Techno-Economic Feasibility and Life Cycle Assessment of Dairy Effluent to Biofuel via Hydrothermal Liquefaction

Transcription

1 Utah State University All Graduate Theses and Dissertations Graduate Studies Techno-Economic Feasibility and Life Cycle Assessment of Dairy Effluent to Biofuel via Hydrothermal Liquefaction Hailey M. Summers Utah State University Follow this and additional works at: Part of the Aerospace Engineering Commons, and the Mechanical Engineering Commons Recommended Citation Summers, Hailey M., "Techno-Economic Feasibility and Life Cycle Assessment of Dairy Effluent to Biofuel via Hydrothermal Liquefaction" (2015). All Graduate Theses and Dissertations This Thesis is brought to you for free and open access by the Graduate Studies at It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of For more information, please contact

2 TECHNO-ECONOMIC FEASIBILITY AND LIFE CYCLE ASSESSMENT OF DAIRY EFFLUENT TO BIOFUEL VIA HYDROTHERMAL LIQUEFACTION by Hailey M. Summers A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Mechanical Engineering Approved: Dr. Jason C. Quinn Major Professor Dr. Lance C. Seefeldt Committee Member Dr. Christine Hailey Committee Member Dr. Mark R. McLellan Vice President for Research and Dean of the School of Graduate Studies UTAH STATE UNIVERSITY Logan, Utah 2015

3 ii Copyright Hailey M.. Summers 2015 Alll Rights Reserved

4 iii ABSTRACT Techno-Economic Feasibility and Life Cycle Assessment of Dairy Effluent to Biofuel via Hydrothermal Liquefaction by Hailey M. Summers, Master of Science Utah State University, 2015 Major Professor: Dr. Jason C. Quinnn Department: Mechanical and Aerospace Engineering Uncertainty in the global energy market and negative environmental impacts associated with fossil fuels has led to renewedd interest in alternativee fuels. The scalability of new technologies and productionn pathways are critically being evaluated through economic feasibility studiess and environmental impact assessments. This work investigated the conversion of agricultural waste, delactosed whey permeate (delac), with yeast fermentation for the generation of biofuel via hydrothermal liquefaction (HTL). The feasibility of the process was demonstrated at laboratory scale with data leveraged to validate systems models used to perform industrial-scale economic andd environmental impact analyses. Results showed a minimum fuel selling point of $4.56 per gasoline gallon equivalent (GGE), a net energy ratio (NER), defined as energy required to process biofuel

5 iv divided by energy in the biofuel produced, of 0.81 and greenhouse gas (GHG) emissions of g CO 2 -eq MJ -1. High production costs can be attributed to operational temperatures of HTL while the high lipid yields of the yeast counter these heating demands, resulting in a favorable NER. The operating conditions of both fermentation and HTL contributed to the majority of GHG emissions. Further discussion focuses on optimization of the process, on the metrics of TEA and LCA, and the evaluation of the process through a sensitivity analysis that highlights areas for directed research to improve commercial feasibility. (57 pages)

6 v PUBLIC ABSTRACT Techno-Economic Feasibility and Life Cycle Assessment of Dairy Effluent to Biofuel via Hydrothermal Liquefaction Hailey M. Summers, Master of Science Delactosed whey permeate (delac) is a low valued by-product in the dairy industry with 90 million tons annually disposed of worldwide. Upgrading delac to bioproducts, specifically biofuel, has been demonstrated at laboratory scale through yeast fermentation. However, the large-scale environmental impact and economic feasibility of this process is yet to be quantified. Further research, focused on evaluating the sustainability, scalability and economic feasibility of the fermentation pathway, directs research and development too move the technology towards commercialization. The enclosed research incorporates biological experimentation with engineering systems modeling to evaluate the large-scale bioproducts from delac. Systemss engineering process models were developed concurrently with biological experimentation to facilitate data feedback from modeling work and streamlinee further experimentation. Integrating process models, validated with experimental results, enabled realistic life cycle and techno-economic assessmentss of various environmental impacts and economic feasibility of generatingg large-scale conversion pathways. Tradeoffs between environmental impact and economic feasibility are leveraged to direct research towards the most

