2% of all water on earth is freshwater. It is estimated that one out of six people on Earth does not have access to drinking water. Water consumption in either direct or indirect manner provides a highly indicative aspect of the environmental impacts in a n ISO 14040 :2006 , water consumption potential (WCP) is described as water that has been removed from the watershed and cannot be returned. This is impact is presented in volumes of freshwater consumed such as gallons or liters. CSTR Lagoon Fuel for Land Application 178 205 Electricity Consumed 9,748 744 Land Application 15,821 18,190 Leakage 37,763 108,542 - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 Air Acidification Potential (kg SO2 - eq.) 122 Water is a scarce resource and even though it does not seem obvious, digesters r equire vast amounts of water for different purposes, such as heating systems or power washing. For example, MSU SCAD only utilizes fresh water for power washing the digester. For digester heating, the system utilizes a glycol mix that is replaced every 2 y ears. Even though this glycol mix requires water to be produced, it is outside of the referent scope in this project. Lagoons do not require a supplemental heating system; therefore, heating system water consumption is not being considered in this impact a ssessment. For this impact assessment, the t hree contributions taken into consideration are: milking parlor, slope screen and power washing. Power wash data was obtained from calculations from digester design and system size calculations, while the slope screen and milking parlor w ere assumed from similar system design parameters. The contribution analysis for WCP is presented in Figure 6.8. The unit presented for the contribution is gallons. For all the systems, milking parlor will have a higher potential when compared to power washing and slop e screens. The water utilized in the milking parlor is utilized to spray the manure deposited when milking the cow and avoid bacteria spreading from manure to milk ing equipment. The slope screens and power washing are directly related to digester design and operations. The lagoon will require less water to clean the slope screens due that there is only one slope screen present in covered lagoon designs. In the CSTR , there are two slope screen s present from one of which is utilized to thicken the influent before entering the digester. Power washing will be based on di gester size and individual operations. It can be noted that when comparing all the systems, the main c oncern is the amount of water utilized for the milking parlor. Although cow manure scenario provides less WCP, the slope screen and power washing are associated to digester design and not to is the waste management procedures at barns . Overall, the 123 WCP of digester operations is only a small percentage when compared to manure management strategies at barns. Figure 6. 8 . Contribution Analysis for Water Consumption in a CSTR , a Covered Lagoon and Dairy Cow Manure per FU 3 6.4.4 Water Eutrophication Potential (WEP) Eutrophication is defined as the excess of nutrients available in a water body that cau se catastrophic events such as algal bloom s . When phosphorus or nitrogen are introduced to a water system , algal blooms develop due to nut rient accumulation . The bloom is not necessarily the major problem. The main issue occurs when the algae is broken down by bacteria present in the water , which are defined as low oxygen areas causing harm in marine life (Mueller & Helsel, 1996). 3 The scenario for current waste management practices includes storage which is not within the system boundary . CSTR Lagoon Cow Manure Slope Screen 15,768,000 10,512,000 Milking Parlor 876,000,000 876,000,000 876,000,000 Power Wash 10,678,978 79,783,093 - 200,000,000 400,000,000 600,000,000 800,000,000 1,000,000,000 1,200,000,000 Water Consumption Potential (gallons) 124 The primary cause of eutrophication within the U.S. is the runoff of nitrogen and phosphorus from chemical fertilizers or septic systems (NOAA, 2017). Chemical fertilize rs are often derived from materials such as petroleum or other forms of fossil fuels. Chemical fertilizers have been used for decades due to their fast release of nutrients into the soil. On the other hand, organic fertilizers have slowly gained popularity since they are derived from animal or plant matter but require the availability of various microorganisms in the ground in order to release nutrients into the soil (Tisdale et al., 1985). According to Guinée et al. ( 200 2 ), w ater eutrophication potential ( WEP) is defined as: the impacts on terrestrial and aquatic environments due to over - fertilization or excess supply of nutrients, particularly focusing on the most important substances nitrogen (N) and phosphorus (P) WEP can be presented as either mass of nitrogen equivalents (kg N - e q. ) or phosphate equivalents ( PO 4 - e q. ) . In the scenarios presented, digestate is a form of non - chemical or organic fertilizer being utilized as a substitution for current chemical fertilizers. D igestate has been studied in literature as an adequate substitute for chemical fertilizer due to concentrations of both nitrogen and phosphorus. Based on the TRACI model, the conversion utilized are listed in Table 6. 8 . Stoichiometric equations and additional assumptions with regards to conversions of compounds in solid are presented in Appendix G, Section G.2 and Section G.3. Table 6. 8 . Water Eutrophication Potential Conversion Values Obtained from the TRACI Model Element Water Eutrophication Potentials Value Unit Nitrogen 0.9864 k g N - e q. /kg substance Phosphorus 7.290 125 The contribution analysis for WEP in both scenarios in comparison to cow manure is presented in Figure 6.9 . The contributions are presented as kilograms of nitrogen equivalents (kg N - e). P hosphorus has the highest impact in all the scenarios when compared to nitrogen. Phosphorus represents a vast proportion of the contribution analysis due to higher potential of impact when compared to nitrogen . Both the CSTR and the covered lagoon had less WEP when compared to the cow manure scenario. For both scenarios, t he output whether it was digestate or post storage manure, was assumed to be land applied as - is. Although certain values have been expressed for the conversion of phosphorus and nitrogen during manure storage in literature, there is a lack of data available with regards to the transformation of certain molecules in cow manure during storage. Through the AD process, elemental nitrogen and phosphorus are converted to compounds with ammonia and phosphate, respectively. Ammonia and phosphate compounds a re readily available for soil conversions and plant adsorption, when compared to elemental nitrogen and phosphorus. Due to these conversions , f urther investigation should be performed depending o n soil type and composition within the specified geographical scope. 126 Figure 6.9 . Contribution Analysis for Water Eutrophication Potential in a CSTR, a Covered Lagoon and Dairy Cow Manure per FU 4 6.5 Interpretation 6.5.1 Sensitivity Analysis A sensitivity analysis is a tool utilized to measure the change in impacts based in changes in key parameters influencing the model and re porting which parameters within each impact are influenced greatly by changes in the model. 6.5.1.1 Global Warming Po tential (GWP) The sensitivity analysis for the CSTR and covered lagoon system was performed by subtracting or adding 50% of methane yield to the stoichiometric formula utilized in the base case scenario. The parameters analyzed for global warming potential were leakage, electricity 4 The scenario for current waste management practices includes storage which is not within the system boundary . CSTR Lagoon Cow Manure Phosphorus 22,076 25,381 27,463 Nitrogen 9,230 10,612 13,850 - 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 Water Eutrophication Potential (ton N eq.) 127 consumption, fuel for land application and effects of digestate on land application. The sensi tivity analysis for a CSTR is presented in Figure 6.10 (top); while the sensitivity for a covered lagoon, Figure 6.11 (bottom). The variation in methane yield has also been plotted in both figures to present correlation between parameters. For both system, electricity consumption, fuel for land application and effects of digestate on land application will show either no ne or minimally sensitive changes with regards to methane yield. The variation in methane yield influenced parameters that were directly cor related to biogas quality such as leakage. For both scenarios , the most sensitive parameter is leakage. Leakage is associated to the presence of gases in biogas such as methane and carbon dioxide. The variations in methane concentration had inversely propo rtional relations to car b on dioxide concentrations ; and therefore, changes in biogas composition affected the AAP of biogas leakage. Figure 6. 10 . CSTR Sensitivity Analysis for Global Warming Potential per FU (15,000) (10,000) (5,000) 0 5,000 10,000 15,000 -50% 0% 50% Global Warming Potential (ton CO2 - eq.) Leakage Fuel for Land Application Electricity Consumption Land Application Methane Yield 128 Figure 6. 11 . Covered Lagoon Sensitivity Analysis for Global Warming Potential per FU 6.5.1. 2 Air Acidification Potential (AAP) The sensitivity analysis for a CSTR and a covered lagoon was performed by subtracting or adding 50% of cow manure concentration to the base case scenario. T he following parameters represented: leakage, electricity consumption, fuel for land application and effects on land application from digestate. The sensitivity analysis for a CSTR is presented in Figure 6.12 (top); while the sensitivity for a covered lago on, Figure 6.13 (bottom). In the CSTR system, all the parameters showed a sensitivity to increase or decrease in manure concentrations to some extent . The least sensitive parameter for a CSTR and a covered lagoon is fuel for land application . The second le ast sensitive parameter fo r both systems is electricity consumption. Electricity consumption presented a greater sensitivity for a CSTR than a covered lagoon . The electricity consumption in covered lagoons is related to components such as pumps, but it s le ss sensitive than CSTR, due that it does not include heating or mixing system s . For both systems, leakage presented the most sensitive parameter , followed by land application of digestate. Both CSTR and lagoons (50,000) (40,000) (30,000) (20,000) (10,000) 0 10,000 20,000 30,000 40,000 50,000 -50% 0% 50% Global Warming Potential (ton CO2 - eq.) Leakage Fuel for Land Application Electricity Consumption Land Application Methane Yield 129 provide digestate as a by - product , which is correlated to the concentration of manure introduced to the system. As mentioned previously, lagoon systems produce higher amounts of digestate when compared to CSTR . Figure 6. 12 . CSTR Sensitivity Analysis for Air Acidification Potential per FU Figure 6. 13 . C overed Lagoon Sensitivity Analysis for Air Acidification Potential per FU (25,000) (20,000) (15,000) (10,000) (5,000) 0 5,000 10,000 15,000 20,000 25,000 -50% 0% 50% Air Acidification Potential (kg SO2 - eq.) Leakage Land Application Electricity Consumed Fuel for Land Application (60,000) (40,000) (20,000) 0 20,000 40,000 60,000 -50% 0% 50% Air Acidification Potential (kg SO2 - eq.) Leakage Land Application Electricity Consumed Fuel for Land Application 130 6.5.1. 3 Water Consumption Potential (WCP) The sensitivity analysis for the CSTR and lagoon system was performed by subtracting or adding 50% of cow manure concent ration to the base case scenario. The t hree p rocesses assessed are: milking parlor, power wash and slope screens . The sensitivity analysis for a CSTR is presented in Figure 6.14 (top); while the sensitivity for a covered lagoon, Figure 6.15 (bottom). Power wash and slope screens has n one or minimal sensitivity changes with respect to cow manure. Both of these parameters are highly dependent on system design , rather than herd size . For both systems , the most sensitive parameter is related to the milking parlor due that water consumption in the milking parlor is based in a per cow basis. Figure 6. 14 . CSTR Sensitivity Analysis for W ater Consumption Potential per FU (500,000,000) (400,000,000) (300,000,000) (200,000,000) (100,000,000) - 100,000,000 200,000,000 300,000,000 400,000,000 500,000,000 -50% 0% 50% Water Consumption Potential (gallons) Power Wash Milking Parlor Slope Screen 131 Figure 6. 15 . Covered Lagoon Sensitivity Analysis for Water Consumption Potential per FU 6.5.1.4 Water Eutrophication Potential (WEP) The sensitivity analysis for the CSTR and lagoon system was performed by subtracting or adding 50% of cow manure concentration to the base case scenario . The sensitivity analysis for a CSTR is presented in Figure 6.16 (top); while the sensitivity for a covered lagoon, Figure 6.17 (bottom). B oth systems have a higher sensitivity towards changes in phosphorus. In both systems, phosphorus h as a higher potential for eutrophication than nitrogen . (500,000,000) (400,000,000) (300,000,000) (200,000,000) (100,000,000) 0 100,000,000 200,000,000 300,000,000 400,000,000 500,000,000 -50% 0% 50% Water Consumption Potential (gallons) Power Wash Milking Parlor Slope Screen 132 Figure 6.16. CSTR Sensitivity Analysis for Water Eutrophication Potential per FU Figure 6.1 7 . Covered Lagoon Sensitivity Analysis for Water Eutrophication Potential per FU (15,000) (10,000) (5,000) 0 5,000 10,000 15,000 -50% 0% 50% Water Eutrophication Potential (ton N eq.) Nitrogen Phosphorus (15,000) (10,000) (5,000) 0 5,000 10,000 15,000 -50% 0% 50% Water Eutrophication Potential (ton N eq.) Nitrogen Phosphorus 133 6.5.2 Consistency and Completeness Check The consistency check is a form to verify that assumptions, methods , and data used throughout the LCA process is consistent with the goal and scope of the study . It allows use to revise the consistency of the data used to compare systems to one another. The consistency check and explanations of inconsistency are explained within Table 6.9. The overall data adequately shows consistency to support goal a nd scope of the study. Table 6.9 . Checklist and Inconsistencies based on Data Quality Category Checklist and Inconsistencies Data Source The CSTR scenario was heavily based on literature, while the covered lagoon was heavily based on assumptions and studies of individual scenarios Data Accuracy For both alternatives, a detailed process flow diagram was presented but in real life scenario, s ystem design will be highly dependent on individu al site needs. Technological Representation Both scenarios are available at the full scale , but the covered lagoon has a l ess DQI for technological representation due to the lack of commercial scale data available when compared to a CSTR. Temporal Representation Both technologies are utilized up to date. Geographical Representation Both technologies include data from the United States, but data is also included from Europe where these systems are more predominant at the industry scale. System Boundary, Assumption and Model Both systems serve as a waste management system and produce the same co - products. Completeness check aims to assure that the required data for interpretation are available and complete. If there is a case that data is not completed, a verification must be done whether the incomplete data will affect the goal and scope of the study. A control list has been made that include all life cycle stages and the impact assessment indicators which are AAP , GWP, WEP and 134 WCP . Tab le 6.1 0 presents a summary of the results for the CSTR and table 6.11 presents a summary of the results for the covered lagoon . Throughout both studies, there might be incompletion with regards to the effects of settling in a covered lagoon and the impact of digestate for land application in both scenarios. At the moment, there is a lack of data available with regards to settling rates and effects on a covered lagoon with regards to feedstocks. The settling might affect parameters such as biogas production and digestate quality. Moreover, there was a lack of data available presenting elemental changes between the original cow manure introduced into a digester and the digestate retrieved. The Phyllis2 database did not contain elemental analysis from the same location. There was a lack of studies available comparing the elemental conversion between cow manure and digestate. Additionally, digestate contains compounds such as ammonia and phosphate in which there is a lack of research available towards the presen ce of this within digestate and their forms and their conversions within soil in comparison to chemical fertilizers. Table 6.10 . Completeness Check for a CSTR Life cycle stage CSTR Complete Required Actions Input: Cow Manure X Yes - Process: Anaerobic Digester X Yes - Output: Digestate X Data Gap There is lack of data available with regards to elemental analysis of digestate. Additionally , t here is a lack of data comparing the infiltration of digestate into soil and the interactions of elements available in digestate and the availability for plant use. Output: Biogas X Yes - X: data available n.a.: not applicable 135 Table 6.11 . Completeness Check for a Covered Lagoon Life cycle stage Covered Lagoon Complete Required Actions Input: Cow Manure X Yes - Process: Anaerobic Digester X Data Gap There is a lack of data explaining how events such as settling affect the co - products of biogas and digestate. Output: Digestate X Data Gap There is lack of data available with regards to elemental analysis of digestate. Additionally , t here is a lack of data comparing the infiltration of digestate into soil and the interactions of elements available in digestate and the availability for plant use. Output: Biogas X Yes - X: data available n.a.: not applicable 6. 