7 vi commercially feasible pathway for the conversion of delac to bioproducts. Results show a minimum fuel selling point of $4.56 per gasoline gallon equivalent, a net energy ratio, defined as energy required to process biofuel divided by energy in the biofuel produced, of 0.81 and greenhouse gas emissions of g CO 2 -eq MJ -1. Discussion focuses on optimization of the process, in terms of techno-economic and life cycle assessments, presented by a sensitivity analysis that highlights areas for directed research to improve commercial feasibility.

8 vii ACKNOWLEDGMENTS First and foremost, much gratitude goes to my advisor, Jason Quinn. My continued motivation and enthusiasm was sustained by Dr. Quinn s dedication and interest in my success. I am genuinely honored to have worked with him and will take many valued lessons with me as I progress in life. Recognition also goes to Rhesa Ledbetter, Alex McCurdy and Mike Morgan for their teachings in biochemistry and assisted laboratory experiments. Your expertise and patience were greatly appreciated. Hailey M. Summers

9 viii CONTENTS Page ABSTRACT... iii PUBLIC ABSTRACT... v LIST OF TABLES... x LIST OF FIGURES... xi ABBREVIATI IONS... xii CHAPTER INTRODUCTION... 1 BACKGROUND INFORMATION... 4 FOUNDATIONAL BIOLOGICAL EXPERIMENTATION Hydrothermal Liquefaction... 8 Lipid Analysis SYSTEMS ENGINEERING MODEL Biomass Fermentationn and Harvest Hydrothermal Liquefaction Nutrient Recycle through CHG Biocrude Upgrading TEA AND LCA MODEL DEVELOPMENT TEA Model Development LCA Model Development Net Energy Ratio Greenhouse Gas Emissions RESULTS AND DISCUSSION Biological Experiment tation... 20

10 6.1.1 Lipid optimization HTL Conversion of Biomass Techno-Economic Analysis Life Cycle Analysis Sensitivity Analysis CONCLUSIONS REFERENCES APPENDICES APPENDIX A APPENDIX B APPENDIX C ix

11 x LIST OF TABLES Table 1 2 Page "N th " plant assumptions as provided byy BETO Growth results for the cultivation of C. curvatus while increasing nitrogenn source without ncreasing carbon source (in control (glucose) and delac) A.1 Growth results for the cultivation of C. curvatus while increasing carbon source without ncreasing nitrogen source (in control (glucose) and delac simulated media A.2 Growth results for the cultivation of C. curvatus while, increasing carbon source and nitrogen source proportionately in control (glucose) and delac simulated media...42 B.1 System inputs for TEA that were applied to all processes of the baseline biofuel pathway B.2 Economic optimization, reported as $ GGE -1, of C. curvatus growth and lipid yields combined with HTL conversion efficiencies B.3 System modeling mass inputs for all processes of the baselinee biofuel pathway C.1 LCA evaluation of HTL conversion efficiencies, with and without catalyst, in combination with C. curvatus growth yields C.2 System modeling emissions resulting from all processes of the baseline biofuel pathway C.3 System modeling energy requirements for all processes of the baseline biofuel pathway

12 xi LIST OF FIGURES Figure Page Process flow diagram and research architecture for delac-to-biofuel biofuel pathway Techno-economic assessment process breakdown with results for the industrial-scale delac-to-bio ofuel pathway, resulting in a MFSP of $4.56 per GGE TEA optimization of the baseline pathway on the basis of process flows and equipment efficiencies resulting in a MFSP of $3.666 per GGE Life cycle assessment results comparedd with traditional and non-traditional methods for obtaining biofuel. NER results for the baseline pathway resulted in 0.81, compared to soybean biofuel att 0.22 and conventional diesel at 0.19 [39] Greenhouse gas emissions for the baseline pathway compared with traditional and non-tradition nal methods for obtaining biofuel. GHG results for the baseline pathway yielded g-co 2 -eq MJ -1, compared to soybean biofuel at g-co 2 -eq MJ -1 and conventional diesel at g-co 2 -eq MJ -1 [22] Sensitivity analysis results of input variables on pathway economics highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway production cost Sensitivity analysis results of input variables on pathway environmental impact highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway GHGs 32 C.1 Sensitivity analysis results of input variables on pathway environmental impact highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway NER C.2 LCA optimization of the biorefinery NER with respect to plant equipment sizing and processing rates. The NER approaches C.3 LCA optimization of the biorefinery GHGs with respect to plant equipment sizing and processing rates. The GHGs approaches g CO2-eq MJ

13 xii ABBREVIATI IONS ANL BETO CHG GGE GC GC-MS GHG HTL LCA NER TEA Argonne National Laboratory Bioenergy Technology Office Catalytic Hydrotherm mal Gasification Gasoline Gallon Equivalent Gas Chromatography Gas Chromatography Mass Spectrometer Greenhouse Gas Emissions Hydrothermal Liquefaction Life Cycle Assessment Net Energy Ratio Techno-Economic Assessment

14 CHAPTER 1 INTRODUCTION Alternative fuel generation has progressed into third and fourth generation biofuels, focusing on improved productivity, decreasing resource requirements, and improving product quality while moving awayy from food-based has gained interest as they have the added benefit of addressing domestic fuel demand while improving the value chain of their respectivee process. In the processing of milk, one of the low-valued products generated is delactosed whey permeate (delac). Compositionally, delac has feedstocks. Production of fuels from traditional waste streams residual lactose, calcium, potassium, and magnesium which makes for an appealing growth media replacement in yeast fermentation [1]. Yeast has the potential to produce valuable bioproducts such as grain alcohol, single-cell protein, and lipids [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Previous experimenta al work has demonstrated the feasibility of yeast fermented on whey with minimal work focused on the potential of using delac as the nutrient source for the production off a biofuel feedstock [3, 4, 8, 11]. Increased global energy consumption has resulted in a variety of feedstocks, ncluding agricultural residues being investigated as feedstocks for renewable fuels. Laboratory-scale research has focused on upgrading delac to value-added products, ncluding sophorolipids and ethanol, through yeast fermentation [2, 3, 6, 8, 11, 12, 13]. The feasibility of generating lipids from a yeast platform, Cryptococcus curvatus, has been demonstrated [4, 14, 15, 16]. However, the use of dairy effluent,

15 2 specifically delac, has not been investigated as a carbon source for yeast fermentation with the intent of biofuel production. Additionally, previous work has required the energy intensive processing of drying yeast feedstocks for downstream processing with minimal work focused on converting wet feedstocks. Conversion of biomass through hydrothermal liquefaction (HTL) has recently been a subject of research interest due to the ability to process wet feedstocks while converting lipid and non-lipid (carbohydrates and proteins) biomass fractions into biocrude which can be upgraded to renewable diesel [17, 18, 19, 20]. The majority of HTL research has focused on microalgae feedstocks with recent investigation of the conversion of other microbes such as yeast [16, 21]. Although biological demonstration of HTL capabilities has been established, minimal work has been performed to understand the economic and environmental feasibility of implementing this technology into an industrial application. Further, no previous work has investigated the environmental feasibility or environmental impacts of generating biofuel from yeast fermented on delac and converted using hydrothermal liquefaction (HTL). Based on the current state of the field, the need exists to understand the scalability, economic feasibility, and environmental impact of biofuel production derived from yeast fermentation on delac and processed through HTL. A systems engineering process model, based on the integration of sub-process models, was developed concurrently with biological experimentation to facilitate experimental data feedback and validation on a process level. The validated engineering process model was leveraged to perform techno-economic (TEA) and life cycle assessments

16 3 (LCA) of an industrial scale biorefinery integrated with a dairy processing facility. TEA and LCA results for the conversion pathway are presented encompassing yeast inoculation through on-site fuel storage, along with a representation of sensitivity to biological and mechanical inputs to illustrate the largest contributors to environmental impact and overall costs. Discussion focuses on optimization of the process, in terms of TEA and LCA, and highlights areas for directed research and development to facilitate demonstration of a commercially feasible pathway for the conversion of delac to biofuel.