6 Overall Life Cycle Comparison and System Recommendation The purpose of this life cycle assessment was to understand the environmental impacts of different anaerobic digester types in comparison to current manure management systems. The two types chosen for this analysis were a CSTR and a covered lagoon. Contribution analyses for the impacts are presented in Section 6.4 and sensitivity analysis are presented in Section 6.5.1. A summary of the total impacts for each scenario can be found in Table 6.1 2 . Table 6.1 2 . Overall System Comparison for all Impact Categories analyzed for a CSTR, Covered Lagoon and Dairy Cow Manure per Functional Unit Impact Units Scenario (Per FU) CSTR Covered Lagoon Dairy Cow Manure GWP t CO 2 - e q . 136,344 102,323 928,065 AAP kg SO 2 - e q . 63,511 127,681 5,727,598 WCP gal 902,446,978 966,295,093 876,000,000 WEP kg N - e q . 31,305 35,992 41,314 136 This LCA found both systems, a CSTR and a covered lagoon , have less environmental burdens when compared to current waste management systems. When comparing a CSTR and a covered lagoon, the covered lagoon provides less environmental burdens with respect to GWP; while the CSTR with respect to AAP and WEP. In GWP a nd AAP, electricity consumption played a key role for the CSTR scenario. CSTR digesters are well studied in literature and have been widely implemented across the United States as the second most common digester type. This type of digesters has supplementa l heating and mixing systems which have been demonstrated in literature to require vast amounts of energy input . The lagoon system does not require this supplemental heating or mixing systems; but this can lead to settling which has not been well recorded in literature . Also, there is a lack of literature presenting case studies with regards to lagoon operations. As an overall conclusion, the CSTR seems to possess less environmental burdens than a covered lagoon, but both systems possess less environmental burdens than current manure management systems. Both systems analyzed in this LCA provide a waste management solution. Both systems produce organic fertilizer and biogas that can be converted to renewable electricity or RNG . Based on the overall comparison , either system can be chosen based on stakeholder needs and resource availability. A covered lagoon can be implemented as a low - cost low technology waste management system instead of a CSTR . Both systems have various benefits that can be associated with d target audience. 137 7 . OVERALL CONCLUSIONS AND RECOMMENDATIONS 7.1 B iochemical methane potential testing The BMP tests for all conditions were anaerobically biodegradable. There was no difference in biogas quantity and quality between 30°C non - mixed, 39 °C non - mixed, and 39°C mixed. BMP trials have been used mainly to determine the ideal biodegradability of the m aterial. Temperature did not influence the biodegradability of the material to great extent when temperatures where in the mesophilic range. The idea that an additional 9 ° C within digester temperature might not be necessary in order to obtain higher biog as quantity and quality. This would represent a theoretical energy reduction of approximately 30% for a change in 9 degrees. The energy reduction in heating requirements for digesters could provide incentives to opt for covered lagoons. Covered lagoons hav e been believed to not be able to produce the same biogas quality and quantity as a CSTR; but through this BMPs, might be able to provide the same biogas quality and quantity if an operating temperature of 30 o C can be maintained to promote the growth of me thanogens at the lower end of the mesophilic range. The BMPs allowed to provide a backbone for the idea that covered lagoons are due offer material biodegradability and therefore should not be disregarded as an optional waste management system when compar ed to a CSTR. 7.2 Pilot data The mai n purpose of pilot testing was to run pilots at similar conditions to a covered lagoon and compare the effects of biogas quantity and quality due to temperature. The pilot research provided intriguing results for the purpo ses of this research. The mesophilic condition was able to produce higher biogas yield in comparison to psychrophilic and unregulated conditions. The 138 unregulated pilots were able to provide higher methane yields than the pilots maintained at mesophilic tem peratures. There was no indication of inhibition due to changes in pH; but inhibitions associated to pH could have probably be encountered if the project were run for a longer timeline. TSVS reduction were achieved in all the pilots above 45% indicated the degradation of material associated to biogas production. The pilots provided relevant information with regards to the idea that biogas quality can be achi eved at lower temperatures. T he biogas quantity aspect might require more in - depth analysis of variations in other parameters, or the simulation of geographical temperatures where covered lagoons might be located. 7 .3 Life Cycle Analysis The life cycle analysis provided a foundation for future work regarding LCA analysis on th ese systems. The CSTR had lowest environmental impacts when compared to a covered lagoon. Both systems provided reduction of emissions when compared to current manure waste management systems. Both systems provided cleaner solutions with additional benefits that can be associated to revenue. Although the CSTR had lower impact s , covered lagoon proves to be a viable solution with a low cost as a waste management system. In both systems, leakage proved to be a sensitive factor for both scenarios when it came to global warming potential. Digesters have been believed to be carbon negative systems and further research should be performed in order to identify how this leakage can be reduced and possibly avoided . In another aspect, leakage represents monetary losses due that a volume of biogas towards electricity or RNG is lost. No system will be leakage free, but a reduction of these values obtained 139 by EPA should be analyzed in order to obtain minimum leakage and therefore reduction in global warming potential. 7 .4 Future Work Given the limited data availability with respect to covered lagoons, the implementation of new studies to fill the data gap could be appreciated. The pil ot testing provided interesting results with regard to gas quality. There is the availability of studies presenting the phenomenon but lack on the why of this occurrence . The COVID - 19 pandemic provided a shorten amount of time for testing. The pandemic limited the availability to run the testing for 60 days instead of 45 or run 5 HRTs instead of 3. Longer HRTs and experimentation time could have provided a more in - depth ana lysis of parameters presented . If more HRTs have been analyzed, extra data for unregulated pilots during the winter season could have been obtained . Additionally, it would have been interesting analyzing the mixing patterns for these systems; but the time allotted for a master program did not allowed for the complexity of studying effects of mixing on digester systems. There is a lack of case studies comparing the performance of different digester types to one another . There is a lack of data available co mparing individual digester operations and conveying results about how operations might be affecting biogas quantity and quality. Although most of the data available for anaerobic digestion research is based on lab scale models, there should be more resear ch on already implemented commercial scale sites. For future studies, it would be beneficial for the industry to compare these systems to a power plant, or the impacts associated with chemical fertilizers; du e that both systems, a CSTR and a covered lagoon, will provide renewable energy forms and organic fertilizer. A possible future study could be the impacts associated with producing 1 kWh in an anaerobic digestion system versus 1 kWh in a power plant 140 APPEND ICIES 141 Appendix A . R - Script and Results for BMP data A.1 R - script and Results for Cumulative Biogas Production ## Statistical analysis for BMP Biogas ## Feb 17, 2021 CREATED MIB # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be u sed as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } 142 data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". ##load biogas.txt con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFINING FACTORS ### Abbreviations ## A - 10 C, non - mixed ## B - 20 C, non - mixed ## C - 30 C, non - mixed ## D - 39 C, non - mixed ## E - 39 C, mixed metadata $ Temp < - factor (metadata $ Temp) ##Factor statement # 1. Effects of Temp ## one - way ANOVA fit1 < - aov (Cumul_Gas ~ Temp, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 4175174 1043794 92.67 <2e - 16 *** ## Residuals 276 3108698 11263 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparisons Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Cumul_Gas ~ Temp, data = metadata) ## ## $Temp 143 ## diff lwr upr p adj ## B - A 34.181818 - 21.38975 89.75 338 0.4424396 ## C - A 244.745455 189.17389 300.31702 0.0000000 ## D - A 278.456113 223.60785 333.30437 0.0000000 ## E - A 270.249216 215.40096 325.09748 0.0000000 ## C - B 210.563636 154.99207 266.13520 0.0000000 ## D - B 244.274295 189.42603 299.12256 0.0000000 ## E - B 236.067398 181.21914 290.91566 0.0000000 ## D - C 33.710658 - 21.13760 88.55892 0.4432553 ## E - C 25.503762 - 29.34450 80.35202 0.7057045 ## E - D - 8.206897 - 62.32219 45.90839 0.9936747 #Biogas data summary Gas_data1 < - data_summary (metadata, varname= " Cumul_Gas" , groupnames= "Temp " ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate Gas_data1 ## Temp Cumul_Gas sd ## 1 A 37.18182 20.35799 ## 2 B 71.36364 42.97086 ## 3 C 281.92727 137.58726 ## 4 D 315.63793 132.97588 ## 5 E 307.43103 129.53806 #2. Plot Gas_production1 < - data_summary (metadata, varname= "Cumul_Gas" , groupnames= c ( "Temp" )) Gas_producti on1 $ Temp = as.factor (Gas_production1 $ Temp) Gas_production1 ## Temp Cumul_Gas sd ## 1 A 37.18182 20.35799 ## 2 B 71.36364 42.97086 ## 3 C 281.92727 137.58726 ## 4 D 315.63793 132.97588 ## 5 E 307.43103 129.53806 box_1 < - ggplot (Gas _production1, aes ( x= Temp, y= Cumul_Gas, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Cumul_Gas - sd, ymax= Cumul_Gas + sd), width= 0.2 , pos ition= position_dodge ( 0.9 )) + 144 xlab ( "Temperature" ) + ylab ( "Cumulative biogas production (mL)" ) + ylim ( 0 , 500 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_ fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_1 Figure A.1 . Average Cumulative Biogas Production in BMP bottles, Average of Trials 1, 2 & 3 145 A.2 R - script and Results for Methane Concentration ## Statistical analysis for BMP Methane ## Feb 17, 2021 CREATED MIB # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ----------------------------- -------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the varia ble #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) 146 return (data_sum) } # ANALYSIS ------------------------------------------------------------- -- ## the .txt file needs to be saved as the type of "Tab delimited". ##load methane.txt con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFINING FACTORS ### Abbreviations ## A - 10 C, non - mixed ## B - 20 C, non - mixed ## C - 30 C, non - mixed ## D - 39 C, non - mixed ## E - 39 C, mixed metadata $ Temp < - factor (metadata $ Temp) ##Factor statement # 1. Effects of Temp ## one - way ANOVA fit1 < - aov (Methane ~ Temp, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 10725 2681.3 51.7 <2e - 16 *** ## Residuals 55 2852 51.9 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparisons Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Methane ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 13.5000000 5.208074 21.791926 0.0002443 ## C - A 32.4166667 24.124741 40.708592 0.0000000 147 ## D - A 32.8333333 24.541408 41.125259 0.0000000 ## E - A 32.5833333 24.291408 40.875259 0.0000000 ## C - B 18.9166667 10.624741 27.208592 0.0000003 ## D - B 19.3333333 1 1.041408 27.625259 0.0000002 ## E - B 19.0833333 10.791408 27.375259 0.0000003 ## D - C 0.4166667 - 7.875259 8.708592 0.9999062 ## E - C 0.1666667 - 8.125259 8.458592 0.9999976 ## E - D - 0.2500000 - 8.541926 8.041926 0.9999878 #Biogas data summary Methane_data1 < - data_summary (metadata, varname= "Methane" , groupnames= "Te mp" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate Methane_data1 ## Temp Methane sd ## 1 A 21.00000 10.099505 ## 2 B 34.50000 11.658005 ## 3 C 53.41667 3.342790 ## 4 D 53.83333 2.480225 ## 5 E 53.58333 2.020726 #2. Plot Methane_production1 < - data_summary (metadata, varname= "Methane" , groupnames= c ( "Temp" )) Methane_production1 $ Temp = as.factor (Methane_production1 $ Temp) Methane_production1 ## Temp Methane sd ## 1 A 21.00000 10.099505 ## 2 B 34.50000 11.658005 ## 3 C 53.41667 3.342790 ## 4 D 53.83333 2.480225 ## 5 E 53.58333 2.020726 box_1 < - ggplot (Methane_production1, aes ( x= Temp, y= Methane, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Methane - sd, ymax= Methane + sd), width= 0.2 , positio n= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Methane Concentration (%)" ) + ylim ( 0 , 65 ) + labs ( title = "" , sub 148 title= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_1 Figure A.2. Methane Content in BMP bottles, Average of Trials 1, 2 & 3 149 A.3 R - script and Results for Total Solids ## Statistical analysis for TS ## Feb 17, 2021 CREATED MIB # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package --------- ---------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [ y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], n a.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) 150 return (data_sum) } # ANALYSIS ------------------------------------ --------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFINING FACTORS ### Abbreviations ## A - 10 C, non - mixed ## B - 20 C, non - mixed ## C - 30 C, non - mixed ## D - 39 C, non - mixed ## E - 39 C, mixed metadata $ Temp < - factor (metadata $ Temp) ##Factor statement ## TS PRE ANALYSIS ------------------------------------------------------- --- ## one - way ANOVA fit1 < - aov (TS_P re ~ Temp, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 1687224 421806 0.126 0.972 ## Residuals 40 134093910 3352348 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparisons Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = TS_Pre ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 549.44444 - 1915.689 3014.577 0 .9681291 ## C - A 87.00000 - 2378.133 2552.133 0.9999756 ## D - A 100.88889 - 2364.244 2566.022 0.9999559 ## E - A 115.88889 - 2349.244 2581.022 0.9999234 ## C - B - 462.44444 - 2927.577 2002.689 0.9830441 ## D - B - 448.55556 - 2913.689 2016.577 0.9848636 151 ## E - B - 433. 55556 - 2898.689 2031.577 0.9866714 ## D - C 13.88889 - 2451.244 2479.022 1.0000000 ## E - C 28.88889 - 2436.244 2494.022 0.9999997 ## E - D 15.00000 - 2450.133 2480.133 1.0000000 # data summary TS_data1 < - data_summary (metadata, varname= "TS_Pre" , groupnames= "Temp" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate TS_data1 ## Temp TS_Pre sd ## 1 A 13255.11 2054.874 ## 2 B 13804.56 2042.183 ## 3 C 13342.11 1659.064 ## 4 D 13356.00 1546.983 ## 5 E 13371.00 1795.291 #2. Plot TS1 < - data_summary (metadata, varname= "TS_Pre" , groupnames= c ( "Temp" )) TS1 $ Temp = as.factor (TS1 $ Temp) TS1 ## Temp TS_Pre sd ## 1 A 13255.11 2054.874 ## 2 B 13804.56 2042.183 ## 3 C 13342.11 1659.064 ## 4 D 13356.00 1546.983 ## 5 E 13371.00 1795.291 box_1 < - ggplot (TS1, aes ( x= Temp, y= TS_Pre, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0 .9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Pre - sd, ymax= TS_Pre + sd), width= 0.2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Total Solids Pre - digestion (mg/L)" ) + ylim ( 0 , 20000 ) + labs ( titl e = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), 152 axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" , "blue" , "blue" )) box_1 Figure A.3. Average Pre - digestion Content with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ## TS POST ANALYSIS ------------------------------------------------------ ---- ## one - way ANOVA fit2 < - aov (TS_Post ~ Temp, data = metadata) summary (fit2) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 29857000 7464250 2.601 0.0504 . ## Residuals 40 114811330 2870283 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey2 < - TukeyHSD (fit2, conf.level= 0.95 ) #Tukey multiple comparisons Tukey2 153 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = TS_Post ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A - 422.3333 - 2703.349 1858.6820 0.9838506 ## C - A - 1509.1111 - 3790.126 771.9042 0.3393481 ## D - A - 1740.6667 - 4021.682 540.3487 0.2083140 ## E - A - 2144.2222 - 4425.238 136.7931 0.0742095 ## C - B - 1086.7778 - 3367.793 1194.2376 0.6555897 ## D - B - 1318.3333 - 3599.349 962.6820 0.4750735 ## E - B - 1721.8889 - 4002.904 559.1264 0.2173855 ## D - C - 231.5556 - 2512.571 2049.4598 0.9983897 ## E - C - 635.1111 - 2916.126 1645.9042 0.9304898 ## E - D - 403.5556 - 2684.571 1877.4598 0.9863725 #data summary TS_data2 < - data_summary (metadata, varname= "TS_Post" , groupnames= "Temp" ) TS_data2 ## Temp TS_Post sd ## 1 A 12651.11 2110.252 ## 2 B 12228.78 2244.987 ## 3 C 11142.00 1449.052 ## 4 D 10910.44 1137.758 ## 5 E 10506.89 1209.977 #2. Plot TS2 < - da ta_summary (metadata, varname= "TS_Post" , groupnames= c ( "Temp" )) TS2 $ Temp = as.factor (TS2 $ Temp) TS2 ## Temp TS_Post sd ## 1 A 12651.11 2110.252 ## 2 B 12228.78 2244.987 ## 3 C 11142.00 1449.052 ## 4 D 10910.44 1137.758 ## 5 E 10506.89 1209.977 box_2 < - ggplot (TS2, aes ( x= Temp, y= TS_Post, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Post - sd, ymax= TS_Post + sd), width= 0.2 , positio n= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Total Solids Post - Digestion (mg/L)" ) + ylim ( 0 , 15000 ) + labs ( ti tle = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), 154 axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legen d.