17 4 CHAPTER 2 BACKGROUND INFORMATION Alternative fuel generation has progressed into third and fourth generationn biofuels, focusing on improved biomass production and quality. Production of fuels from traditional waste streams has gained interest as they have the added benefit of addressing domestic fuel demand while improving the value chain of their respective process. With 90 million pounds of delac disposed of annually, there is significant potential for impacting the value chain of dairy processing. Compositionally, delac has residual lactose, calcium, potassium, and magnesiumm which makes for an appealing growth media replacementt in yeast fermentation [1]. Yeast has the potential to produce biofuel precursors, lipids, and previous experimental work has demonstrated the feasibility of yeast fermented on whey [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. However, minimal work focused on the potential of using delac as the nutrient source [3, 4, 8, 11]. Laboratory scale research has focused onn upgradingg delac to value-added products, including sophorolipids and ethanol, through yeast fermentation [8, 12]. However, no existing studies demonstrate the feasibility of producing lipids as precursors for drop-in fuel replacement from yeast fermentation on delac [2, 3, 6, 8, 13]. With increased global energy consumptio n, a variety of feedstocks, including agricultural and microbial, are being investigated as feedstocks for renewable fuels. The feasibility of generating lipids from a yeast platform as a means for biofuel production has been demonstrated [9, 16, 22, 23]. However, the use of dairy

18 5 effluent, specifically delac, has not been investigated. Additionally, previous work has required the energy intensive processing of drying yeast feedstocks for downstream processing with minimal work focused on converting wet feedstocks. Hydrothermal liquefaction (HTL) has recently been a subject of research interest due to the ability to process wet biomass while converting lipid and nonlipid (carbohydrates and proteins) biomass fractions into biocrude [17, 18, 19, 20]. The majority of HTL research has focused on microalgae feedstock with recent inclusion of microbes such as yeast [16, 21]. Although biological demonstration of HTL capabilities has been established, minimal work has been performed to understand the economic and environmental feasibility of implementing this technology into an industrial application. No previous work has investigated the impacts on the dairy value chain of generating biofuel through the extraction and conversion of lipids via yeast fermentation on delac. Furthermore, economic and environmental feasibility of using hydrothermal liquefaction (HTL) for the biomass to biocrude conversion process has not been analyzed on this platform. Based on the current state of the field, the need exists to understand the potential impact of biofuel production derived from yeast fermentation on delac and processing through HTL. Systems engineering process models were developed concurrently with biological experimentation to facilitate data feedback from modeling work and streamline further experimentation. The validated engineering process model was leveraged to perform techno-economic (TEA) and life cycle assessments (LCA) of an industrial scale biorefinery integrated with a dairy processing facility. TEA and LCA results for the conversion pathway are presented

19 6 as a whole, encompassing processes from yeast inoculation through on-site fuel storage, along with a representation of sensitive biological and mechanical inputs to illustrate the largest contributors to environmental impact and overall costs. Additionally, discussion of the optimization process is provided, in terms of TEA and LCA, to direct research and development towards the demonstration of a commercially feasible pathway for the conversion of delac to biofuel.