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" , "blue" , "blue" )) box_2 Figure A.4. Average Post - digestion Content with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ## TS DESTROYED ANALYSIS ------------------------------------------------- --------- ## one - way ANOVA fit3 < - aov (TS_Destroyed ~ Temp, data = metadata) summary (fit3) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 27856208 6964052 19.37 6.22e - 09 *** ## Residuals 40 14380091 359502 155 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey3 < - Tuke yHSD (fit3, conf.level= 0.95 ) #Tukey multiple comparisons Tukey3 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = TS_Destroyed ~ Temp, data = metadata) ## ## $Temp ## diff lwr up r p adj ## B - A 971.3333 164.06736 1778.599 0.0114505 ## C - A 1596.0000 788.73403 2403.266 0.0000142 ## D - A 1841.6667 1034.40069 2648.933 0.0000009 ## E - A 2260.1111 1452.84514 3067.377 0.0000000 ## C - B 624.6667 - 182.59931 1431.933 0.1969401 ## D - B 870.3333 63.06736 1677.599 0.0290328 ## E - B 1288.7778 481.51180 2096.044 0.0004369 ## D - C 245.6667 - 561.59931 1052.933 0.9064558 ## E - C 664.1111 - 143.15486 1471.377 0.1507497 ## E - D 418.4444 - 388.82153 1225.710 0.5807905 #data summary TS_data3 < - dat a_summary (metadata, varname= "TS_Destroyed" , groupnames= "Te mp" ) TS_data3 ## Temp TS_Destroyed sd ## 1 A 604.000 260.4765 ## 2 B 1575.333 606.5124 ## 3 C 2200.000 447.6933 ## 4 D 2445.667 667.4684 ## 5 E 2864.111 846.0868 #2. Plot TS3 < - data_summary (metadata, varname= "TS_Destroyed" , groupnames= c ( "Temp" )) TS3 $ Temp = as.factor (TS3 $ Temp) TS3 ## Temp TS_Destroyed sd ## 1 A 604.000 260.4765 ## 2 B 1575.333 606.5124 ## 3 C 2200.000 447.6933 ## 4 D 2445.667 667.4684 ## 5 E 2864.111 846.0868 156 box_3 < - ggplot (TS3, aes ( x= Temp, y= TS_Destroyed, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Destroyed - sd, ymax= TS_Destroyed + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Total Solids Destroyed (mg/L)" ) + ylim ( 0 , 4000 ) + labs ( title = " " , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_3 Figure A.5. Average Reductions with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 ## TS REDUCTION ANALYSIS ------------------------------------------------- --------- ## one - way ANOVA 157 fit4 < - aov (TS_Reduc ~ Temp, data = metadata) summa ry (fit4) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 1517.6 379.4 26.46 9.01e - 11 *** ## Residuals 40 573.6 14.3 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey4 < - Tu keyHSD (fit4, conf.level= 0.95 ) #Tukey multiple comparisons Tukey4 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = TS_Reduc ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 7.000000 1.90172125 12.098279 0.0029451 ## C - A 11.666667 6.56838792 16.764945 0.0000008 ## D - A 13.555556 8.45727681 18.653834 0.0000000 ## E - A 16.666667 11.56838792 21.764945 0.0000000 ## C - B 4.666667 - 0.43161208 9.764945 0.08677 40 ## D - B 6.555556 1.45727681 11.653834 0.0059861 ## E - B 9.666667 4.56838792 14.764945 0.0000297 ## D - C 1.888889 - 3.20938986 6.987168 0.8264666 ## E - C 5.000000 - 0.09827875 10.098279 0.0568934 ## E - D 3.111111 - 1.98716764 8.209390 0.4203284 #data su mmary TS_data4 < - data_summary (metadata, varname= "TS_Reduc" , groupnames= "Temp" ) TS_data4 ## Temp TS_Reduc sd ## 1 A 4.666667 2.236068 ## 2 B 11.666667 5.431390 ## 3 C 16.333333 2.397916 ## 4 D 18.222222 3.929942 ## 5 E 21.333333 4. 000000 #2. Plot TS4 < - data_summary (metadata, varname= "TS_Reduc" , groupnames= c ( "Temp" )) TS4 $ Temp = as.factor (TS4 $ Temp) TS4 ## Temp TS_Reduc sd ## 1 A 4.666667 2.236068 158 ## 2 B 11.666667 5.431390 ## 3 C 16.333333 2.397916 ## 4 D 18.222222 3.929942 ## 5 E 21.333333 4.000000 box_4 < - ggplot (TS4, aes ( x= Temp, y= TS_Reduc, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Reduc - sd, ymax= TS_Re duc + sd), width= 0.2 , posit ion= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Total Solids Reduction (%)" ) + ylim ( 0 , 30 ) + labs ( title = "" , su btitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_4 Figure A.6. Percent Average Reductions with Standard Deviations for Total Solids in BMP bottles, Average of Trials 1, 2 & 3 159 A.4 R - script and Results for Volatile Solids ## Statistical analysis for VS ## Feb 17, 2021 CREATED MIB # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, dependin g on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) sum mary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) 160 return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DE FINING FACTORS ### Abbreviations ## A - 10 C, non - mixed ## B - 20 C, non - mixed ## C - 30 C, non - mixed ## D - 39 C, non - mixed ## E - 39 C, mixed metadata $ Temp < - factor (metadata $ Temp) ##Factor statement ## VS PRE ANALYSIS ------------------------------------------------------- --- ## one - way ANOVA fit1 < - aov (VS_Pre ~ Temp, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 1209945 302486 0.12 0.975 ## Residuals 40 100872205 2521805 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparisons Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = VS_Pre ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 372.777778 - 1765.291 2510.846 0.9870989 ## C - A - 67.777778 - 2205.846 2070.291 0.9999841 ## D - A - 52.333333 - 2190.402 2085.735 0.9999943 ## E - A - 8.444444 - 2146.513 2129.624 1.0000000 ## C - B - 440.555556 - 2578.624 1697.513 0.9760345 ## D - B - 425.111111 - 2563.179 1712.957 0.9789755 161 ## E - B - 381.222222 - 2519.291 1756.846 0.9859695 ## D - C 15.444444 - 2122.624 2153.513 1.0000000 ## E - C 59.333333 - 2078.735 2197.402 0.9999906 ## E - D 43.888889 - 2094.179 2181.957 0.9999972 # data summary VS_data1 < - data_summary (metadata, varname= "VS_Pre" , groupnames= "Temp" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutat e VS_data1 ## Temp VS_Pre sd ## 1 A 9498.000 1677.644 ## 2 B 9870.778 1752.217 ## 3 C 9430.222 1490.467 ## 4 D 9445.667 1393.249 ## 5 E 9489.556 1600.511 #2. Plot VS1 < - data_summary (metadata, varname= "VS_Pre" , groupnames= c ( "Temp" )) VS1 $ Temp = as.factor (VS1 $ Temp) VS1 ## Temp VS_Pre sd ## 1 A 9498.000 1677.644 ## 2 B 9870.778 1752.217 ## 3 C 9430.222 1490.467 ## 4 D 9445.667 1393.249 ## 5 E 9489.556 1600.511 box_1 < - ggplot (VS1, aes ( x= Temp, y= VS_Pre, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= VS_Pre - sd, ymax= VS_Pre + sd), width= 0.2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Volatile Solids Pre - digestion (mg/L)" ) + ylim ( 0 , 15000 ) + labs ( t itle = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , f amily= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), 162 axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" , "blue" , "blue" )) box_1 Figure A.7. Average Pre - digestion Content with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 ## VS POST ANALYSIS ------------------------------------------------------ ---- ## one - way ANOVA fit2 < - aov (VS_Post ~ Temp, data = metadata) summary (fit2) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 36639511 9159878 4.584 0.00385 ** ## Residuals 40 79931799 1998295 ## --- ## Signif. codes: 0 '***' 0 .001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey2 < - TukeyHSD (fit2, conf.level= 0.95 ) #Tukey multiple comparisons Tukey2 163 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = VS_Post ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A - 461.7778 - 2365.027 1441.4713 0.9568605 ## C - A - 1709.2222 - 3612.471 194.0268 0.0966009 ## D - A - 2022.5556 - 3925.805 - 119.3065 0.0324048 ## E - A - 2294.4444 - 4197.694 - 391.1954 0.0 112492 ## C - B - 1247.4444 - 3150.694 655.8046 0.3486025 ## D - B - 1560.7778 - 3464.027 342.4713 0.1529818 ## E - B - 1832.6667 - 3735.916 70.5824 0.0639846 ## D - C - 313.3333 - 2216.582 1589.9157 0.9896056 ## E - C - 585.2222 - 2488.471 1318.0268 0.9032272 ## E - D - 271.8889 - 2175.138 1631.3602 0.9939429 #data summary VS_data2 < - data_summary (metadata, varname= "VS_Post" , groupnames= "Temp" ) VS_data2 ## Temp VS_Post sd ## 1 A 8741.222 1842.0779 ## 2 B 8279.444 1806.8145 ## 3 C 7032.000 1160.8508 ## 4 D 6718.667 1046.0535 ## 5 E 6446.778 944.3742 #2. Plot VS2 < - data_summary (metadata, varname= "VS_Post" , groupnames= c ( "Temp" )) VS2 $ Temp = as.factor (VS2 $ Temp) VS2 ## Temp VS_Post sd ## 1 A 8741.222 1842.0779 ## 2 B 8279.444 1806.8145 ## 3 C 7032.000 1160.8508 ## 4 D 6718.667 1046.0535 ## 5 E 6446.778 944.3742 box_2 < - ggplot (VS2, aes ( x= Temp, y= VS_Post, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= VS_Post - sd, ymax= VS_Post + sd), width= 0.2 , positio n= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Volatile Solids Post - Digestion (mg/L)" ) + ylim ( 0 , 13000 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), 164 axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" , "blue" , "blue" )) box_2 Figure A.8. Average Post - digestion Content with Standard Deviations for Volatile Soli ds in BMP bottles, Average of Trials 1, 2 & 3 ## VS DESTROYED ANALYSIS ------------------------------------------------- --------- ## one - way ANOVA fit3 < - aov (VS_Destroyed ~ Temp, data = metadata) summary (fit3) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 30902222 7725556 34.42 1.85e - 12 *** 165 ## Residuals 40 8978493 224462 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey3 < - TukeyHSD (fit3, conf.level= 0.95 ) #Tukey multiple comparisons Tukey3 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = VS_Destroyed ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 834.3333 196.455437 1472.2112 0.0050104 ## C - A 1641.2222 1003.344326 2279.1001 0.0000001 ## D - A 1970.1111 1332.233215 2607.9890 0.0000000 ## E - A 2285.8889 1648.010993 2923.7668 0.0000000 ## C - B 806.8889 169.010993 1444.7668 0.0 070688 ## D - B 1135.7778 497.899882 1773.6557 0.0000848 ## E - B 1451.5556 813.677660 2089.4335 0.0000009 ## D - C 328.8889 - 308.989007 966.7668 0.5857252 ## E - C 644.6667 6.788771 1282.5446 0.0465181 ## E - D 315.7778 - 322.100118 953.6557 0.6225541 #dat a summary VS_data3 < - data_summary (metadata, varname= "VS_Destroyed" , groupnames= "Te mp" ) VS_data3 ## Temp VS_Destroyed sd ## 1 A 756.8889 231.9140 ## 2 B 1591.2222 410.8673 ## 3 C 2398.1111 400.9372 ## 4 D 2727.0000 426.7807 ## 5 E 3042.7778 746.2059 #2. Plot VS3 < - data_summary (metadata, varname= "VS_Destroyed" , groupnames= c ( "Temp" )) VS3 $ Temp = as.factor (VS3 $ Temp) VS3 ## Temp VS_Destroyed sd ## 1 A 756.8889 231.9140 ## 2 B 1591.2222 410.8673 ## 3 C 2398.1111 400.9372 ## 4 D 2727.0000 426.7807 ## 5 E 3042.7778 746.2059 166 box_3 < - ggplot (VS3, aes ( x= Temp, y= VS_Destroyed, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom _errorbar ( aes ( ymin= VS_Destroyed - sd, ymax= VS_Destroyed + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Volatile Solids Destroyed (mg/L)" ) + ylim ( 0 , 4300 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_3 ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x $label), x$x, x$y , : font ## family not found in Windows font database Figure A.9. Average Reductions with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 167 ## VS REDUCTION ANALYSIS ---------------------------------------- --------- --------- ## one - way ANOVA fit4 < - aov (VS_Reduc ~ Temp, data = metadata) summary (fit4) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 3263 815.6 63.7 <2e - 16 *** ## Residuals 40 512 12.8 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey4 < - TukeyHSD (fit4, conf.level= 0.95 ) #Tukey multiple comparisons Tukey4 ## Tukey multiple comparisons of means ## 95% fa mily - wise confidence level ## ## Fit: aov(formula = VS_Reduc ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A 8.222222 3.404242 13.040203 0.0001648 ## C - A 16.777778 11.959797 21.595758 0.0000000 ## D - A 20.22222 2 15.404242 25.040203 0.0000000 ## E - A 23.333333 18.515353 28.151314 0.0000000 ## C - B 8.555556 3.737575 13.373536 0.0000885 ## D - B 12.000000 7.182019 16.817981 0.0000001 ## E - B 15.111111 10.293131 19.929092 0.0000000 ## D - C 3.444444 - 1.373536 8.262425 0.2653634 ## E - C 6.555556 1.737575 11.373536 0.0032615 ## E - D 3.111111 - 1.706869 7.929092 0.3634301 #data summary VS_data4 < - data_summary (metadata, varname= "VS_Reduc" , groupnames= "Temp" ) VS_data4 ## Temp VS_Reduc sd ## 1 A 8.555556 3.844188 ## 2 B 16.777778 5.426274 ## 3 C 25.333333 1.936492 ## 4 D 28.777778 2.223611 ## 5 E 31.888889 3.333333 #2. Plot VS4 < - data_summary (metadata, varname= "VS_Reduc" , groupnames= c ( "Temp" )) 168 VS4 $ Temp = as.factor ( VS4 $ Temp) VS4 ## Temp VS_Reduc sd ## 1 A 8.555556 3.844188 ## 2 B 16.777778 5.426274 ## 3 C 25.333333 1.936492 ## 4 D 28.777778 2.223611 ## 5 E 31.888889 3.333333 box_4 < - ggplot (VS4, aes ( x= Temp, y= VS_Reduc, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= VS_Reduc - sd, ymax= VS_Reduc + sd), width= 0.2 , posit ion= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( " Volatile Solids Reduction (%)" ) + ylim ( 0 , 40 ) + labs ( tit le = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "green" , "green" , "green" )) box_4 169 Figure A.10. Percent Average Reductions with Standard Deviations for Volatile Solids in BMP bottles, Average of Trials 1, 2 & 3 170 A.5 R - script and Results for Pre and Post Digestion pH ## Statistical analysis for pH ## Feb 17, 2021 CREATED MIB # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vec tor of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - dd ply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } 171 # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFINING FACTORS ### Abb reviations ## A - 10 C, non - mixed ## B - 20 C, non - mixed ## C - 30 C, non - mixed ## D - 39 C, non - mixed ## E - 39 C, mixed metadata $ Temp < - factor (metadata $ Temp) ##Factor statement ## PRE ANALYSIS ---------------------------------------------------------- ## one - way ANOVA fit1 < - aov (Pre ~ Temp, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 0.0216 0.005397 0.4 0.808 ## Residuals 40 0.5403 0.013508 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparisons Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Pre ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A - 0.04555556 - 0.202038 4 0.11092728 0.9192355 ## C - A - 0.02333333 - 0.1798162 0.13314950 0.9928639 ## D - A - 0.03333333 - 0.1898162 0.12314950 0.9729471 ## E - A - 0.06555556 - 0.2220384 0.09092728 0.7533016 ## C - B 0.02222222 - 0.1342606 0.17870506 0.9940790 ## D - B 0.01222222 - 0.1442606 0.16870506 0.9994257 ## E - B - 0.02000000 - 0.1764828 0.13648284 0.9960534 ## D - C - 0.01000000 - 0.1664828 0.14648284 0.9997405 172 ## E - C - 0.04222222 - 0.1987051 0.11426061 0.9375546 ## E - D - 0.03222222 - 0.1887051 0.12426061 0.9760930 # data summary pH_data1 < - dat a_summary (metadata, varname= "Pre" , groupnames= "Temp" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate pH_data1 ## Temp Pre sd ## 1 A 7.615556 0.09234597 ## 2 B 7.570000 0.08544004 ## 3 C 7.592222 0.10929064 ## 4 D 7.582222 0.13169831 ## 5 E 7.550000 0.14974979 #2. Plot pH1 < - data_summary (metadata, varname= "Pre" , groupnames= c ( "Temp" )) pH1 $ Temp = as.factor (pH1 $ Temp) pH1 ## Temp Pre sd ## 1 A 7.615556 0.09234597 ## 2 B 7.570000 0.08544004 ## 3 C 7.592222 0.10929064 ## 4 D 7.582222 0.13169831 ## 5 E 7.550000 0.14974979 box_1 < - ggplot (pH1, aes ( x= Temp, y= Pre, fill= Temp)) + geom _bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Pre - sd, ymax= Pre + sd), width= 0.2 , position= positi on_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "pH Pre - digestion" ) + ylim ( 0 , 8 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + 173 scale_fill_manual ( values= c ( "blue" , "blue" , "blue" , "blue" , "blue" )) box_1 Figure A.11. Average Pre - digestion pH with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ## POST ANALYSIS --------------------------------------------------------- - ## one - way ANOVA fit2 < - aov (Post ~ Temp, data = metadata) summary (fit2) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 0.2496 0.06241 1.841 0.14 ## Residuals 40 1.3560 0.03390 Tukey2 < - TukeyHSD (fit2, conf.level= 0.95 ) #Tukey multiple comparisons Tukey2 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Post ~ Temp, data = metadata) 174 ## ## $Temp ## diff lwr upr p adj ## B - A - 0.035555556 - 0.28344936 0.2123383 0.9938489 ## C - A 0.118888889 - 0.12900492 0.3667827 0.6 500231 ## D - A 0.154444444 - 0.09344936 0.4023383 0.3993672 ## E - A 0.116666667 - 0.13122714 0.3645605 0.6658176 ## C - B 0.154444444 - 0.09344936 0.4023383 0.3993672 ## D - B 0.190000000 - 0.05789381 0.4378938 0.2047049 ## E - B 0.152222222 - 0.09567158 0.4001160 0.4139990 ## D - C 0.035555556 - 0.21233825 0.2834494 0.9938489 ## E - C - 0.002222222 - 0.25011603 0.2456716 0.9999999 ## E - D - 0.037777778 - 0.28567158 0.2101160 0.9922458 # data summary pH_data2 < - data_summary (metadata, varname= "Post" , groupnames= "Temp" ) pH_data2 ## Temp Post sd ## 1 A 7.176667 0.1918333 ## 2 B 7.141111 0.1499537 ## 3 C 7.295556 0.1955832 ## 4 D 7.331111 0.1673652 ## 5 E 7.293333 0.2096426 #2. Plot pH2 < - data_summary (metadata, varname= "Post" , groupnames= c ( "Temp" )) pH2 $ Temp = as.factor (pH2 $ Temp) pH2 ## Temp Post sd ## 1 A 7.176667 0.1918333 ## 2 B 7.141111 0.1499537 ## 3 C 7.295556 0.1955832 ## 4 D 7.331111 0.1673652 ## 5 E 7.293333 0.2096426 box_2 < - ggplot (pH2, ae s ( x= Temp, y= Post, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Post - sd, ymax= Post + sd), width= 0.2 , position= posi tion_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "pH Post - digestion" ) + ylim ( 0 , 8 ) + labs ( title = "" , subtitle= NUL L ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), 175 axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue " , "blue" , "blue" )) box_2 Figure A.12. Average Post - digestion pH with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 ## POST ANALYSIS --------------------------------------------------------- - ## one - way ANOVA fit3 < - aov (Change ~ Temp, data = metadata) summary (fit3) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 4 0.3082 0.07706 6.069 0.000649 *** ## Resi duals 40 0.5079 0.01270 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey3 < - TukeyHSD (fit3, conf.level= 0.95 ) #Tukey multiple comparisons Tukey3 176 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Change ~ Temp, data = metadata) ## ## $Temp ## diff lwr upr p adj ## B - A - 0.010000000 - 0.1617088 0.141708770 0.9997066 ## C - A - 0.142222222 - 0.2939310 0.009486547 0.0754363 # # D - A - 0.187777778 - 0.3394865 - 0.036069008 0.0087587 ## E - A - 0.182222222 - 0.3339310 - 0.030513453 0.0116376 ## C - B - 0.132222222 - 0.2839310 0.019486547 0.1134493 ## D - B - 0.177777778 - 0.3294865 - 0.026069008 0.0145536 ## E - B - 0.172222222 - 0.3239310 - 0.020513453 0.0191507 ## D - C - 0.045555556 - 0.1972643 0.106153214 0.9104805 ## E - C - 0.040000000 - 0.1917088 0.111708770 0.9423217 ## E - D 0.005555556 - 0.1461532 0.157264325 0.9999717 # data summary pH_data3 < - data_summary (metadata, varname= "Change" , gro upnames= "Temp" ) pH_data3 ## Temp Change sd ## 1 A 0.4388889 0.11285438 ## 2 B 0.4288889 0.07896905 ## 3 C 0.2966667 0.16568042 ## 4 D 0.2511111 0.08146233 ## 5 E 0.2566667 0.10210289 #2. Plot pH3 < - data_summary (metadata, varnam e= "Change" , groupnames= c ( "Temp" )) pH3 $ Temp = as.factor (pH3 $ Temp) pH3 ## Temp Change sd ## 1 A 0.4388889 0.11285438 ## 2 B 0.4288889 0.07896905 ## 3 C 0.2966667 0.16568042 ## 4 D 0.2511111 0.08146233 ## 5 E 0. 2566667 0.10210289 box_3 < - ggplot (pH3, aes ( x= Temp, y= Change, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Change - sd, ymax= Change + sd), width= 0.2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "pH Change" ) + ylim ( 0 , 0.6 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), 177 axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "green" , "green" , "green" )) box_3 Figure A.13. Average pH Change with Standard Deviations in BMP bottles, Average of Trials 1, 2 & 3 178 Appendix B . R - Scripts and Results for Pilot Data B .1 R - script and Resu lts for Biogas Production ## Statistical analysis for Biogas ## Maria Bariosarosemena's data ## Feb 9, 2021 created ## Feb 9, 2021 update WL ## Feb 9, 2021 update MIB ## Feb 15, 2021 update MIB ## Mar 4, 2021 update MIB # Loading Library and Tables --------------------- ----------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may t ake a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION ------------- -------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summariezed # groupnames : vector of column names to be used as # grouping vari ables 179 data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited" . ##load biogas.txt con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFINING FACTORS metadata $ HRT < - factor (metadata $ HRT) ##Factor Statement metadata $ Temp < - factor (metadata $ Temp) ##Factor statement # 1. Effects of HRT and temp on Daily Biogas production ## two - way ANOVA # Daily biogas fit1 < - aov (Daily_Gas ~ Temp * HRT, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 61.66 30.831 77.236 < 2e - 16 *** ## HRT 2 0.26 0.128 0.321 0.726 ## Temp:HRT 4 16.73 4.183 10.478 4.8e - 08 *** ## Residuals 365 145.70 0.399 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fi t1, conf.level= 0.95 ) #Tukey multiple comparions Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## 180 ## Fit: aov(formula = Daily_Gas ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B 0.90138581 0.7129359 1.0898357 0.0000000 ## L - B 0.08754581 - 0.1009041 0.2759957 0.5188596 ## L - H - 0.81384000 - 1.0019111 - 0.6257689 0.0000000 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 0.06480571 - 0.12597 44 0.2555858 0.7036300 ## 3 - 1 0.03400822 - 0.1504107 0.2184271 0.9014415 ## 3 - 2 - 0.03079749 - 0.2215776 0.1599826 0.9235542 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 0.61048097 0.18770746 1.03325449 0.0003032 ## L:1 - B:1 - 0.54510042 - 0.96787394 - 0.12232691 0.0022511 ## B:2 - B:1 - 0.27120813 - 0.70782198 0.16540571 0.5875091 ## H:2 - B:1 0.68431818 0.24770434 1.12093203 0.0000526 ## L:2 - B:1 - 0.14620813 - 0.58282198 0.29040571 0.9811310 ## B:3 - B:1 - 0.56330087 - 0.98 861220 - 0.13798953 0.0014566 ## H:3 - B:1 0.59500000 0.17466324 1.01533676 0.0004458 ## L:3 - B:1 0.12954545 - 0.29079130 0.54988221 0.9889461 ## L:1 - H:1 - 1.15558140 - 1.58077770 - 0.73038509 0.0000000 ## B:2 - H:1 - 0.88168911 - 1.32064936 - 0.44272886 0.0000000 ## H:2 - H:1 0.07383721 - 0.36512304 0.51279746 0.9998539 ## L:2 - H:1 - 0.75668911 - 1.19564936 - 0.31772886 0.0000048 ## B:3 - H:1 - 1.17378184 - 1.60150159 - 0.74606209 0.0000000 ## H:3 - H:1 - 0.01548097 - 0.43825449 0.40729254 1.0000000 ## L:3 - H:1 - 0.48093552 - 0.9 0370903 - 0.05816200 0.0128267 ## B:2 - L:1 0.27389229 - 0.16506796 0.71285254 0.5814163 ## H:2 - L:1 1.22941860 0.79045835 1.66837886 0.0000000 ## L:2 - L:1 0.39889229 - 0.04006796 0.83785254 0.1086868 ## B:3 - L:1 - 0.01820044 - 0.44592019 0.40951930 1.000000 0 ## H:3 - L:1 1.14010042 0.71732691 1.56287394 0.0000000 ## L:3 - L:1 0.67464588 0.25187236 1.09741939 0.0000345 ## H:2 - B:2 0.95552632 0.50322077 1.40783186 0.0000000 ## L:2 - B:2 0.12500000 - 0.32730554 0.57730554 0.9946735 ## B:3 - B:2 - 0.29209273 - 0. 73349775 0.14931228 0.4990831 ## H:3 - B:2 0.86620813 0.42959429 1.30282198 0.0000001 ## L:3 - B:2 0.40075359 - 0.03586025 0.83736743 0.1010383 ## L:2 - H:2 - 0.83052632 - 1.28283186 - 0.37822077 0.0000008 ## B:3 - H:2 - 1.24761905 - 1.68902406 - 0.80621403 0.0000000 ## H:3 - H:2 - 0.08931818 - 0.52593203 0.34729566 0.9993719 ## L:3 - H:2 - 0.55477273 - 0.99138657 - 0.11815888 0.0028255 ## B:3 - L:2 - 0.41709273 - 0.85849775 0.02431228 0.0809046 ## H:3 - L:2 0.74120813 0.30459429 1.17782198 0.0000072 ## L:3 - L:2 0.27575359 - 0.16086025 0.71236743 0.5647862 ## H:3 - B:3 1.15830087 0.73298953 1.58361220 0.0000000 181 ## L:3 - B:3 0.69284632 0.26753499 1.11815765 0.0000209 ## L:3 - H:3 - 0.46545455 - 0.88579130 - 0.04511779 0.0176206 #Biogas data summary Daily_Gas_dat a1 < - data_summary (metadata, varname= "Daily_Gas" , groupnames = "HRT" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate Daily_Gas_data1 ## HRT Daily_Gas sd # # 1 1 1.152308 0.7904366 ## 2 2 1.219649 0.7634747 ## 3 3 1.193923 0.7755893 Daily_Gas_data2 < - data_summary (metadata, varname= "Daily_Gas" , groupnames = "Temp" ) Daily_Gas_data2 ## Temp Daily_Gas sd ## 1 B 0.8567742 0.6122377 ## 2 H 1. 7581600 0.8327552 ## 3 L 0.9443200 0.4966782 #2. Plot for Daily Biogas Production #Daily gas production based on HRT Daily_gas_production1 < - data_summary (metadata, varname= "Daily_Gas" , groupnames= c ( "HRT" )) Daily_gas_production1 $ HRT = as.factor (Daily_gas_production1 $ HRT) Daily_gas_production1 ## HRT Daily_Gas sd ## 1 1 1.152308 0.7904366 ## 2 2 1.219649 0.7634747 ## 3 3 1.193923 0.775 5893 box_1 < - ggplot (Daily_gas_production1, aes ( x= HRT, y= Daily_Gas, fill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Daily_Gas - sd, ymax= Daily_Gas + sd), width= 0.2 , pos ition= position_dodge ( 0.9 )) + 182 xlab ( "HRT" ) + ylab ( "Daily biogas production (L/day)" ) + ylim ( 0 , 2.5 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_1 Figure B.1. Average Daily Biogas Production based on HRTs , Average of 3 Conditions #Daily gas production based on Temp Daily_gas_production2 < - data_summary (metadata, varname= "Daily_Gas" , groupnames= c ( "Temp" )) Daily_gas_production2 $ Temp = as.factor (Daily_gas_production2 $ Temp) Daily_gas_production2 183 ## Temp Daily_Gas sd ## 1 B 0.8567742 0.6122377 ## 2 H 1.7581600 0.8327552 ## 3 L 0.9443200 0.4966782 box_2 < - ggplot (Daily_gas_production2, aes ( x= Temp, y= Daily_Gas, fill= Temp )) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Daily_Gas - sd, ymax= Daily_Gas + sd ), width= 0.2 , pos ition= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Daily biogas production (L/day)" ) + ylim ( 0 , 3 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 18 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "blue" )) box_2 Figure B.2. Average Daily Biogas Production based on Temperature Profile, Average of 3 184 Figure B.3 . Tukey Honest Significant Difference Results for the Daily Biogas Production based on HRT and Tempera ture ## Section 2 ------------------------------------------------------------- ------- # 3. Effects of HRT and temp on Biogas Production per kg Initial VS ## two - way ANOVA # Biogas per Kg Initial VS fit2 < - aov (Gas_kgVS ~ Temp * HRT, data = metadata) summary (fit2) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 7397233 3698616 88.756 < 2e - 16 *** ## HRT 2 53740 26870 0.645 0.525 ## Temp:HRT 4 2045847 511462 12.274 2.25e - 09 *** ## Residuals 365 15210220 41672 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey2 < - TukeyHSD (fit2, conf.level= 0.95 ) #Tukey multiple comparions Tukey2 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## 185 ## Fit: aov(formula = Gas_kgVS ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B 313.56819 252.67972 374.45667 0.0000000 # # L - B 33.56019 - 27.32828 94.44867 0.3977175 ## L - H - 280.00800 - 340.77409 - 219.24191 0.0000000 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 5.380373 - 56.26097 67.02171 0.9770097 ## 3 - 1 27.268243 - 32.31781 86.85429 0.5289960 ## 3 - 2 21.887871 - 39.75347 83.52921 0.6811269 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 204.405391 67.806598 341.004184 0.0001464 ## L:1 - B:1 - 182.501586 - 319.100379 - 45.902792 0.0012582 ## B:2 - B:1 - 103.642344 - 244.712970 37.428281 0.3493112 ## H:2 - B:1 215.647129 74.576504 356.717755 0.0000924 ## L:2 - B:1 - 71.458134 - 212.528759 69.612492 0.8149030 ## B:3 - B:1 - 191.003247 - 328.422013 - 53.584481 0.0006266 ## H:3 - B:1 230.795455 94.983981 366.606928 0.00 00070 ## L:3 - B:1 61.386364 - 74.425110 197.197837 0.8934187 ## L:1 - H:1 - 386.906977 - 524.288577 - 249.525376 0.0000000 ## B:2 - H:1 - 308.047736 - 449.876489 - 166.218982 0.0000000 ## H:2 - H:1 11.241738 - 130.587015 153.070491 0.9999996 ## L:2 - H:1 - 275.863525 - 417.692278 - 134.034772 0.0000001 ## B:3 - H:1 - 395.408638 - 533.605567 - 257.211709 0.0000000 ## H:3 - H:1 26.390063 - 110.208730 162.988857 0.9995882 ## L:3 - H:1 - 143.019027 - 279.617821 - 6.420234 0.0321991 ## B:2 - L:1 78.859241 - 62.969512 220.687995 0.7 244409 ## H:2 - L:1 398.148715 256.319961 539.977468 0.0000000 ## L:2 - L:1 111.043452 - 30.785302 252.872205 0.2639602 ## B:3 - L:1 - 8.501661 - 146.698590 129.695268 0.9999999 ## H:3 - L:1 413.297040 276.698247 549.895833 0.0000000 ## L:3 - L:1 243.88794 9 107.289156 380.486742 0.0000017 ## H:2 - B:2 319.289474 173.148835 465.430113 0.0000000 ## L:2 - B:2 32.184211 - 113.956428 178.324849 0.9989246 ## B:3 - B:2 - 87.360902 - 229.979562 55.257758 0.6062381 ## H:3 - B:2 334.437799 193.367174 475.508425 0. 0000000 ## L:3 - B:2 165.028708 23.958083 306.099334 0.0090409 ## L:2 - H:2 - 287.105263 - 433.245902 - 140.964624 0.0000001 ## B:3 - H:2 - 406.650376 - 549.269036 - 264.031716 0.0000000 ## H:3 - H:2 15.148325 - 125.922300 156.218951 0.9999954 ## L:3 - H:2 - 154.2607 66 - 295.331391 - 13.190140 0.0203076 ## B:3 - L:2 - 119.545113 - 262.163773 23.073547 0.1836643 ## H:3 - L:2 302.253589 161.182963 443.324214 0.0000000 ## L:3 - L:2 132.844498 - 8.226128 273.915123 0.0831233 ## H:3 - B:3 421.798701 284.379935 559.217467 0 .0000000 186 ## L:3 - B:3 252.389610 114.970844 389.808376 0.0000007 ## L:3 - H:3 - 169.409091 - 305.220565 - 33.597617 0.0037287 #Biogas data summary Gaskg_data1 < - data_summary (metadata, varname= "Gas_kgVS" , groupnames= "HRT " ) Gaskg_data1 ## HRT Gas_kgVS sd ## 1 1 394.6769 254.4945 ## 2 2 400.9474 246.6602 ## 3 3 424.6154 270.1238 Gaskg_data2 < - data_summary (metadata, varname= "Gas_kgVS" , groupnames= "Tem p" ) Gaskg_data2 ## Temp Gas_kgVS sd ## 1 B 290.9758 193.9921 ## 2 H 60 4.5440 276.3760 ## 3 L 324.5360 160.8791 #2. Plot for Biogas Production per kg Initial VS #Gas per VS production based on HRT Gaskg_production1 < - data_summary (metadata, varname= "Gas_kgVS" , groupnames= c ( "HRT" )) Gaskg_production1 $ HRT = as.factor (Gaskg_production1 $ HRT) Gaskg_production1 ## HRT Gas_kgVS sd ## 1 1 394.6769 254.4945 ## 2 2 400.9474 246.6602 ## 3 3 424.6154 270.1238 box_3 < - ggplot (Gaskg_production1, aes ( x= HRT, y= Gas_kgVS, fill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Gas_kgVS - sd, ymax= Gas_kgVS + sd), width= 0.2 , posit ion= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Daily biogas production per kg Initial VS (L/kg/day)" ) + ylim ( 0 , 800 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 12 , family= "Times New Roman" ), axis.title.y = element_text ( size = 15 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + 187 scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_3 Figure B.4 . Average Daily Biogas Production in L per kg Initial VS based on HRTs, Average of 3 Conditions #Gas per VS production based on Temp Gaskg_production2 < - data_summary (metadata, varname= "Gas_kgVS" , groupnames= c ( "Temp" )) Ga skg_production2 $ Temp = as.factor (Gaskg_production2 $ Temp) Gaskg_production2 ## Temp Gas_kgVS sd ## 1 B 290.9758 193.9921 ## 2 H 604.5440 276.3760 ## 3 L 324.5360 160.8791 box_4 < - ggplot (Gaskg_production2, aes ( x= Temp, y= Gas_kgVS, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Gas_kgVS - sd, ymax= Gas_kgVS + sd), width= 0.2 , posit ion= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + 188 ylab ( "Daily biogas production per kg Initial VS (L/kg/day)" ) + ylim ( 0 , 1000 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 12 , family= "Times New Roman" ), axis.title.y = element_text ( size = 15 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "blue" )) box_4 Figure B.5 . Average Daily Biogas Production in L per kg Initial VS based on Temperature 189 Figure B.6 . Tukey Honest Significant Difference Results for the Daily Biogas Production per kg Initial VS based on HRT and Temperature ##Section 3 -------------------------------------------------------------- ------ # 4. Effects of HRT and temp on Cumulative Biogas Production ## two - way ANOVA # Biogas per Kg Initial VS fit3 < - aov (Total_Gas ~ Temp * HRT, data = metadata) summary (fit3) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 65797 32898 248.34 <2e - 16 *** ## HRT 2 161029 805 14 607.77 <2e - 16 *** ## Temp:HRT 4 22659 5665 42.76 <2e - 16 *** ## Residuals 365 48353 132 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey3 < - TukeyHSD (fit3, conf.level= 0.95 ) #Tukey multiple comparions Tukey3 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Total_Gas ~ Temp * HRT, data = metadata) 190 ## ## $Temp ## diff lwr upr p adj ## H - B 24.912219 21.479175 28.345264 0.0000000 ## L - B - 5.560501 - 8.993545 - 2.127456 0.0004746 ## L - H - 30.472720 - 33.898864 - 27.046576 0.0000000 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 23.01358 19.53809 26.48908 