20 7 CHAPTER 3 FOUNDATIONAL BIOLOGICAL L EXPERIMENTATION Initial phases of research focused on demonstration and optimization of lipid production from yeast fermentation on simulated and actual delac. Based on a literature survey and preliminary experimenta ation, the species of yeast examined, acknowledged for high lipid production, Cryptococcus curvatus [4, 5, 9, 16, 24]. C. curvatus (ATCC# 20509), was obtainedd from the American Type Culture Collection (ATCC, Manassas, VA) and preserved at -80 C in YPD [14] media with 20% (% v/v) glycerol and periodically cultured on YPD media Two investigations were performed; 1) lipid optimization from varying carbon and nitrogen concentrations and 2) scaling of batch size to 50 L to produce necessary biomass amounts for HTL experimentation. Initial growth set-up for both investigations was identical. Inoculums were initially cultivated inn approximately 8 ml s of YPD media where cells were held at 30 C and placed in ann incubator at 250 rpm for hours. For lipid optimization, cells were then transferred into 200 ml of medium with varying carbon and nitrogen concentrations, placed on a shaker table operated at 250 rpm and held at a temperaturee of 30 C forr 48 hours. Growth mediums varied carbon (1:2, 1:3 and 1:4) and nitrogen (1:800, 1:400, 1:200, 1:133 and 1:40) ratios, focusing on lipid optimization. The carbon source used, delac, was obtained from Glanbia Foods in Twin Falls, ID and multiple effluent streams were tested due to compositional variations. Cells were harvestedd with a centrifuge operated at 4,000

21 8 rpm for 20 minutes and lysate was removed. Cell pellets were lyophilized and dry cellular masses were obtained to determine biomass yields, calculated by dry cellular masss per volume of fermented media harvested (g L -1 ). Lyophilized cells were stored in -80 C until lipid analysis was performed. A yeast growth for HTL experimentation was performed in a 50 L fermenter with aeration rates of standard cubic meters per minute (CMM, or 3 cubicc feet per minute (CFM)). Additionally, the fermenter was held at 30 C and agitated with three marine blades rotating at 250 rpm. Angerbauer was used as the growth medium with 40 g L -1 lactose and 0.5 g L -1 ammonium sulfate for supplemental nitrogen [25]. Biomass produced in the 50 L fermenter was harvested using a continuous centrifuge and lyophilized until lipid analysis was performed, as described in 3.2Lipid Analysis. 3.1 Hydrothermal Liquefaction Laboratory experimentation for HTL of C. curvatus samples was performed to investigate the feasibility of converting lipid-rich yeast biomass to biocrude through HTL. Lyophilized C. curvatus cell mass was re-suspended in a 1:10 ratio of biomass to water. HTL was performed at ~300 C and 18 MPa (~2650 psi) with and without the use of a catalyst, sodium carbonate (Na 2 CO 3 5% w/w) ). C. curvatus biomass samples were examined using a 2-L high pressure reactorr system (Parr Instruments Co., IL, USA). Samples were run for 30 minutess residence time with an agitation rate of 300 rpm. Resulting HTL lipid percentages were determined using methods described in 3.2 Lipid Analysis.

22 9 3.2 Lipid Analysis C. curvatus growth samples were prepared for lipid analysis following acid- catalyzed fatty acid methyl ester (FAME) methods developed by Wahlen et al. [24]. Methods used dissolve cellular walls, allowingg lipids to be extracted by van der Waals' forces of chloroform. Due to high concentrations of lipids from C. curvatus optimization, samples were dilutedd in chloroform (1:10 lipids chloroform) in gas chromatograph (GC) vials, ensuring sample concentrations to fall within a calibration curve. Lipid concentrations were extrapolated from a standard curve generated using pure methyl myristate (C14:0), methyl palmitoleate (C16:0), and methyl oleate (C18:1). Standard samples for curve generation were prepared at 0.1, 0.2, 0.4, 0.6, 0.8, and 1.0 g L -1 concentrations and used to calibrate a flame ionization detector (FID)(Agilent 123-BD11 column (15mx0.32mm ID with 0.1µ µm film thickness)). Standard and sample concentratio ons were read by a gas chromatograph mass spectrometer (GC-MS) are presented in 6.1 Biological Experimentation. in conjunction with the calibrated FID. Resulting lipid concentrations