0 ## 3 - 1 49.72653 46.36691 53.0861 4 0 ## 3 - 2 26.71294 23.23745 30.18843 0 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 5.863832 - 1.8379501 13.5656139 0.3004736 ## L:1 - B:1 - 3.961284 - 11.6630664 3.7404977 0.8017512 ## B:2 - B:1 20.703911 12.7499964 28.6578266 0.0000000 ## H:2 - B:1 42.211806 34.2578911 50.1657213 0.0000000 ## L:2 - B:1 8.161806 0.2078911 16.1157213 0.0392739 ## B:3 - B:1 35.969037 28.2210227 43.7170509 0.0000000 ## H:3 - B:1 81.222955 73.5655636 88.8803455 0.00000 00 ## L:3 - B:1 33.661364 26.0039727 41.3187546 0.0000000 ## L:1 - H:1 - 9.825116 - 17.5710349 - 2.0791976 0.0029014 ## B:2 - H:1 14.840080 6.8434193 22.8367398 0.0000005 ## H:2 - H:1 36.347974 28.3513140 44.3446346 0.0000000 ## L:2 - H:1 2.297974 - 5.698 6860 10.2946346 0.9930586 ## B:3 - H:1 30.105205 22.3133160 37.8970938 0.0000000 ## H:3 - H:1 75.359123 67.6573406 83.0609046 0.0000000 ## L:3 - H:1 27.797532 20.0957497 35.4993137 0.0000000 ## B:2 - L:1 24.665196 16.6685356 32.6618561 0.0000000 ## H: 2 - L:1 46.173091 38.1764303 54.1697508 0.0000000 ## L:2 - L:1 12.123091 4.1264303 20.1197508 0.0001107 ## B:3 - L:1 39.930321 32.1384322 47.7222101 0.0000000 ## H:3 - L:1 85.184239 77.4824569 92.8860209 0.0000000 ## L:3 - L:1 37.622648 29.9208660 45 .3244300 0.0000000 ## H:2 - B:2 21.507895 13.2681196 29.7476699 0.0000000 ## L:2 - B:2 - 12.542105 - 20.7818804 - 4.3023301 0.0001014 ## B:3 - B:2 15.265125 7.2239281 23.3063225 0.0000003 ## H:3 - B:2 60.519043 52.5651279 68.4729582 0.0000000 ## L:3 - B:2 12.957452 5.0035370 20.9113673 0.0000209 ## L:2 - H:2 - 34.050000 - 42.2897752 - 25.8102248 0.0000000 ## B:3 - H:2 - 6.242769 - 14.2839666 1.7984278 0.2748164 ## H:3 - H:2 39.011148 31.0572332 46.9650634 0.0000000 ## L:3 - H:2 - 8.550443 - 16.5043577 - 0.5965275 0.0244685 ## B:3 - L:2 27.807231 19.7660334 35.8484278 0.0000000 ## H:3 - L:2 73.061148 65.1072332 81.0150634 0.0000000 ## L:3 - L:2 25.499557 17.5456423 33.4534725 0.0000000 ## H:3 - B:3 45.253918 37.5059036 53.0019319 0.0000000 191 ## L:3 - B:3 - 2.307673 - 10.0556873 5.4403410 0.9911793 ## L:3 - H:3 - 47.561591 - 55.2189819 - 39.9041999 0.0000000 #Biogas data summary T_data1 < - data_summary (metadata, varname= "Total_Gas" , groupnames= "HRT" ) T_data1 ## HRT Total_Gas sd ## 1 1 12.76408 8.99 4012 ## 2 2 35.82728 16.773643 ## 3 3 62.63946 26.942374 T_data2 < - data_summary (metadata, varname= "Total_Gas" , groupnames= "Temp" ) T_data2 ## Temp Total_Gas sd ## 1 B 30.66258 17.66933 ## 2 H 55.57480 35.58018 ## 3 L 25.10208 17.1 9481 #2. Plot for Biogas Production per kg Initial VS #HRT T_production1 < - data_summary (metadata, varname= "Total_Gas" , groupnames= c ( "HRT" )) T_production1 $ HRT = as.factor (T_production1 $ HRT) T_production1 ## HRT Total_Gas sd ## 1 1 12.76408 8.994012 ## 2 2 35.82728 16.773643 ## 3 3 62.63946 26.942374 box_5 < - ggplot (T_production1, aes ( x= HRT, y= Total_Gas, fill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_er rorbar ( aes ( ymin= Total_Gas - sd, ymax= Total_Gas + sd), width= 0.2 , pos ition= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Cumulative Biogas Production (L)" ) + ylim ( 0 , 100 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 12 , family= "Times New Roman" ), axis.title.y = element_text ( size = 15 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "green" , "red" )) box_5 192 Figure B. 7 . Average Cumulative Biogas Production based on HRTs, Average of 3 Conditions #TEMP T_pr oduction2 < - data_summary (metadata, varname= "Total_Gas" , groupnames= c ( "Temp" )) T_production2 $ Temp = as.factor (T_production2 $ Temp) T_production2 ## Temp Total_Gas sd ## 1 B 30.66258 17.66933 ## 2 H 55.57480 35.58018 ## 3 L 25.10208 17.19481 box_6 < - ggplot (T_production2, aes ( x= Temp, y= Total_Gas, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Total_Gas - sd, ymax= Total_Gas + sd), width= 0. 2 , pos ition= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Cumulative Biogas Production (L)" ) + ylim ( 0 , 100 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), 193 axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 12 , family= "Times New Roman" ), axis.title.y = element_text ( size = 15 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "red" , "blue" )) box_6 Figure B. 8 . Average Cumulative Biogas Production based on Temperature Profile, B.2 R - script and Results for Methane Production ## Statistical analysis ## Maria Barrios data ## Feb 9, 2021 created # Loading Library and Tables -------------------------------------------- -- library (MASS) 194 library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. ## Importing fonts may take a few minutes, depending on the number of fon ts and th e speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summariezed # groupnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". ##load Methane.txt 195 con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFIN ING FACTORS metadata $ HRT < - factor (metadata $ HRT) ##Factor Statement metadata $ Temp < - factor (metadata $ Temp) ##Factor statement # 1. Effects of HRT and temp on Methane ## two - way ANOVA # Methane fit1 < - aov (Methane_percent ~ Temp * HRT, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 554.2 277.09 46.881 1.11e - 15 *** ## HRT 2 2.8 1.40 0.236 0.790 ## Temp:HRT 4 18.1 4.54 0.767 0.549 ## Residual s 117 691.5 5.91 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparions Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = Methane_percent ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B - 4.905000 - 6.164401 - 3.6455991 0.0000000 ## L - B - 1.130476 - 2.389877 0.1289247 0.0880238 ## L - H 3.774524 2.515123 5.0339247 0.0000000 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 - 0.2376190 - 1.4970200 1.021782 0.8954284 ## 3 - 1 0.1207143 - 1.1386866 1.380115 0.9718685 ## 3 - 2 0.3583333 - 0.9010676 1.617734 0.7782203 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 - 4.93214286 - 7.83643749 - 2.0278482 0.0000143 ## L:1 - B:1 - 1.98428571 - 4.88858034 0.9200089 0.4389207 ## B:2 - B:1 - 0.95857143 - 3.86286606 1.9457232 0.9806807 196 ## H:2 - B:1 - 5.726428 57 - 8.63072320 - 2.8221339 0.0000003 ## L:2 - B:1 - 0.94428571 - 3.84858034 1.9600089 0.9824231 ## B:3 - B:1 - 0.03928571 - 2.94358034 2.8650089 1.0000000 ## H:3 - B:1 - 5.05428571 - 7.95858034 - 2.1499911 0.0000079 ## L:3 - B:1 - 1.46071429 - 4.36500892 1.4435803 0.8085 541 ## L:1 - H:1 2.94785714 0.04356251 5.8521518 0.0437798 ## B:2 - H:1 3.97357143 1.06927680 6.8778661 0.0010496 ## H:2 - H:1 - 0.79428571 - 3.69858034 2.1100089 0.9943485 ## L:2 - H:1 3.98785714 1.08356251 6.8921518 0.0009895 ## B:3 - H:1 4.89285714 1.98 856251 7.7971518 0.0000172 ## H:3 - H:1 - 0.12214286 - 3.02643749 2.7821518 1.0000000 ## L:3 - H:1 3.47142857 0.56713394 6.3757232 0.0074530 ## B:2 - L:1 1.02571429 - 1.87858034 3.9300089 0.9706820 ## H:2 - L:1 - 3.74214286 - 6.64643749 - 0.8378482 0.0026658 ## L :2 - L:1 1.04000000 - 1.86429463 3.9442946 0.9681353 ## B:3 - L:1 1.94500000 - 0.95929463 4.8492946 0.4672270 ## H:3 - L:1 - 3.07000000 - 5.97429463 - 0.1657054 0.0297902 ## L:3 - L:1 0.52357143 - 2.38072320 3.4278661 0.9997137 ## H:2 - B:2 - 4.76785714 - 7.67215177 - 1.8635625 0.0000312 ## L:2 - B:2 0.01428571 - 2.89000892 2.9185803 1.0000000 ## B:3 - B:2 0.91928571 - 1.98500892 3.8235803 0.9851799 ## H:3 - B:2 - 4.09571429 - 7.00000892 - 1.1914197 0.0006302 ## L:3 - B:2 - 0.50214286 - 3.40643749 2.4021518 0.9997905 ## L:2 - H:2 4.78214286 1.87784823 7.6864375 0.0000292 ## B:3 - H:2 5.68714286 2.78284823 8.5914375 0.0000003 ## H:3 - H:2 0.67214286 - 2.23215177 3.5764375 0.9982298 ## L:3 - H:2 4.26571429 1.36141966 7.1700089 0.0003038 ## B:3 - L:2 0.90500000 - 1.99929463 3.809294 6 0.9865979 ## H:3 - L:2 - 4.11000000 - 7.01429463 - 1.2057054 0.0005932 ## L:3 - L:2 - 0.51642857 - 3.42072320 2.3878661 0.9997416 ## H:3 - B:3 - 5.01500000 - 7.91929463 - 2.1107054 0.0000096 ## L:3 - B:3 - 1.42142857 - 4.32572320 1.4828661 0.8307447 ## L:3 - H:3 3.593571 43 0.68927680 6.4978661 0.0047278 #Methane data summary Methane_data1 < - data_summary (metadata, varname= "Methane_percent" , groupn ames= "HRT" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate Methane_data1 ## HRT Methane_percent sd ## 1 1 60.53667 3.461578 197 ## 2 2 60.29905 3.070081 ## 3 3 60.65738 3.068768 Methane_data2 < - data_summary (metadata, varname= "Methane_percent" , gro upn ames= "Temp" ) Methane_data2 ## Temp Methane_percent sd ## 1 B 62.50952 2.578621 ## 2 H 57.60452 1.691031 ## 3 L 61.37905 2.804994 #2. Plot for Methane # based on HRT Methane_data_production1 < - data_summary (metadat a, varname= "Methane_perce nt" , groupnames= c ( "HRT" )) Methane_data_production1 $ HRT = as.factor (Methane_data_production1 $ HRT) Methane_data_production1 ## HRT Methane_percent sd ## 1 1 60.53667 3.461578 ## 2 2 60.29905 3.070081 ## 3 3 60.65738 3.068768 box_1 < - ggplot (Methane_data_production1, aes ( x= HRT, y= Methane_percent, f ill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ym in= Methane_percent - sd, ymax= Methane_percent + sd), wi dth= 0.2 , position= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Methane Concentration (%)" ) + ylim ( 0 , 70 ) + labs ( title = "" , sub title= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_ text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_1 198 Figure B.9 . Average Methane Content based on HRTs, Average of 3 Conditions # based on Temp Methane_data_production2 < - data_summary (metadata, varname= "Methane_perce nt" , groupnames= c ( "Temp" )) Methane_data_production2 $ Temp = as.factor (Methane_data_production2 $ Temp) Methane_data_production2 ## Temp Methane_percent sd ## 1 B 62.50952 2.578621 ## 2 H 57.60452 1.691031 ## 3 L 61.37905 2.804994 box_2 < - ggplot (Methane_data_production2, aes ( x= Temp, y= Methane_percent, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= Methane_percent - sd, ymax= Methane_percent + sd), wi dth= 0.2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Methane Concentration (%)" ) + ylim ( 0 , 70 ) + labs ( title = "" , sub titl e= NULL ) + 199 theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_2 Figure B.10 . Average Methane 200 Figure B.11 . Tukey Honest Significant Difference Results for the Methane Content based on HRT and Temperature B .3 R - script and Results for Hydrogen Sulfide ## Statistical analysis ## Feb 9, 2021 cr eated # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extr afont) ## Registering fonts with R font_import () #It may take a few minutes to import. 201 ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard deviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summariezed # gro upnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ require (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". ##load Methane.txt con < - file.choose ( new = FALSE ) metadata < - read.table (con, header = T, row.names = 1 ) ## DEFIN ING FACTORS metadata $ HRT < - factor (metadata $ HRT) ##Factor Statement metadata $ Temp < - factor (metadata $ Temp) ##Factor statement # 1. Effects of HRT and temp ## two - way ANOVA 202 fit1 < - aov (H2S_ppm ~ Temp * HRT, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 8090288 4045144 7.963 0.000608 *** ## HRT 2 81238768 40619384 79.964 < 2e - 16 *** ## Temp:HRT 4 3986279 996570 1.962 0.105881 ## Residuals 103 52321 088 507972 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparions Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidenc e level ## ## Fit: aov(formula = H2S_ppm ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B - 176.7617 - 571.3948 217.87141 0.5377878 ## L - B - 630.6642 - 1017.0003 - 244.32813 0.0005333 ## L - H - 453.9025 - 850. 9840 - 56.82103 0.0208535 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 552.2164 138.7032 965.7295 0.0055526 ## 3 - 1 2031.6423 1618.1291 2445.1555 0.0000000 ## 3 - 2 1479.4260 1109.5685 1849.2834 0.0000000 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 - 40.03416 - 1131.98110 1051.9128 1.0000000 ## L:1 - B:1 - 96.14873 - 1082.93694 890.6395 0.9999974 ## B:2 - B:1 739.10084 - 170.85494 1649.0566 0.2106568 ## H:2 - B:1 559.91442 - 350.04137 1469.8702 0.5813346 ## L:2 - B:1 225.44870 - 684.50709 1135.4045 0.9970591 ## B:3 - B:1 2587.11799 1677.16220 3497.0738 0.0000000 ## H:3 - B:1 2003.26656 1093.31077 2913.2223 0.0000000 ## L:3 - B:1 1372.35727 462.40149 2282.3131 0.0001975 ## L:1 - H:1 - 56 .11457 - 1169.09131 1056.8622 1.0000000 ## B:2 - H:1 779.13500 - 266.32460 1824.5946 0.3163721 ## H:2 - H:1 599.94857 - 445.51103 1645.4082 0.6699726 ## L:2 - H:1 265.48286 - 779.97675 1310.9425 0.9965097 ## B:3 - H:1 2627.15214 1581.69254 3672.6117 0.00000 00 ## H:3 - H:1 2043.30071 997.84111 3088.7603 0.0000004 ## L:3 - H:1 1412.39143 366.93182 2457.8510 0.0013414 ## B:2 - L:1 835.24957 - 99.83793 1770.3371 0.1191939 203 ## H:2 - L:1 656.06314 - 279.02435 1591.1506 0.3988493 ## L:2 - L:1 321.59743 - 613.4900 7 1256.6849 0.9745064 ## B:3 - L:1 2683.26671 1748.17922 3618.3542 0.0000000 ## H:3 - L:1 2099.41529 1164.32779 3034.5028 0.0000000 ## L:3 - L:1 1468.50600 533.41850 2403.5935 0.0000888 ## H:2 - B:2 - 179.18643 - 1032.80062 674.4278 0.9990964 ## L:2 - B:2 - 5 13.65214 - 1367.26634 339.9620 0.6107062 ## B:3 - B:2 1848.01714 994.40295 2701.6313 0.0000000 ## H:3 - B:2 1264.16571 410.55152 2117.7799 0.0002781 ## L:3 - B:2 633.25643 - 220.35776 1486.8706 0.3224487 ## L:2 - H:2 - 334.46571 - 1188.07991 519.1485 0.945 1051 ## B:3 - H:2 2027.20357 1173.58938 2880.8178 0.0000000 ## H:3 - H:2 1443.35214 589.73795 2296.9663 0.0000179 ## L:3 - H:2 812.44286 - 41.17134 1666.0570 0.0752011 ## B:3 - L:2 2361.66929 1508.05509 3215.2835 0.0000000 ## H:3 - L:2 1777.81786 924.20 366 2631.4320 0.0000001 ## L:3 - L:2 1146.90857 293.29438 2000.5228 0.0014620 ## H:3 - B:3 - 583.85143 - 1437.46562 269.7628 0.4346007 ## L:3 - B:3 - 1214.76071 - 2068.37491 - 361.1465 0.0005678 ## L:3 - H:3 - 630.90929 - 1484.52348 222.7049 0.3274055 ## data summa ry H2S_data1 < - data_summary (metadata, varname= "H2S_ppm" , groupnames= "HRT" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate H2S_data1 ## HRT H2S_ppm sd ## 1 1 209.5654 214.3300 ## 2 2 762.0674 577.8486 ## 3 3 2241.4933 1100.7416 H2S_data2 < - data_summary (metadata, varname= "H2S_ppm" , groupnames= "Temp" ) H2S_data2 ## Temp H2S_ppm sd ## 1 B 1447.9400 1388.3155 ## 2 H 1271.1783 10 44.1035 ## 3 L 817.2758 858.0148 #2. Plot for H2S # based on HRT 204 H2S_data_production1 < - data_summary (metadata, varname= "H2S_ppm" , groupnames= c ( "HRT" )) H2S_data_production1 $ HRT = as.factor (H2S_data_production1 $ HRT) H2S_data_production1 ## HRT H2S_ppm sd ## 1 1 209.5654 214.3300 ## 2 2 762.0674 577.8486 ## 3 3 2241.4933 1100.7416 box_1 < - ggplot (H2S_data_production1, aes ( x= HRT, y= H2S_ppm, fill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= H2S_ppm - sd, ymax= H2S_ppm + sd), width= 0.2 , positio n= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Hydrogen Sulfide Concentration (%)" ) + ylim ( - 100 , 3500 ) + labs ( t itle = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_ text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "green" , "blue" , "red" )) box_1 205 Figure B. 12. Average Hydrogen Sulfide Content based on HRTs, Average of 3 Conditions # based on Temp H2S_data_production2 < - data_summary (metadata, varname= "H2S_ppm" , groupnames= c ( "Temp" )) H2S_data_production2 $ Temp = as.factor (H2S_data_pro duction2 $ Temp) H2S_data_production2 ## Temp H2S_ppm sd ## 1 B 1447.9400 1388.3155 ## 2 H 1271.1783 1044.1035 ## 3 L 817.2758 858.0148 box_2 < - ggplot (H2S_data_production2, aes ( x= Temp, y= H2S_ppm, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= H2S_ppm - sd, ymax= H2S_ppm + sd), width= 0.2 , positio n= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Hydrogen Sulfide Concentration (%)" ) + ylim ( - 200 , 3000 ) + labs ( t itle = " " , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), 206 axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "green" , "blue" , "red" )) box_2 Figure B.13 . Average Hydrogen Sulfide Content based on Temperature Profile, Average of 207 Figure B.1 4 . Tukey Honest Significant Difference Results for the Hydrogen Sulfide Content based on HRT and Temperature B.4 R - script and Results for Total Solids and Volatil e Solids Reduction ## Statistical analysis for TSVs ## Maria Bariosarosemena's data ## Feb 9, 2021 created ## Feb 15, 2021 update # Loading Library and Tables -------------------------------------------- -- library (MASS) library (ggplot2) library (grid) library (gridExtra) library (ggpubr) # Installing the font package ------------------------------------------- -- library (extrafont) ## Registering fonts with R font_import () #It may take a few minutes to import. 