23 10 CHAPTER 4 SYSTEMSS ENGINEERING MODEL Concurrently developed with biological experimentation, sub-process models were generated to track mass and energy requirements of processing steps and integrated into an engineering system model representative of a biorefinery capable of processing 3.8 million liters (one million gallons) of dairy effluent, delac, per day. Developmen nt of systemss engineering models representative of a biorefinery interconnected sub-process models as illustrated in Figure 1 and served as the foundation for the TEA and LCA models. The system boundary of the biorefinery includes processes of yeast inoculation, fermentation, harvesting, HTL, phase separation through centrifugation, upgrading biocrude to renewablee diesel (catalytic hydrothermal gasification of an aqueous phase and hydroprocessing of biocrude phase), and biofuel storage. The biorefinery system boundary encompassess all processes of a well-to-product (WTP) analysis allowing for comparison to previous work [26]. Economicss and energetics for transportationn of input materials and resulting biofuel are not included in this study. The outlined conversion pathway will further be referred too as the delac to biofuel pathway.

24 11 Figure 1: Process flow diagram and research architecture for delac to biofuel biofuel pathway Initial calculations were performed around laboratory-scale procedures and appropriate scaling was then applied to develop an industrial-scale model capable of handling 3.8 million liters (1 million gallons) of delac per day. Processing mass losses validated with biological experimentation were applied to all sub-processes. Detailed mass balances for all sub-processes are providedd in Appendix B and Appendix C. Technology and plant infrastructu ure requirements are outlined for each sub-process in subsequent sections. 4.1 Biomass Fermentation and Harvest Inoculation and fermentation processes were similar in design and include tanks, mixing motors, heat exchangers, transfer pumps and feed systems for

25 12 nutrient supply. C. curvatus cultures are inoculated in ample YPD broth, 10 g L-1 yeast extract, 20 g L-1 peptone, and 20 g L-1 glucose dissolved in water, to initiate exponential growth phase. Holding tanks for each component of YPD glucose and for dry yeast are required for mass storage, 5.7 cubic meters (1,500 gallons) each, with feeder systems to inoculation tanks. Additionally, water holding tanks, 3.8 million liters, and supply pumps, 2.2 kw (3 HP), were sized to accommodate required flow rates. All materials are fed into inoculation tanks through transfer pumps where the yeast are continually mixed and held at the appropriate federation temperatures. Inoculation tanks were sized and quoted based on thickness and manufacturing estimates for 409 stainless steel [27]. Tank thickness requirements were based on temperature and pressure requirements of 30 C and atmospheric pressure at the proposed plant location; approximately 1 atm (13 psi). Transfer pumps were sized at 74.6 kw (100 HP) and mixing motors at 2.2 MW (3,000 HP). Transfer pumps were modeled to supply yeast from inoculation tanks to fermentation tanks. At a maximum cell density in inoculant cultures, cells were introduced at a ratio to fermentation growth medium of 1:25. During fermentation, yeast are supplied a nitrogen source and allowed to grow through exponential growth into stationary phase. Thus, nitrogen source storage tanks, sized at 5.7 cubic meters (~1,500 gallons), and associated feeder systems were required. Additionally, C. curvatus require an oxygen supply for cellular respiration and a compressor was modeled to provide aeration rates of standard cubic meters per minute per liter [24]. Cells were continually mixed and held at a temperature of 30 C. Three