208 ## Importing fonts may take a few minutes, depending on the number of fon ts and the speed of the system. ## Continue? [y/n] ## Exiting. loadfonts ( device= "win" ) # PROGRAM TO PLOT BAR CHART WITH STANDARD DEVIATION --------------------- -- # Function to calculate the mean and the standard d eviation # for each group # data : a data frame # varname : the name of a column containing the variable #to be summariezed # groupnames : vector of column names to be used as # grouping variables data_summary < - function (data, varname, groupnames){ req uire (plyr) summary_func < - function (x, col){ c ( mean = mean (x[[col]], na.rm= TRUE ), sd = sd (x[[col]], na.rm= TRUE )) } data_sum < - ddply (data, groupnames, .fun= summary_func, varname) data_sum < - rename (data_sum, c ( "mean" = varname)) return (data_sum) } # ANALYSIS --------------------------------------------------------------- ## the .txt file needs to be saved as the type of "Tab delimited". ##load TSVS.txt con < - file.choose ( new = FALSE ) metadata < - read.table (con, hea der = T, row.names = 1 ) ## DEFINING FACTORS metadata $ HRT < - factor (metadata $ HRT) ##Factor Statement metadata $ Temp < - factor (metadata $ Temp) ##Factor statement ## Section 1 ----------------------------------------------------------- # 1. Effects of HRT an d temp on TS Reduction 209 ## two - way ANOVA # TS Reduction fit1 < - aov (TS_Reduction ~ Temp * HRT, data = metadata) summary (fit1) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 6148 3074.0 52.276 < 2e - 16 *** ## HRT 2 191 95.5 1.624 0.201717 ## Temp:HRT 4 1179 294.7 5.012 0.000945 *** ## Residuals 111 6527 58.8 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey1 < - TukeyHSD (fit1, conf.level= 0.95 ) #Tukey multiple comparions Tukey1 ## Tukey multiple comparisons of means ## 95% family - wise confidence level ## ## Fit: aov(formula = TS_Reduction ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B 8.16225 4.088907 12.235593 1.74e - 05 ## L - B - 9.35700 - 13.430343 - 5.283657 9.00e - 07 ## L - H - 17.51925 - 21.592593 - 13.445907 0.00e+00 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 2.8255952 - 1.311899 6.963090 0 .2405365 ## 3 - 1 2.4564286 - 1.518748 6.431605 0.3102459 ## 3 - 2 - 0.3691667 - 4.506661 3.768328 0.9755427 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 10.8142857 1.6442165 19.9843549 0.0088791 ## L:1 - B:1 - 10.3900000 - 19.5600692 - 1.2199308 0.0142747 ## B:2 - B:1 6.0286905 - 3.5158201 15.5732011 0.5477822 ## H:2 - B:1 13.5320238 3.9875132 23.0765344 0.0005866 ## L:2 - B:1 - 10.6596429 - 20.2041535 - 1.1151322 0.0167803 ## B:3 - B:1 1.2528571 - 7.9172120 10. 4229263 0.9999645 ## H:3 - B:1 7.3278571 - 1.8422120 16.4979263 0.2301242 ## L:3 - B:1 - 0.7871429 - 9.9572120 8.3829263 0.9999990 ## L:1 - H:1 - 21.2042857 - 30.3743549 - 12.0342165 0.0000000 ## B:2 - H:1 - 4.7855952 - 14.3301059 4.7589154 0.8101924 ## H:2 - H:1 2.7177381 - 6.8267725 12.2622487 0.9925108 ## L:2 - H:1 - 21.4739286 - 31.0184392 - 11.9294180 0.0000000 ## B:3 - H:1 - 9.5614286 - 18.7314978 - 0.3913594 0.0340876 210 ## H:3 - H:1 - 3.4864286 - 12.6564978 5.6836406 0.9543015 ## L:3 - H:1 - 11.6014286 - 20.7714978 - 2 .4313594 0.0035083 ## B:2 - L:1 16.4186905 6.8741799 25.9632011 0.0000111 ## H:2 - L:1 23.9220238 14.3775132 33.4665344 0.0000000 ## L:2 - L:1 - 0.2696429 - 9.8141535 9.2748678 1.0000000 ## B:3 - L:1 11.6428571 2.4727880 20.8129263 0.0033355 ## H:3 - L: 1 17.7178571 8.5477880 26.8879263 0.0000005 ## L:3 - L:1 9.6028571 0.4327880 18.7729263 0.0326973 ## H:2 - B:2 7.5033333 - 2.4014734 17.4081401 0.2961640 ## L:2 - B:2 - 16.6883333 - 26.5931401 - 6.7835266 0.0000181 ## B:3 - B:2 - 4.7758333 - 14.3203440 4.7686773 0.8119114 ## H:3 - B:2 1.2991667 - 8.2453440 10.8436773 0.9999655 ## L:3 - B:2 - 6.8158333 - 16.3603440 2.7286773 0.3757622 ## L:2 - H:2 - 24.1916667 - 34.0964734 - 14.2868599 0.0000000 ## B:3 - H:2 - 12.2791667 - 21.8236773 - 2.7346560 0.0027588 ## H:3 - H :2 - 6.2041667 - 15.7486773 3.3403440 0.5080252 ## L:3 - H:2 - 14.3191667 - 23.8636773 - 4.7746560 0.0002092 ## B:3 - L:2 11.9125000 2.3679894 21.4570106 0.0042384 ## H:3 - L:2 17.9875000 8.4429894 27.5320106 0.0000011 ## L:3 - L:2 9.8725000 0.3279894 19.4170106 0.0367827 ## H:3 - B:3 6.0750000 - 3.0950692 15.2450692 0.4813677 ## L:3 - B:3 - 2.0400000 - 11.2100692 7.1300692 0.9986500 ## L:3 - H:3 - 8.1150000 - 17.2850692 1.0550692 0.1271923 #TS data summary TS_data1 < - data_summary (metadata, varname= "TS_Reduction" , groupnames= "HR T" ) ## Loading required package: plyr ## ## Attaching package: 'plyr' ## The following object is masked from 'package:ggpubr': ## ## mutate TS_data1 ## HRT TS_Reduction sd ## 1 1 55.17357 10.362400 ## 2 2 57.99917 12.715902 ## 3 3 57.63000 9.617261 TS_data2 < - data_summary (metadata, varname= "TS_Reduction" , groupnames= "Te mp" ) TS_data2 ## Temp TS_Reduction sd ## 1 B 57.27925 6.216955 ## 2 H 65.44150 5.291935 ## 3 L 47.92225 11.654731 211 #2. Plot for TS Reduction # based on HRT TS_Reduction1 < - data_summary (metadata, varname= "TS_Reduction" , groupnames= c ( "HRT" )) TS_Reduction1 $ HRT = as.factor (TS_Reduction1 $ HRT) TS_Reduction1 ## HRT TS_Reduction sd ## 1 1 55.17357 10.362400 ## 2 2 57.99917 12.715902 ## 3 3 57.63000 9.617261 box_1 < - ggplot (TS_Reduction1, aes ( x= HRT, y= TS_Reduction, fill= HRT)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), w idth= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Reduction - sd, ymax= TS_Reduction + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Total Solids Reduction (%)" ) + ylim ( 0 , 80 ) + labs ( title = "" , su btitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_1 212 Figure B.15 . Average Total Solid Reductions based on HRTs, Average of 3 Conditions # based on Temp TS_Reduction2 < - data_summary (metadata, varname= "TS_Reduction" , groupnames= c ( "Temp" )) TS_Reduction2 $ Temp = as.factor (TS_Reduction2 $ Temp) TS_Reduction2 ## Temp TS_Reduction sd ## 1 B 57.27925 6.216955 ## 2 H 65.44150 5.291935 ## 3 L 47.92225 11.654731 box_2 < - ggplot (TS_Reduction2, aes ( x= Temp, y= TS_Reduction, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= TS_Reduction - sd, ymax= TS_Reduction + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + ylab ( "Total Solids Reduction (%)" ) + ylim ( 0 , 80 ) + labs ( title = "" , su btitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), 213 axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axi s.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "red" , "blue" , "green" )) box_2 Figure B.16 . Average Total Solid Reductions based on Temperature Profile, Average of 3 214 Figure B.17 . Tukey Honest Significant Difference Results for the Total Solid Reductions based on HRT and Temperature ## Section 2 -------------------------------------------------------- # 1. Effects of HRT and temp on TS Reduction ## two - way ANOVA # VS Reduction fit2 < - aov (VS_Reduction ~ Temp * HRT, data = metadata) summary (fit2) ## Df Sum Sq Mean Sq F value Pr(>F) ## Temp 2 7041 3521 53.539 < 2e - 16 *** ## HRT 2 702 351 5.338 0.006118 ** ## Temp:HRT 4 1666 417 6.334 0.000125 *** ## Residuals 111 7299 66 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Tukey2 < - TukeyHSD (fit2, conf.level= 0.95 ) #Tukey multiple comparions Tukey2 ## Tukey multiple comparisons of means ## 95% family - wise confidence level 215 ## ## Fit: aov(formula = VS_Reduction ~ Temp * HRT, data = metadata) ## ## $Temp ## diff lwr upr p adj ## H - B 8.98550 4.678027 13.292973 7.8e - 06 ## L - B - 9.77225 - 14.079723 - 5.464777 1.2e - 06 ## L - H - 18.75775 - 23.065223 - 14.450277 0.0e+00 ## ## $HRT ## diff lwr upr p adj ## 2 - 1 4.066587 - 0.308724 8.441899 0.0742294 ## 3 - 1 5.614762 1.411099 9.818425 0.005 5004 ## 3 - 2 1.548175 - 2.827137 5.923486 0.6786750 ## ## $`Temp:HRT` ## diff lwr upr p adj ## H:1 - B:1 11.0457143 1.3485637 20.742865 0.0134320 ## L:1 - B:1 - 11.9735714 - 21.6707220 - 2.276421 0.0049071 ## B:2 - B:1 7.2595 238 - 2.8335905 17.352638 0.3658261 ## H:2 - B:1 15.2370238 5.1439095 25.330138 0.0001855 ## L:2 - B:1 - 11.2246429 - 21.3177572 - 1.131529 0.0175829 ## B:3 - B:1 2.7435714 - 6.9535792 12.440722 0.9928247 ## H:3 - B:1 10.5328571 0.8357065 20.230008 0.022 6367 ## L:3 - B:1 2.6400000 - 7.0571506 12.337151 0.9944636 ## L:1 - H:1 - 23.0192857 - 32.7164363 - 13.322135 0.0000000 ## B:2 - H:1 - 3.7861905 - 13.8793048 6.306924 0.9577200 ## H:2 - H:1 4.1913095 - 5.9018048 14.284424 0.9252056 ## L:2 - H:1 - 22.2703571 - 32. 3634715 - 12.177243 0.0000000 ## B:3 - H:1 - 8.3021429 - 17.9992935 1.395008 0.1568151 ## H:3 - H:1 - 0.5128571 - 10.2100077 9.184293 1.0000000 ## L:3 - H:1 - 8.4057143 - 18.1028649 1.291436 0.1452745 ## B:2 - L:1 19.2330952 9.1399809 29.326210 0.0000008 ## H:2 - L:1 27.2105952 17.1174809 37.303710 0.0000000 ## L:2 - L:1 0.7489286 - 9.3441858 10.842043 0.9999997 ## B:3 - L:1 14.7171429 5.0199923 24.414293 0.0001673 ## H:3 - L:1 22.5064286 12.8092780 32.203579 0.0000000 ## L:3 - L:1 14.6135714 4.9164208 24.310722 0.0001919 ## H:2 - B:2 7.9775000 - 2.4966198 18.451620 0.2891969 ## L:2 - B:2 - 18.4841667 - 28.9582864 - 8.010047 0.0000059 ## B:3 - B:2 - 4.5159524 - 14.6090667 5.577162 0.8895568 ## H:3 - B:2 3.2733333 - 6.8197810 13.366448 0.982542 3 ## L:3 - B:2 - 4.6195238 - 14.7126382 5.473591 0.8763223 ## L:2 - H:2 - 26.4616667 - 36.9357864 - 15.987547 0.0000000 ## B:3 - H:2 - 12.4934524 - 22.5865667 - 2.400338 0.0047452 ## H:3 - H:2 - 4.7041667 - 14.7972810 5.388948 0.8648428 ## L:3 - H:2 - 12.5970238 - 22.690 1382 - 2.503909 0.0042393 ## B:3 - L:2 13.9682143 3.8750999 24.061329 0.0008844 ## H:3 - L:2 21.7575000 11.6643856 31.850614 0.0000000 ## L:3 - L:2 13.8646429 3.7715285 23.957757 0.0010001 216 ## H:3 - B:3 7.7892857 - 1.9078649 17.486436 0.2241132 ## L:3 - B:3 - 0.1035714 - 9.8007220 9.593579 1.0000000 ## L:3 - H:3 - 7.8928571 - 17.5900077 1.804293 0.2091325 #TS data summary VS_data1 < - data_summary (metadata, varname= "VS_Reduction" , groupnames= "HR T" ) VS_data1 ## HRT VS_Reduction sd ## 1 1 58. 09786 11.76480 ## 2 2 62.16444 13.55856 ## 3 3 63.71262 9.74957 VS_data2 < - data_summary (metadata, varname= "VS_Reduction" , groupnames= "Te mp" ) VS_data2 ## Temp VS_Reduction sd ## 1 B 61.54525 6.389300 ## 2 H 70.53075 5.355522 ## 3 L 51.77300 13.355472 #2. Plot for TS Reduction # based on HRT VS_Reduction1 < - data_summary (metadata, varname= "VS_Reduction" , groupnames= c ( "HRT" )) VS_Reduction1 $ HRT = as.factor (VS_Reduction1 $ HR T) VS_Reduction1 ## HRT VS_Reduction sd ## 1 1 58.09786 11.76480 ## 2 2 62.16444 13.55856 ## 3 3 63.71262 9.74957 box_3 < - ggplot (VS_Reduction1, aes ( x= HRT, y= VS_Reduction, fill= HRT)) + geom_bar ( stat= "identity" , position= positi on_dodge ( 0.9 ), width= 0.5 ) + geom_errorbar ( aes ( ymin= VS_Reduction - sd, ymax= VS_Reduction + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "HRT" ) + ylab ( "Volatile Solids Reduction (%)" ) + ylim ( 0 , 80 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), 217 legend.position= "none" ) + scale_fill_manual ( values= c ( "blue" , "blue" , "blue" )) box_3 Figure B.18 . Average Volatile Solid Reductions based on HRTs, A verage of 3 Conditions # based on Temp VS_Reduction2 < - data_summary (metadata, varname= "VS_Reduction" , groupnames= c ( "Temp" )) VS_Reduction2 $ Temp = as.factor (VS_Reduction2 $ Temp) VS_Reduction2 ## Temp VS_Reduction sd ## 1 B 61.54525 6.389300 ## 2 H 70.53075 5.355522 ## 3 L 51.77300 13.355472 box_4 < - ggplot (VS_Reduction2, aes ( x= Temp, y= VS_Reduction, fill= Temp)) + geom_bar ( stat= "identity" , position= position_dodge ( 0.9 ), width= 0.5 ) + geom_error bar ( aes ( ymin= VS_Reduction - sd, ymax= VS_Reduction + sd), width= 0. 2 , position= position_dodge ( 0.9 )) + xlab ( "Temperature" ) + 218 ylab ( "Volatile Solids Reduction (%)" ) + ylim ( 0 , 80 ) + labs ( title = "" , subtitle= NULL ) + theme ( title= element_text ( size= 20 , family= "Times New Roman" ), axis.text.x = element_text ( size= 20 , family= "Times New Roman" ), axis.text.y= element_text ( size= 20 , family= "Times New Roman" ), axis.title.y = element_text ( size = 20 , family= "Times New Roman" ), axis.title.x= element_text ( size= 20 , family= "Times New Roman" ), legend.position= "none" ) + scale_fill_manual ( values= c ( "red" , "blue" , "green" )) box_4 Figure B.19 . Average Volatile Solid Reductions based on Temperature Profile, Average of 3 H 219 Figure B.21 . Tukey Honest Significant Difference Results for the Volatile Solid Reductions based on HRT and Temperature 220 Appendix C . Additional BMP data Appendix C give additional BMP data for individual triplicate samples. C.1 Raw Material Characterization Table C.1 . Raw Sample Characterization Round 1 Sample TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 41,843 25,327 41,333 25,014 4.1 2.5 61% Cellulose Microcrystalline 1,017,250 1,017,207 958,989 958,947 95.9 95.9 100% Cow Manure 33,060 22,152 32,978 22,097 3.3 2.2 67% Table C.2 . Raw Sample Characterization Trial 2 Sample TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5/20 54,008 38,253 53,803 38,108 5.4 3.8 71% Cellulose Microcrystalline 1,017,250 1,017,207 958,989 958,947 95.9 95.9 100% Cow Manure 49,080 31,885 48,905 31,771 4.9 3.2 65% Table C.3 . Raw Sample Characterization Trial 3 Sam p le TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 48,743 33,910 48,631 33,832 4.9 3.4 70% Cellulose Microcrystalline 1,017,250 1,017,207 958,989 958,947 95.9 95.9 100% Cow Manure 29,332 20,853 29,222 20,775 2.9 2.1 71% 221 C. 2 BMP Data for Trial 1 C.2.1 15 ° C, non - mixed Table C.4 . Trial 1 BMP Pre - digestion data for 15 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.83 6,377 4,475 6,243 4,381 0.6 0.4 70% Seed 1/29 ( 2 ) 7.86 6,180 4,353 5,984 4,214 0.6 0.4 70% Seed 1/29 ( 3 ) 7.81 6,187 4,260 6,109 4,205 0.6 0.4 69% Cellulose Microcrystalline (1) 7.89 9,288 7,088 9,054 6,910 0.9 0.7 76% Cellulose Microcrystalline (2) 7.83 9,048 6,865 8,924 6,772 0.9 0.7 76% Cellulose Microcrystalline (3) 7.88 8,975 6,875 8,690 6,657 0.9 0.7 77% Cow Manure (1) 7.63 10,550 7,232 10,386 7,120 1.0 0.7 69% Cow Manure (2) 7.65 10,735 7,428 10,649 7,368 1.1 0.7 69% Cow Manure (3) 7.61 10,758 7,485 10,471 7,286 1.0 0.7 70% Table C.5. Trial 1 BMP Post - digestion data for 15 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.63 6,330 4,110 6,291 4,085 0.6 0.4 65% Seed 1/29 ( 2 ) 7.57 5,980 3,938 5,903 3,887 0.6 0.4 66% Seed 1/29 ( 3 ) 7.58 6,143 3,963 6,123 3,950 0.6 0.4 65% Cellulose Microcrystalline (1) 6.48 8,392 6,070 8,330 6,024 0.8 0.6 72% Cellulose Microcrystalline (2) 6.43 8,732 6,200 8,623 6,123 0.9 0.6 71% Cellulose Microcrystalline (3) 6.40 8,887 6,340 8,747 6,239 0.9 0.6 71% Cow Manure (1) 7.29 10,253 6,540 10,135 6,464 1.0 0.6 64% Cow Manure (2) 7.31 10,218 6,443 10,031 6,324 1.0 0.6 63% Cow Manure (3) 7.27 9,780 6,252 9,718 6,213 1.0 0.6 64% 222 Table C.6. Trial 1 BMP Total Solids Reduction for 15 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 6,377 6,330 48 1% Seed 1/29 ( 2 ) 6,180 5,980 200 3% Seed 1/29 ( 3 ) 6,187 6,143 45 1% Cellulose Microcrystalline (1) 9,288 8,392 895 10% Cellulose Microcrystalline (2) 9,048 8,732 315 3% Cellulose Microcrystalline (3) 8,975 8,887 88 1% Cow Manure (1) 10,550 10,253 297 3% Cow Manure (2) 10,735 10,218 517 5% Cow Manure (3) 10,758 9,780 977 9% Table C.7. Trial 1 BMP Volatile Solids Reduction for 15 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 4,475 4,110 365 8% Seed 1/29 ( 2 ) 4,353 3,938 415 10% Seed 1/29 ( 3 ) 4,260 3,963 297 7% Cellulose Microcrystalline (1) 7,088 6,070 1,018 14% Cellulose Microcrystalline (2) 6,865 6,200 665 10% Cellulose Microcrystalline (3) 6,875 6,340 535 8% Cow Manure (1) 7,232 6,540 692 10% Cow Manure (2) 7,428 6,443 985 13% Cow Manure (3) 7,485 6,252 1,232 16% 223 C.2.2 20 ° C, non - mixed Table C.8. Trial 1 BMP Pre - digestion data for 20 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.75 7,585 5,088 7,544 5,060 0.8 0.5 67% Seed 1/29 ( 2 ) 7.75 7,805 5,298 7,653 5,195 0.8 0.5 68% Seed 1/29 ( 3 ) 7.72 7,560 5,085 7,424 4,994 0.7 0.5 67% Cellulose Microcrystalline (1) 7.87 10,308 7,560 10,034 7,359 1.0 0.7 73% Cellulose Microcrystalline (2) 7.75 10,823 8,270 10,651 8,139 1.1 0.8 76% Cellulose Microcrystalline (3) 7.72 11,175 8,532 10,977 8,382 1.1 0.8 76% Cow Manure (1) 7.50 11,448 7,705 11,438 7,698 1.1 0.8 67% Cow Manure (2) 7.57 11,255 7,668 10,976 7,479 1.1 0.7 68% Cow Manure (3) 7.55 11,793 7,870 11,589 7,734 1.2 0.8 67% Table C.9 . Trial 1 BMP Post - digestion data for 20 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.27 7,337 4,837 7,308 4,818 0.7 0.5 66% Seed 1/29 ( 2 ) 7.34 7,478 4,735 7,361 4,662 0.7 0.5 63% Seed 1/29 ( 3 ) 7.29 7,393 4,745 7,286 4,677 0.7 0.5 64% Cellulose Microcrystalline (1) 6.24 8,622 5,968 8,496 5,880 0.8 0.6 69% Cellulose Microcrystalline (2) 5.97 9,000 6,418 8,852 6,312 0.9 0.6 71% Cellulose Microcrystalline (3) 5.94 9,152 6,358 9,080 6,307 0.9 0.6 69% Cow Manure (1) 7.08 9,417 5,942 9,171 5,787 0.9 0.6 63% Cow Manure (2) 7.07 9,625 5,950 9,588 5,927 1.0 0.6 62% Cow Manure (3) 7.04 9,312 5,955 9,264 5,924 0.9 0.6 64% 224 Table C.10 . Trial 1 BMP Total Solids Reduction for 20 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 7,585 7,337 248 3% Seed 1/29 ( 2 ) 7,805 7,478 327 4% Seed 1/29 ( 3 ) 7,560 7,393 168 2% Cellulose Microcrystalline (1) 10,308 8,622 1,685 16% Cellulose Microcrystalline (2) 10,823 9,000 1,823 17% Cellulose Microcrystalline (3) 11,175 9,152 2,022 18% Cow Manure (1) 11,448 9,417 2,030 18% Cow Manure (2) 11,255 9,625 1,630 14% Cow Manure (3) 11,793 9,312 2,480 21% Table C.11 . Trial 1 BMP Volatile Solids Reduction for 20 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 5,088 4,837 250 5% Seed 1/29 ( 2 ) 5,298 4,735 562 11% Seed 1/29 ( 3 ) 5,085 4,745 340 7% Cellulose Microcrystalline (1) 7,560 5,968 1,593 21% Cellulose Microcrystalline (2) 8,270 6,418 1,852 22% Cellulose Microcrystalline (3) 8,532 6,358 2,175 25% Cow Manure (1) 7,705 5,942 1,763 23% Cow Manure (2) 7,668 5,950 1,718 22% Cow Manure (3) 7,870 5,955 1,915 24% 225 C.2.3 30 ° C, non - mixed Table C.12 . Trial 1 BMP Pre - digestion data for 30 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.43 7,663 5,285 7,603 5,244 0.8 0.5 69% Seed 1/29 ( 2 ) 7.67 7,485 5,002 7,275 4,863 0.7 0.5 67% Seed 1/29 ( 3 ) 7.69 7,652 5,467 7,437 5,314 0.7 0.5 71% Cellulose Microcrystalline (1) 7.71 9,607 7,172 9,368 6,994 0.9 0.7 75% Cellulose Microcrystalline (2) 7.59 9,572 7,140 9,497 7,084 0.9 0.7 75% Cellulose Microcrystalline (3) 7.60 9,570 7,127 9,356 6,968 0.9 0.7 74% Cow Manure (1) 7.56 11,190 7,485 10,990 7,352 1.1 0.7 67% Cow Manure (2) 7.58 11,280 7,475 11,095 7,352 1.1 0.7 66% Cow Manure (3) 7.64 11,115 7,410 10,886 7,257 1.1 0.7 67% Table C.13 . Trial 1 BMP Post - digestion data for 30 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.14 7,058 4,435 7,004 4,402 0.7 0.4 63% Seed 1/29 ( 2 ) 7.19 6,902 4,210 6,876 4,194 0.7 0.4 61% Seed 1/29 ( 3 ) 7.24 6,950 4,290 6,819 4,210 0.7 0.4 62% Cellulose Microcrystalline (1) 6.97 7,487 4,617 7,416 4,573 0.7 0.5 62% Cellulose Microcrystalline (2) 6.92 7,548 4,805 7,446 4,741 0.7 0.5 64% Cellulose Microcrystalline (3) 7.00 7,888 5,008 7,776 4,937 0.8 0.5 63% Cow Manure (1) 7.06 9,518 5,665 9,254 5,506 0.9 0.6 60% Cow Manure (2) 7.11 9,457 5,440 9,263 5,328 0.9 0.5 58% Cow Manure (3) 7.09 9,287 5,480 9,118 5,381 0.9 0.5 59% 226 Table C.14 . Trial 1 BMP Total Solids Reduction for 30 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 7,663 7,058 605 8% Seed 1/29 ( 2 ) 7,485 6,902 582 8% Seed 1/29 ( 3 ) 7,652 6,950 702 9% Cellulose Microcrystalline (1) 9,607 7,487 2,120 22% Cellulose Microcrystalline (2) 9,572 7,548 2,025 21% Cellulose Microcrystalline (3) 9,570 7,888 1,682 18% Cow Manure (1) 11,190 9,518 1,673 15% Cow Manure (2) 11,280 9,457 1,822 16% Cow Manure (3) 11,115 9,287 1,828 16% Table C.15 . Trial 1 BMP Volatile Solids Reduction for 30 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 5,285 4,435 850 16% Seed 1/29 ( 2 ) 5,002 4,210 792 16% Seed 1/29 ( 3 ) 5,467 4,290 1,178 22% Cellulose Microcrystalline (1) 7,172 4,617 2,555 36% Cellulose Microcrystalline (2) 7,140 4,805 2,335 33% Cellulose Microcrystalline (3) 7,127 5,008 2,120 30% Cow Manure (1) 7,485 5,665 1,820 24% Cow Manure (2) 7,475 5,440 2,035 27% Cow Manure (3) 7,410 5,480 1,930 26% 227 C.2.4 39 ° C, non - mixed Table C.16 . Trial 1 BMP Pre - digestion data for 39 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.71 7,460 5,000 7,378 4,945 0.7 0.5 67% Seed 1/29 ( 2 ) 7.70 8,030 5,392 7,835 5,262 0.8 0.5 67% Seed 1/29 ( 3 ) 7.71 7,335 4,982 7,281 4,946 0.7 0.5 68% Cellulose Microcrystalline (1) 7.71 10,620 8,175 10,448 8,042 1.0 0.8 77% Cellulose Microcrystalline (2) 7.68 10,545 7,945 10,410 7,842 1.0 0.8 75% Cellulose Microcrystalline (3) 7.63 10,365 7,533 10,219 7,427 1.0 0.7 73% Cow Manure (1) 7.48 11,493 7,645 11,175 7,433 1.1 0.7 67% Cow Manure (2) 7.51 11,000 7,500 10,742 7,324 1.1 0.7 68% Cow Manure (3) 7.50 11,793 7,805 11,533 7,633 1.2 0.8 66% Table C.17 . Trial 1 BMP Post - digestion data for 39 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.22 6,548 3,963 6,514 3,942 0.7 0.4 61% Seed 1/29 ( 2 ) 7.24 7,098 4,063 7,090 4,058 0.7 0.4 57% Seed 1/29 ( 3 ) 7.23 6,775 4,063 6,658 3,991 0.7 0.4 60% Cellulose Microcrystalline (1) 6.76 8,193 5,203 8,169 5,188 0.8 0.5 64% Cellulose Microcrystalline (2) 6.94 8,325 5,217 8,314 5,210 0.8 0.5 63% Cellulose Microcrystalline (3) 6.93 8,260 5,142 8,109 5,049 0.8 0.5 62% Cow Manure (1) 7.16 9,833 5,480 9,645 5,377 1.0 0.5 56% Cow Manure (2) 7.15 9,572 5,313 9,403 5,218 0.9 0.5 55% Cow Manure (3) 7.16 9,583 5,375 9,305 5,220 0.9 0.5 56% 228 Table C.18 . Trial 1 BMP Total Solids Reduction for 39 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 7,460 6,548 913 12% Seed 1/29 ( 2 ) 8,030 7,098 932 12% Seed 1/29 ( 3 ) 7,335 6,775 560 8% Cellulose Microcrystalline (1) 10,620 8,193 2,428 23% Cellulose Microcrystalline (2) 10,545 8,325 2,220 21% Cellulose Microcrystalline (3) 10,365 8,260 2,105 20% Cow Manure (1) 11,493 9,833 1,660 14% Cow Manure (2) 11,000 9,572 1,427 13% Cow Manure (3) 11,793 9,583 2,210 19% Table C.19 . Trial 1 BMP Volatile Solids Reduction for 39 ° C, non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 5,000 3,963 1,037 21% Seed 1/29 ( 2 ) 5,392 4,063 1,330 25% Seed 1/29 ( 3 ) 4,982 4,063 920 18% Cellulose Microcrystalline (1) 8,175 5,203 2,972 36% Cellulose Microcrystalline (2) 7,945 5,217 2,728 34% Cellulose Microcrystalline (3) 7,533 5,142 2,390 32% Cow Manure (1) 7,645 5,480 2,165 28% Cow Manure (2) 7,500 5,313 2,187 29% Cow Manure (3) 7,805 5,375 2,430 31% 229 C.2.5 39 ° C, mixed Table C.20 . Trial 1 BMP Pre - digestion data for 39 ° C, mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.68 7,508 5,178 7,303 5,038 0.7 0.5 69% Seed 1/29 ( 2 ) 7.62 7,615 5,080 7,432 4,959 0.7 0.5 67% Seed 1/29 ( 3 ) 7.61 7,747 5,110 7,646 5,043 0.8 0.5 66% Cellulose Microcrystalline (1) 7.57 10,143 7,682 9,938 7,527 1.0 0.8 76% Cellulose Microcrystalline (2) 7.62 9,898 7,495 9,783 7,408 1.0 0.7 76% Cellulose Microcrystalline (3) 7.58 9,558 7,143 9,442 7,056 0.9 0.7 75% Cow Manure (1) 7.40 11,128 7,510 10,965 7,401 1.1 0.7 67% Cow Manure (2) 7.40 11,330 7,495 11,108 7,349 1.1 0.7 66% Cow Manure (3) 7.48 11,080 7,340 10,973 7,269 1.1 0.7 66% Table C.21 . Trial 1 BMP Post - digestion data for 39 ° C , mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 1/29 (1) 7.17 6,838 3,950 6,789 3,922 0.7 0.4 58% Seed 1/29 ( 2 ) 7.17 6,675 4,017 6,437 3,875 0.6 0.4 60% Seed 1/29 ( 3 ) 7.12 6,750 4,085 6,724 4,069 0.7 0.4 61% Cellulose Microcrystalline (1) 6.50 8,300 5,515 8,259 5,488 0.8 0.5 66% Cellulose Microcrystalline (2) 6.25 8,328 5,590 8,304 5,574 0.8 0.6 67% Cellulose Microcrystalline (3) 6.62 8,552 5,662 8,368 5,540 0.8 0.6 66% Cow Manure (1) 7.07 8,835 5,090 8,668 4,995 0.9 0.5 58% Cow Manure (2) 7.06 9,210 5,240 9,039 5,144 0.9 0.5 57% Cow Manure (3) 7.04 9,235 5,358 8,981 5,211 0.9 0.5 58% 230 Table C.22 . Trial 1 BMP Total Solids Reduction for 39 ° C, mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 7,508 6,838 670 9% Seed 1/29 ( 2 ) 7,615 6,675 940 12% Seed 1/29 ( 3 ) 7,747 6,750 997 13% Cellulose Microcrystalline (1) 10,143 8,300 1,843 18% Cellulose Microcrystalline (2) 9,898 8,328 1,570 16% Cellulose Microcrystalline (3) 9,558 8,552 1,005 11% Cow Manure (1) 11,128 8,835 2,292 21% Cow Manure (2) 11,330 9,210 2,120 19% Cow Manure (3) 11,080 9,235 1,845 17% Table C.23 . Trial 1 BMP Volatile Solids Reduction for 39 ° C, mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 1/29 (1) 5,178 3,950 1,227 24% Seed 1/29 ( 2 ) 5,080 4,017 1,063 21% Seed 1/29 ( 3 ) 5,110 4,085 1,025 20% Cellulose Microcrystalline (1) 7,682 5,515 2,168 28% Cellulose Microcrystalline (2) 7,495 5,590 1,905 25% Cellulose Microcrystalline (3) 7,143 5,662 1,480 21% Cow Manure (1) 7,510 5,090 2,420 32% Cow Manure (2) 7,495 5,240 2,255 30% Cow Manure (3) 7,340 5,358 1,983 27% 231 C.3 BMP Data for Trial 2 C.3.1 15 ° C, non - mixed Table C.24 . Trial 2 BMP Pre - digestion data for 15 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.56 8,080 6,095 7,925 5,978 0.8 0.6 75% Seed 5/20 ( 2 ) 7.64 8,182 6,185 8,138 6,151 0.8 0.6 76% Seed 5/20 ( 3 ) 7.67 9,703 7,285 9,444 7,089 0.9 0.7 75% Cellulose Microcrystalline (1) 7.69 13,308 10,940 13,235 10,880 1.3 1.1 82% Cellulose Microcrystalline (2) 7.75 13,350 10,940 13,045 10,691 1.3 1.1 82% Cellulose Microcrystalline (3) 7.72 13,730 11,230 13,549 11,082 1.4 1.1 82% Cow Manure (1) 7.50 15,212 11,347 15,018 11,201 1.5 1.1 75% Cow Manure (2) 7.50 15,633 11,232 15,258 10,963 1.5 1.1 72% Cow Manure (3) 7.51 15,085 10,840 14,637 10,517 1.5 1.1 72% Table C.25 . Trial 2 BMP Post - digestion data for 15 ° C, non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.17 7,670 5,720 7,652 5,707 0.8 0.6 75% Seed 5/20 ( 2 ) 7.24 7,920 5,998 7,888 5,974 0.8 0.6 76% Seed 5/20 ( 3 ) 7.25 8,757 6,250 8,670 6,188 0.9 0.6 71% Cellulose Microcrystalline (1) 6.22 12,475 9,772 12,448 9,751 1.2 1.0 78% Cellulose Microcrystalline (2) 6.13 12,170 9,615 12,106 9,565 1.2 1.0 79% Cellulose Microcrystalline (3) 6.12 12,420 9,570 12,374 9,535 1.2 1.0 77% Cow Manure (1) 6.84 14,777 10,710 14,742 10,684 1.5 1.1 72% Cow Manure (2) 6.96 15,258 10,673 15,173 10,614 1.5 1.1 70% Cow Manure (3) 6.98 14,638 10,265 14,601 10,239 1.5 1.0 70% 232 Table C.26 . Trial 2 BMP Total Solids Reduction for 15 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 8,080 7,670 410 5% Seed 5/20 ( 2 ) 8,182 7,920 262 3% Seed 5/20 ( 3 ) 9,703 8,757 945 10% Cellulose Microcrystalline (1) 13,308 12,475 833 6% Cellulose Microcrystalline (2) 13,350 12,170 1,180 9% Cellulose Microcrystalline (3) 13,730 12,420 1,310 10% Cow Manure (1) 15,212 14,777 435 3% Cow Manure (2) 15,633 15,258 375 2% Cow Manure (3) 15,085 14,638 448 3% Table C.27 . Trial 2 BMP Volatile Solids Reduction for 15 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 6,095 5,720 375 6% Seed 5/20 ( 2 ) 6,185 5,998 187 3% Seed 5/20 ( 3 ) 7,285 6,250 1,035 14% Cellulose Microcrystalline (1) 10,940 9,772 1,168 11% Cellulose Microcrystalline (2) 10,940 9,615 1,325 12% Cellulose Microcrystalline (3) 11,230 9,570 1,660 15% Cow Manure (1) 11,347 10,710 638 6% Cow Manure (2) 11,232 10,673 560 5% Cow Manure (3) 10,840 10,265 575 5% 233 C.3.2 20 ° C, non - mixed Table C.28 . Trial 2 BMP Pre - digestion data for 20 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.69 10,385 7,925 10,045 7,665 1.0 0.8 76% Seed 5/20 ( 2 ) 7.73 10,625 7,780 10,554 7,728 1.1 0.8 73% Seed 5/20 ( 3 ) 7.73 9,722 7,162 9,499 6,997 0.9 0.7 74% Cellulose Microcrystalline (1) 7.69 13,060 10,735 12,917 10,617 1.3 1.1 82% Cellulose Microcrystalline (2) 7.69 13,717 11,375 13,405 11,117 1.3 1.1 83% Cellulose Microcrystalline (3) 7.68 13,308 11,035 12,983 10,765 1.3 1.1 83% Cow Manure (1) 7.46 16,008 11,645 15,644 11,381 1.6 1.1 73% Cow Manure (2) 7.48 15,933 11,668 15,588 11,415 1.6 1.1 73% Cow Manure (3) 7.55 16,598 11,983 15,988 11,543 1.6 1.2 72% Table C.29 . Trial 2 BMP Post - digestion data for 20 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.26 9,620 6,858 9,464 6,747 0.9 0.7 71% Seed 5/20 ( 2 ) 7.30 10,282 7,125 9,987 6,920 1.0 0.7 69% Seed 5/20 ( 3 ) 7.32 9,115 6,412 8,893 6,258 0.9 0.6 70% Cellulose Microcrystalline (1) 6.06 12,377 9,545 12,107 9,336 1.2 0.9 77% Cellulose Microcrystalline (2) 5.98 12,510 9,435 11,952 9,014 1.2 0.9 75% Cellulose Microcrystalline (3) 6.06 12,048 9,228 11,638 8,914 1.2 0.9 77% Cow Manure (1) 6.93 13,763 9,563 13,592 9,443 1.4 0.9 69% Cow Manure (2) 7.08 14,122 9,620 13,836 9,426 1.4 0.9 68% Cow Manure (3) 7.08 15,417 10,465 15,206 10,320 1.5 1.0 68% 234 Table C.30 . Trial 2 BMP Total Solids Reduction for 20 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 10,385 9,620 765 7% Seed 5/20 ( 2 ) 10,625 10,282 343 3% Seed 5/20 ( 3 ) 9,722 9,115 607 6% Cellulose Microcrystalline (1) 13,060 12,377 683 5% Cellulose Microcrystalline (2) 13,717 12,510 1,207 9% Cellulose Microcrystalline (3) 13,308 12,048 1,260 9% Cow Manure (1) 16,008 13,763 2,245 14% Cow Manure (2) 15,933 14,122 1,810 11% Cow Manure (3) 16,598 15,417 1,180 7% Table C.31 . Trial 2 BMP Volatile Solids Reduction for 20 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 7,925 6,858 1,068 13% Seed 5/20 ( 2 ) 7,780 7,125 655 8% Seed 5/20 ( 3 ) 7,162 6,412 750 10% Cellulose Microcrystalline (1) 10,735 9,545 1,190 11% Cellulose Microcrystalline (2) 11,375 9,435 1,940 17% Cellulose Microcrystalline (3) 11,035 9,228 1,807 16% Cow Manure (1) 11,645 9,563 2,082 18% Cow Manure (2) 11,668 9,620 2,047 18% Cow Manure (3) 11,983 10,465 1,518 13% 235 C.3.3 30 ° C, non - mixed Table C.32 . Trial 2 BMP Pre - digestion data for 30 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.75 9,960 7,465 9,905 7,424 1.0 0.7 75% Seed 5/20 ( 2 ) 7.78 9,637 7,230 9,507 7,132 1.0 0.7 75% Seed 5/20 ( 3 ) 7.77 9,333 7,020 9,248 6,956 0.9 0.7 75% Cellulose Microcrystalline (1) 7.76 12,425 10,240 12,045 9,926 1.2 1.0 82% Cellulose Microcrystalline (2) 7.79 12,610 10,418 12,251 10,122 1.2 1.0 83% Cellulose Microcrystalline (3) 7.71 13,380 11,065 12,841 10,620 1.3 1.1 83% Cow Manure (1) 7.49 14,575 10,578 14,476 10,506 1.4 1.1 73% Cow Manure (2) 7.47 14,853 10,553 14,744 10,476 1.5 1.0 71% Cow Manure (3) 7.45 15,098 10,690 14,909 10,556 1.5 1.1 71% Table C.33 . Trial 2 BMP Post - digestion data for 30 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.21 8,195 5,703 7,996 5,564 0.8 0.6 70% Seed 5/20 ( 2 ) 7.27 7,728 5,413 7,584 5,312 0.8 0.5 70% Seed 5/20 ( 3 ) 7.40 8,132 5,745 8,002 5,653 0.8 0.6 71% Cellulose Microcrystalline (1) 7.08 9,033 6,498 8,953 6,440 0.9 0.6 72% Cellulose Microcrystalline (2) 7.06 9,372 6,680 9,133 6,509 0.9 0.7 71% Cellulose Microcrystalline (3) 7.13 9,427 6,732 9,160 6,541 0.9 0.7 71% Cow Manure (1) 7.21 12,173 7,732 11,938 7,585 1.2 0.8 64% Cow Manure (2) 7.28 13,130 8,353 12,746 8,109 1.3 0.8 64% Cow Manure (3) 7.32 12,798 8,023 12,513 7,844 1.3 0.8 63% 236 Table C.34 . Trial 2 BMP Total Solids Reduction for 30 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 9,960 8,195 1,765 18% Seed 5/20 ( 2 ) 9,637 7,728 1,910 20% Seed 5/20 ( 3 ) 9,333 8,132 1,200 13% Cellulose Microcrystalline (1) 12,425 9,033 3,393 27% Cellulose Microcrystalline (2) 12,610 9,372 3,238 26% Cellulose Microcrystalline (3) 13,380 9,427 3,953 30% Cow Manure (1) 14,575 12,173 2,403 16% Cow Manure (2) 14,853 13,130 1,722 12% Cow Manure (3) 15,098 12,798 2,300 15% Table C.35 . Trial 2 BMP Volatile Solids Reduction for 30 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 7,465 5,703 1,762 24% Seed 5/20 ( 2 ) 7,230 5,413 1,818 25% Seed 5/20 ( 3 ) 7,020 5,745 1,275 18% Cellulose Microcrystalline (1) 10,240 6,498 3,743 37% Cellulose Microcrystalline (2) 10,418 6,680 3,737 36% Cellulose Microcrystalline (3) 11,065 6,732 4,333 39% Cow Manure (1) 10,578 7,732 2,845 27% Cow Manure (2) 10,553 8,353 2,200 21% Cow Manure (3) 10,690 8,023 2,667 25% 237 C.3.4 39 ° C, non - mixed Table C.36 . Trial 2 BMP Pre - digestion data for 39 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.67 9,293 7,108 9,004 6,887 0.9 0.7 76% Seed 5/20 ( 2 ) 7.70 9,393 6,848 9,069 6,611 0.9 0.7 73% Seed 5/20 ( 3 ) 7.69 9,848 7,318 9,621 7,149 1.0 0.7 74% Cellulose Microcrystalline (1) 7.70 12,420 10,078 12,104 9,821 1.2 1.0 81% Cellulose Microcrystalline (2) 7.70 12,888 10,518 12,618 10,298 1.3 1.0 82% Cellulose Microcrystalline (3) 7.68 12,273 9,935 11,869 9,608 1.2 1.0 81% Cow Manure (1) 7.51 14,505 10,368 14,457 10,333 1.4 1.0 71% Cow Manure (2) 7.49 15,075 10,833 14,606 10,495 1.5 1.0 72% Cow Manure (3) 7.48 15,040 10,905 14,813 10,740 1.5 1.1 73% Table C.37 . Trial 2 BMP Post - digestion data for 39 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.31 8,650 6,070 8,608 6,041 0.9 0.6 70% Seed 5/20 ( 2 ) 7.30 9,005 6,220 8,973 6,198 0.9 0.6 69% Seed 5/20 ( 3 ) 7.31 8,710 6,008 8,669 5,979 0.9 0.6 69% Cellulose Microcrystalline (1) 7.14 10,313 7,575 10,226 7,511 1.0 0.8 73% Cellulose Microcrystalline (2) 7.14 9,865 6,985 9,829 6,960 1.0 0.7 71% Cellulose Microcrystalline (3) 7.17 9,232 6,490 9,215 6,478 0.9 0.6 70% Cow Manure (1) 7.26 12,493 7,782 12,402 7,727 1.2 0.8 62% Cow Manure (2) 7.31 12,143 7,640 12,049 7,581 1.2 0.8 63% Cow Manure (3) 7.34 12,077 7,727 12,014 7,687 1.2 0.8 64% 238 Table C.38 . Trial 2 BMP Total Solids Reduction for 39 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 9,293 8,650 642 7% Seed 5/20 ( 2 ) 9,393 9,005 388 4% Seed 5/20 ( 3 ) 9,848 8,710 1,138 12% Cellulose Microcrystalline (1) 12,420 10,313 2,107 17% Cellulose Microcrystalline (2) 12,888 9,865 3,022 23% Cellulose Microcrystalline (3) 12,273 9,232 3,040 25% Cow Manure (1) 14,505 12,493 2,013 14% Cow Manure (2) 15,075 12,143 2,933 19% Cow Manure (3) 15,040 12,077 2,963 20% Table C.39 . Trial 2 BMP Volatile Solids Reduction for 39 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 7,108 6,070 1,037 15% Seed 5/20 ( 2 ) 6,848 6,220 628 9% Seed 5/20 ( 3 ) 7,318 6,008 1,310 18% Cellulose Microcrystalline (1) 10,078 7,575 2,503 25% Cellulose Microcrystalline (2) 10,518 6,985 3,533 34% Cellulose Microcrystalline (3) 9,935 6,490 3,445 35% Cow Manure (1) 10,368 7,782 2,585 25% Cow Manure (2) 10,833 7,640 3,193 29% Cow Manure (3) 10,905 7,727 3,178 29% 239 C.3.5 39 ° C, mixed Table C.40 . Trial 2 BMP Pre - digestion data for 39 °C , mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.68 8,993 6,835 8,838 6,717 0.9 0.7 76% Seed 5/20 ( 2 ) 7.67 9,365 7,145 9,249 7,057 0.9 0.7 76% Seed 5/20 ( 3 ) 7.66 9,077 6,915 8,811 6,712 0.9 0.7 76% Cellulose Microcrystalline (1) 7.68 12,600 10,295 12,311 10,059 1.2 1.0 82% Cellulose Microcrystalline (2) 7.69 12,467 10,230 12,222 10,028 1.2 1.0 82% Cellulose Microcrystalline (3) 7.68 13,525 11,155 13,294 10,965 1.3 1.1 82% Cow Manure (1) 7.51 14,158 10,330 13,815 10,079 1.4 1.0 73% Cow Manure (2) 7.46 15,905 11,418 15,623 11,215 1.6 1.1 72% Cow Manure (3) 7.49 15,295 11,070 14,930 10,806 1.5 1.1 72% Table C.41 . Trial 2 BMP Post - digestion data for 39 °C , mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 5 /20 (1) 7.32 8,345 5,718 8,156 5,588 0.8 0.6 69% Seed 5/20 ( 2 ) 7.32 7,545 5,200 7,532 5,191 0.8 0.5 69% Seed 5/20 ( 3 ) 7.31 8,307 5,677 8,075 5,519 0.8 0.6 68% Cellulose Microcrystalline (1) 7.15 9,075 6,553 8,981 6,484 0.9 0.6 72% Cellulose Microcrystalline (2) 7.18 9,170 6,598 9,097 6,545 0.9 0.7 72% Cellulose Microcrystalline (3) 7.08 9,680 7,060 9,385 6,845 0.9 0.7 73% Cow Manure (1) 7.25 12,228 7,477 11,676 7,140 1.2 0.7 61% Cow Manure (2) 7.30 11,795 7,330 11,559 7,182 1.2 0.7 62% Cow Manure (3) 7.31 11,453 6,997 11,236 6,865 1.1 0.7 61% 240 Table C.42 . Trial 2 BMP Total Solids Reduction for 39 °C , mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 8,993 8,345 648 7% Seed 5/20 ( 2 ) 9,365 7,545 1,820 19% Seed 5/20 ( 3 ) 9,077 8,307 770 8% Cellulose Microcrystalline (1) 12,600 9,075 3,525 28% Cellulose Microcrystalline (2) 12,467 9,170 3,297 26% Cellulose Microcrystalline (3) 13,525 9,680 3,845 28% Cow Manure (1) 14,158 12,228 1,930 14% Cow Manure (2) 15,905 11,795 4,110 26% Cow Manure (3) 15,295 11,453 3,843 25% Table C.43 . Trial 2 BMP Volatile Solids Reduction for 39 °C , mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 5 /20 (1) 6,835 5,718 1,117 16% Seed 5/20 ( 2 ) 7,145 5,200 1,945 27% Seed 5/20 ( 3 ) 6,915 5,677 1,238 18% Cellulose Microcrystalline (1) 10,295 6,553 3,743 36% Cellulose Microcrystalline (2) 10,230 6,598 3,632 36% Cellulose Microcrystalline (3) 11,155 7,060 4,095 37% Cow Manure (1) 10,330 7,477 2,853 28% Cow Manure (2) 11,418 7,330 4,088 36% Cow Manure (3) 11,070 6,997 4,073 37% 241 C.4 BMP Data for Trial 3 C.4.1 15 ° C, non - mixed Table C.44 . Trial 3 BMP Pre - digestion data for 15 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 8.17 8,987 6,845 8,858 6,746 0.9 0.7 76% Seed 6/15 ( 2 ) 8.08 9,143 6,885 8,869 6,679 0.9 0.7 75% Seed 6/15 ( 3 ) 8.04 8,772 6,472 8,611 6,355 0.9 0.6 74% Cellulose Microcrystalline (1) 7.97 12,420 10,240 12,368 10,197 1.2 1.0 82% Cellulose Microcrystalline (2) 7.97 12,975 10,578 12,768 10,409 1.3 1.0 82% Cellulose Microcrystalline (3) 7.98 12,698 10,285 12,332 9,989 1.2 1.0 81% Cow Manure (1) 7.72 13,943 10,170 13,550 9,885 1.4 1.0 73% Cow Manure (2) 7.72 13,380 9,670 13,123 9,483 1.3 0.9 72% Cow Manure (3) 7.70 14,000 10,078 13,956 10,046 1.4 1.0 72% Table C.45 . Trial 3 BMP Post - digestion data for 15 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.80 8,570 6,495 8,394 6,361 0.8 0.6 76% Seed 6/15 ( 2 ) 7.77 8,900 6,540 8,721 6,409 0.9 0.6 73% Seed 6/15 ( 3 ) 7.70 8,408 6,313 8,307 6,237 0.8 0.6 75% Cellulose Microcrystalline (1) 6.71 11,815 9,243 11,741 9,185 1.2 0.9 78% Cellulose Microcrystalline (2) 6.68 11,542 9,015 11,235 8,775 1.1 0.9 78% Cellulose Microcrystalline (3) 6.70 11,243 8,782 11,085 8,659 1.1 0.9 78% Cow Manure (1) 7.31 13,235 9,485 12,763 9,147 1.3 0.9 72% Cow Manure (2) 7.31 12,733 9,113 12,402 8,876 1.2 0.9 72% Cow Manure (3) 7.32 12,968 9,190 12,830 9,093 1.3 0.9 71% 242 Table C.46 . Trial 3 BMP Total Solids Reduction for 15 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 8,987 8,570 417 5% Seed 6/15 ( 2 ) 9,143 8,900 242 3% Seed 6/15 ( 3 ) 8,772 8,408 365 4% Cellulose Microcrystalline (1) 12,420 11,815 605 5% Cellulose Microcrystalline (2) 12,975 11,542 1,433 11% Cellulose Microcrystalline (3) 12,698 11,243 1,455 11% Cow Manure (1) 13,943 13,235 707 5% Cow Manure (2) 13,380 12,733 647 5% Cow Manure (3) 14,000 12,968 1,033 7% Table C.47 . Trial 3 BMP Volatile Solids Reduction for 15 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 6,845 6,495 350 5% Seed 6/15 ( 2 ) 6,885 6,540 345 5% Seed 6/15 ( 3 ) 6,472 6,313 160 2% Cellulose Microcrystalline (1) 10,240 9,243 997 10% Cellulose Microcrystalline (2) 10,578 9,015 1,562 15% Cellulose Microcrystalline (3) 10,285 8,782 1,503 15% Cow Manure (1) 10,170 9,485 685 7% Cow Manure (2) 9,670 9,113 557 6% Cow Manure (3) 10,078 9,190 888 9% 243 C.4.2 20 ° C, non - mixed Table C.48 . Trial 3 BMP Pre - digestion data for 20 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 8.05 9,333 7,175 9,277 7,132 0.9 0.7 77% Seed 6/15 ( 2 ) 7.96 9,108 7,008 8,895 6,843 0.9 0.7 77% Seed 6/15 ( 3 ) 8.00 9,347 7,065 9,315 7,040 0.9 0.7 76% Cellulose Microcrystalline (1) 7.97 12,345 10,160 11,914 9,805 1.2 1.0 82% Cellulose Microcrystalline (2) 7.94 12,438 10,210 12,343 10,132 1.2 1.0 82% Cellulose Microcrystalline (3) 7.95 12,090 9,852 11,865 9,669 1.2 1.0 81% Cow Manure (1) 7.69 13,570 9,995 13,392 9,864 1.3 1.0 74% Cow Manure (2) 7.66 13,918 10,195 13,499 9,888 1.3 1.0 73% Cow Manure (3) 7.67 13,718 10,108 13,325 9,818 1.3 1.0 74% Table C.49 . Trial 3 BMP Post - digestion data for 20 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.97 9,230 6,743 9,147 6,682 0.9 0.7 73% Seed 6/15 ( 2 ) 7.93 8,965 6,570 8,830 6,472 0.9 0.6 73% Seed 6/15 ( 3 ) 7.89 9,158 6,768 9,137 6,752 0.9 0.7 74% Cellulose Microcrystalline (1) 6.61 10,458 8,048 10,266 7,900 1.0 0.8 77% Cellulose Microcrystalline (2) 6.54 10,847 8,245 10,562 8,028 1.1 0.8 76% Cellulose Microcrystalline (3) 6.53 11,073 8,520 10,834 8,336 1.1 0.8 77% Cow Manure (1) 7.30 12,750 8,910 12,625 8,823 1.3 0.9 70% Cow Manure (2) 7.33 12,963 9,085 12,823 8,987 1.3 0.9 70% Cow Manure (3) 7.36 12,690 9,025 12,462 8,862 1.2 0.9 71% 244 Table C.50 . Trial 3 BMP Total Solids Reduction for 20 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 9,333 9,230 102 1% Seed 6/15 ( 2 ) 9,108 8,965 143 2% Seed 6/15 ( 3 ) 9,347 9,158 190 2% Cellulose Microcrystalline (1) 12,345 10,458 1,887 15% Cellulose Microcrystalline (2) 12,438 10,847 1,590 13% Cellulose Microcrystalline (3) 12,090 11,073 1,017 8% Cow Manure (1) 13,570 12,750 820 6% Cow Manure (2) 13,918 12,963 955 7% Cow Manure (3) 13,718 12,690 1,028 7% Table C.51 . Trial 3 BMP Volatile Solids Reduction for 20 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 7,175 6,743 432 6% Seed 6/15 ( 2 ) 7,008 6,570 438 6% Seed 6/15 ( 3 ) 7,065 6,768 297 4% Cellulose Microcrystalline (1) 10,160 8,048 2,112 21% Cellulose Microcrystalline (2) 10,210 8,245 1,965 19% Cellulose Microcrystalline (3) 9,852 8,520 1,332 14% Cow Manure (1) 9,995 8,910 1,085 11% Cow Manure (2) 10,195 9,085 1,110 11% Cow Manure (3) 10,108 9,025 1,083 11% 245 C.4.3 30 ° C, non - mixed Table C.52 . Trial 3 BMP Pre - digestion data for 30 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 8.02 9,023 6,795 9,002 6,780 0.9 0.7 75% Seed 6/15 ( 2 ) 7.95 9,425 7,063 9,410 7,051 0.9 0.7 75% Seed 6/15 ( 3 ) 7.95 9,455 7,080 9,423 7,056 0.9 0.7 75% Cellulose Microcrystalline (1) 8.00 12,792 10,500 12,747 10,463 1.3 1.0 82% Cellulose Microcrystalline (2) 7.97 12,848 10,393 12,789 10,345 1.3 1.0 81% Cellulose Microcrystalline (3) 7.98 12,893 10,542 12,740 10,417 1.3 1.0 82% Cow Manure (1) 7.75 14,005 10,268 13,910 10,198 1.4 1.0 73% Cow Manure (2) 7.71 13,853 10,130 13,823 10,108 1.4 1.0 73% Cow Manure (3) 7.68 14,110 10,283 14,076 10,258 1.4 1.0 73% Table C.53 . Trial 3 BMP Post - digestion data for 30 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.67 7,965 5,670 7,901 5,624 0.8 0.6 71% Seed 6/15 ( 2 ) 7.67 8,242 5,917 8,171 5,866 0.8 0.6 72% Seed 6/15 ( 3 ) 7.68 8,100 5,773 8,038 5,729 0.8 0.6 71% Cellulose Microcrystalline (1) 7.34 9,425 7,090 9,281 6,982 0.9 0.7 75% Cellulose Microcrystalline (2) 7.33 8,847 6,400 8,519 6,162 0.9 0.6 72% Cellulose Microcrystalline (3) 7.37 8,670 6,393 8,436 6,220 0.8 0.6 74% Cow Manure (1) 7.51 11,143 7,550 11,044 7,484 1.1 0.7 68% Cow Manure (2) 7.53 11,342 7,555 11,031 7,348 1.1 0.7 67% Cow Manure (3) 7.55 11,430 7,490 11,310 7,411 1.1 0.7 66% 246 Table C.54 . Trial 3 BMP Total Solids Reduction for 30 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 9,023 7,965 1,058 12% Seed 6/15 ( 2 ) 9,425 8,242 1,183 13% Seed 6/15 ( 3 ) 9,455 8,100 1,355 14% Cellulose Microcrystalline (1) 12,792 9,425 3,367 26% Cellulose Microcrystalline (2) 12,848 8,847 4,000 31% Cellulose Microcrystalline (3) 12,893 8,670 4,223 33% Cow Manure (1) 14,005 11,143 2,862 20% Cow Manure (2) 13,853 11,342 2,510 18% Cow Manure (3) 14,110 11,430 2,680 19% Table C.55 . Trial 3 BMP Volatile Solids Reduction for 30 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 6,795 5,670 1,125 17% Seed 6/15 ( 2 ) 7,063 5,917 1,145 16% Seed 6/15 ( 3 ) 7,080 5,773 1,308 18% Cellulose Microcrystalline (1) 10,500 7,090 3,410 32% Cellulose Microcrystalline (2) 10,393 6,400 3,993 38% Cellulose Microcrystalline (3) 10,542 6,393 4,150 39% Cow Manure (1) 10,268 7,550 2,718 26% Cow Manure (2) 10,130 7,555 2,575 25% Cow Manure (3) 10,283 7,490 2,793 27% 247 C.4.4 39 ° C, non - mixed Table C.56 . Trial 3 BMP Pre - digestion data for 39 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.99 9,033 6,797 8,982 6,760 0.9 0.7 75% Seed 6/15 ( 2 ) 7.97 8,993 6,803 8,748 6,618 0.9 0.7 76% Seed 6/15 ( 3 ) 7.97 8,903 6,763 8,788 6,676 0.9 0.7 76% Cellulose Microcrystalline (1) 8.00 12,398 9,995 12,186 9,824 1.2 1.0 81% Cellulose Microcrystalline (2) 8.03 12,590 10,317 12,425 10,182 1.2 1.0 82% Cellulose Microcrystalline (3) 8.04 12,162 10,115 11,899 9,895 1.2 1.0 83% Cow Manure (1) 7.78 13,625 9,920 13,425 9,776 1.3 1.0 73% Cow Manure (2) 7.74 13,970 10,130 13,581 9,847 1.4 1.0 73% Cow Manure (3) 7.75 13,703 9,905 13,535 9,783 1.4 1.0 72% Table C.57 . Trial 3 BMP Post - digestion data for 39 °C , non - mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.62 7,790 5,375 7,663 5,287 0.8 0.5 69% Seed 6/15 ( 2 ) 7.60 8,125 5,513 7,981 5,414 0.8 0.5 68% Seed 6/15 ( 3 ) 7.59 8,075 5,558 8,054 5,543 0.8 0.6 69% Cellulose Microcrystalline (1) 7.38 8,443 5,663 8,340 5,594 0.8 0.6 67% Cellulose Microcrystalline (2) 7.37 8,437 5,915 8,284 5,808 0.8 0.6 70% Cellulose Microcrystalline (3) 7.36 8,540 6,020 8,189 5,773 0.8 0.6 70% Cow Manure (1) 7.48 11,225 7,323 10,940 7,137 1.1 0.7 65% Cow Manure (2) 7.55 10,685 6,928 10,665 6,915 1.1 0.7 65% Cow Manure (3) 7.57 10,583 6,900 10,501 6,847 1.1 0.7 65% 248 Table C.58 . Trial 3 BMP Total Solids Reduction for 39 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 9,033 7,790 1,243 14% Seed 6/15 ( 2 ) 8,993 8,125 868 10% Seed 6/15 ( 3 ) 8,903 8,075 828 9% Cellulose Microcrystalline (1) 12,398 8,443 3,955 32% Cellulose Microcrystalline (2) 12,590 8,437 4,153 33% Cellulose Microcrystalline (3) 12,162 8,540 3,622 30% Cow Manure (1) 13,625 11,225 2,400 18% Cow Manure (2) 13,970 10,685 3,285 24% Cow Manure (3) 13,703 10,583 3,120 23% Table C.59 . Trial 3 BMP Volatile Solids Reduction for 39 °C , non - mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 6,797 5,375 1,422 21% Seed 6/15 ( 2 ) 6,803 5,513 1,290 19% Seed 6/15 ( 3 ) 6,763 5,558 1,205 18% Cellulose Microcrystalline (1) 9,995 5,663 4,333 43% Cellulose Microcrystalline (2) 10,317 5,915 4,402 43% Cellulose Microcrystalline (3) 10,115 6,020 4,095 40% Cow Manure (1) 9,920 7,323 2,597 26% Cow Manure (2) 10,130 6,928 3,203 32% Cow Manure (3) 9,905 6,900 3,005 30% 249 C.4.5 39 ° C, mixed Table C.60 . Trial 3 BMP Pre - digestion data for 39°C, mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.99 9,513 7,100 9,473 7,070 0.9 0.7 75% Seed 6/15 ( 2 ) 8.00 9,375 6,982 9,349 6,963 0.9 0.7 74% Seed 6/15 ( 3 ) 8.00 9,480 7,062 9,419 7,017 0.9 0.7 74% Cellulose Microcrystalline (1) 8.00 12,940 10,470 12,864 10,409 1.3 1.0 81% Cellulose Microcrystalline (2) 7.99 12,493 10,108 12,443 10,068 1.2 1.0 81% Cellulose Microcrystalline (3) 7.96 13,320 10,805 13,236 10,737 1.3 1.1 81% Cow Manure (1) 7.72 13,848 10,070 13,795 10,032 1.4 1.0 73% Cow Manure (2) 7.82 13,850 10,125 13,759 10,059 1.4 1.0 73% Cow Manure (3) 7.67 13,745 10,048 13,663 9,988 1.4 1.0 73% Table C.61 . Trial 3 BMP Post - digestion data for 39°C, mixed Sample pH TS VS TS VS TS VS TS:VS (mg/L) (mg/L) (mg/kg) (mg/kg) (%) (%) Seed 6/15 (1) 7.57 7,375 5,210 7,350 5,192 0.7 0.5 71% Seed 6/15 ( 2 ) 7.54 7,592 5,315 7,577 5,304 0.8 0.5 70% Seed 6/15 ( 3 ) 7.50 7,690 5,192 7,655 5,168 0.8 0.5 68% Cellulose Microcrystalline (1) 7.41 8,182 5,860 8,069 5,779 0.8 0.6 72% Cellulose Microcrystalline (2) 7.42 8,455 6,068 8,279 5,941 0.8 0.6 72% Cellulose Microcrystalline (3) 7.41 8,717 6,332 8,675 6,301 0.9 0.6 73% Cow Manure (1) 7.49 10,475 6,767 10,444 6,747 1.0 0.7 65% Cow Manure (2) 7.56 10,823 7,000 10,744 6,950 1.1 0.7 65% Cow Manure (3) 7.56 10,508 6,762 10,442 6,720 1.0 0.7 64% 250 Table C.62 . Trial 3 BMP Total Solids Reduction for 39°C, mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 9,513 7,375 2,138 22% Seed 6/15 ( 2 ) 9,375 7,592 1,783 19% Seed 6/15 ( 3 ) 9,480 7,690 1,790 19% Cellulose Microcrystalline (1) 12,940 8,182 4,758 37% Cellulose Microcrystalline (2) 12,493 8,455 4,038 32% Cellulose Microcrystalline (3) 13,320 8,717 4,603 35% Cow Manure (1) 13,848 10,475 3,372 24% Cow Manure (2) 13,850 10,823 3,027 22% Cow Manure (3) 13,745 10,508 3,238 24% Table C.63 . Trial 3 BMP Volatile Solids Reduction for 39°C, mixed Sample Initial Final Destroyed Reduction (mg/L) (mg/L) (mg/ L ) (%) Seed 6/15 (1) 7,100 5,210 1,890 27% Seed 6/15 ( 2 ) 6,982 5,315 1,668 24% Seed 6/15 ( 3 ) 7,062 5,192 1,870 26% Cellulose Microcrystalline (1) 10,470 5,860 4,610 44% Cellulose Microcrystalline (2) 10,108 6,068 4,040 40% Cellulose Microcrystalline (3) 10,805 6,332 4,473 41% Cow Manure (1) 10,070 6,767 3,303 33% Cow Manure (2) 10,125 7,000 3,125 31% Cow Manure (3) 10,048 6,762 3,285 33% 251 Appendix D . Additional Data and Figures for Pilot Data D .1 Daily Biogas Production per kg Initial VS Table D .1 . Biogas Production per kg of Initial VS based on Environment Environment Biogas Production per kg Initial VS ± Std. Dev. (L/ kg Initial VS ) Min (L/ kg Initial VS ) Max (L/ kg Initial VS ) n Lab 324±161 53 682 125 Unregulated 291 ± 194 32 894 124 Mesophilic 604 ± 276 59 1 , 272 125 Figure D .1 . Daily Biogas Production for Lab, Unregulated and Mesophilic Pilots - 200 400 600 800 1,000 1,200 1,400 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L/kg Initial VS) Lapsed Time (hrs) Daily Biogas Production Pilot 1 Pilot 2 Pilot 3 Pilot 4 Pilot 5 Pilot 6 252 Figure D . 2 . Daily Biogas Production for Lab Pilots Figure D . 3 . Daily Biogas Production for Unregulated Pilots - 100 200 300 400 500 600 700 800 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L/kg Initial VS) Lapsed Time (hrs) Daily Biogas Production, 20 C Pilot 1 Pilot 2 - 100 200 300 400 500 600 700 800 900 1,000 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L/kg Initial VS) Lapsed Time (hrs) Daily Biogas Production, Bay Area Pilot 3 Pilot 4 253 Figure D . 4 . Daily Biogas Production for Mesophilic Pilots D . 2 Cumulative Biogas Production Figure D .5. Cumulative Biogas Production for Lab Pilots - 200 400 600 800 1,000 1,200 1,400 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Biogas Production (L/kg Initial VS) Lapsed Time (hrs) Daily Biogas Production, 39 C Pilot 5 Pilot 6 - 10 20 30 40 50 60 70 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Cumulative Biogas Production (L) Lapsed Time (hrs) Cumulative Biogas Production, 20 C Pilot 1 Pilot 2 254 Figure D .6. Cumulative Biogas Production for Unregulated Pilots Figure D.7. Cumulative Biogas Production for M esophilic Pilots - 10 20 30 40 50 60 70 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Cumulative Biogas Production (L) Lapsed Time (hrs) Cumulative Biogas Production, Bay Area Pilot 3 Pilot 4 - 20 40 60 80 100 120 140 160 0 500 1,000 1,500 2,000 2,500 3,000 3,500 Cumulative Biogas Production (L) Lapsed Time (hrs) Cumulative Biogas Production, 39 C Pilot 5 Pilot 6 255 Appendix E . Covered Lagoon System Diagram and Table of System Stream Conditions Figure E .1 . System Diagram for a Covered Lagoon Anaerobic Digester Ta b le E . 1. Stream Conditions for Figure E . 1 St ream Volume TS VS (kg/day) 1 4,465,000 104,897 87,869 2 454,200 6,813 5,450 3 4,919,200 111,710 93,319 4 55,855 27,928 18,664 5 4,863,345 83,783 74,656 6 23,938 16,757 12,691 7 4,839,407 67,026 61,964 8 - 30,982 30,982 9 4,839,407 36,044 30,982 10 3,785,000 15,897 12,869 11 1,054,407 20,147 18,113 256 Appendix F . CSTR System Diagram and Table of System Stream Conditions Figure F . 1. System Diagram for a C STR Anaerobic Digester Table F . 1 . Stream Conditions for Figure F . 1 Stream Volume TS VS (kg/day) 1 4,465,000 145,775 112,850 2 454,200 6,813 5,450 3 4,919,200 152,588 118,300 4 61,035 30,518 23,660 5 4,858,165 122,070 94,640 6 3,785,000 56,775 37,850 7 101,532 6,997 4,371 8 971,633 58,298 52,420 9 - 36,694 36,694 10 971,633 21,604 15,726 11 6,173 4,321 2,673 12 965,460 17,283 13,052 13 1,066,992 24,281 17,423 257 Appendix G. Stoichiometric Equation s G.1 Stoichiometric Equation for the Anaerobic Digestion Process The following equation was obtained by utilizing ratios from Phyllis2 database and Chen et al. (2015 ). In order to obtain the equation, procedures presente d by McCarty et al. (2011) were utilized . G.2 Stoichiometric Equation for the Conversion of Ammonia in Soil The following equations were obtained f rom the following literature: Kanter & Brownlie (2019 ), Meynell (1972) and Fontaine (2019) in which the process of the nitrogen cycle and ammonia conversions from digestate in soil are described. G.3 Stoichiometric Equation for the Conversion of Phosphate in Soil The following equations were obtained from the following literature: Kanter & Brownlie (2019 ), and Mullins (20 0 9) in which the process of the phosphorus cycle and phosphate conversions from digestate in soil are described. 258 REFERENCES 259 REFERENCES Alves, M. M., Pereira, M. A., Sousa, D. Z., Cavaleiro, A. 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