26 13 fermenters, 18.9 cubic meters (five million gallons) each, are required to handle the volumes of medium ingredients and create a continuous flow system based on a 48- hour residence time. Leaving the fermenters, yeast cells were pumped to centrifuges where solutions are concentrated to 24% solids (wt. %). Five continuous centrifuges, operated in parallel, were required to process daily flows of 2,270 liters per minute (LPM) (~600 gpm), requiring 74.6 kw (100 HP) motors. Concentrated yeast cultures were pumped into HTL reactorss for conversion to biocrude. 4.2 Hydrothermal Liquefaction HTL represents a promising technology capable of converting biomass to biocrude with minimal work on yeast and limited work on microalgae. Validation of this sub-process model included leveraging experimentall work to verify the performancee of the system and economic justification through previous design case studies focused on microalgae conversion [27,, 28]. Directt scaling was applied based on dry mass processing rates and lipid fraction. HTL reactor design for continuous modeling includes pumps, reactors, knock-outt drums and heat exchangers. Feed streams were modeled continuousl ly in four, parallel reactor trains with heat recycling performed via heat exchangers and supplemented by preheaters. The reactors were modeled as operating at ~300 CC and 18 MPa (~2650 psi) while processing 450 LPM (120 gpm), or 570 kg minn -1 (1,250 lb min -1 ) corresponding to 24% solids. Converted biomass is fed from thee reactors to a gas knockout drum where liquids and gasess are separated. Liquidss passed through a biocrude and water separator from which the biocrude is pumped to a hydroprocessing system for

27 14 further refining and the water was pumped to catalytic hydrothermal gasification (CHG) for the recovery of nutrients. 4.3 Nutrient Recycle through CHG The aqueous phase of HTL consists of mostly water and can be run throughh catalytic hydrothermal gasification to separatee water and nitrogen for recycling of materials. CHG is similar to HTL in that the solution is heated and pressurized to keep water in a liquid state, but differs in the catalyst used. Employing transitionn metal Ruthenium as a catalyst (7.8 w/w % solution), organic material was converted mainly to carbon dioxide and methane. Organic material was removed from the solution, leaving water to be recycledd back to the inoculation and fermentationn processes. Additionally, 90% recoverability of nitrogenn as ammonium sulfate was applied [28]. CHG requires a 30 C temperature increase from HTL effluent resulting in natural gas heating demands of 3.1 x 10-4 kwh per g CH 4 produced [20]. 4.4 Biocrude Upgrading The upgrading of the HTL biocrude into a renewablee diesel product was modeled. Downstream processing of the biocrude and aqueous phases resulting from the HTL process were incorporated into systems engineering models based on the work of Jones et al. and Frank et al. [20, 28]. Scaling factors were established using biomass and percent solid ratios for eachh process. Environmental impacts for the downstream processing of biocrude, hydrotreating and hydrocracking, weree

28 15 calculated from biological and energetic requirements outlined from previous research performed by Bennion et al. [29].

29 16 CHAPTER 5 TEA AND LCA MODEL DEVELOPMENT 5.1 TEA Model Development The modelled biorefinery, capable of processing 3.8 million liters (1 million gallons) of delac per day, was designed with assumptions from the standard reference of the N th plant design [28, 30]. Plant assumptions do not account for first of kind plant costs associated with longer r startup times, large contingency plans, and special financing. Instead, costs represent results when technologies exist and several plants are built and operating [31]. The developed plant is assumed operational for 90% of any given year, or 329 days. Detailed economic inputs are outlined in Table 1. The three year construction period for the N th plant design outlines construction period costs to be allocated as 8%, 60%, and 32% of the fixed capital investment, spent in the first, second and third years, respectively. Additionally, during the startup period of six months, revenue is assumed to be half that of full production, operating costs are assumed 75% of normal and fixed costs are not discounted. Cost data were gathered to account for the startup capital and installation costs as well as operational expenses of the biorefinery. Model assumptions thatt were applied to all processes include an electrical cost of $0.07 kwh -1 [32], cost of water set at $0.53 per cubic meter ($0.002 perr gallon) [33], cost of natural gas of $ 4.03 x 10-3 per MJ ($4.25 MMBTU-1 ) [27] and for processes where installation costs were not obtained from literature, the United States Environmental Protection

30 17 Table 1: " N th " plant assumption ns as provided by BETO Assumption Internal Rate of Return Plant Financing Debt Plant Financing Equity Plant Life Income Tax Rate Interest Rate for Debt Financing Loan Term Working Capital Cost Depreciation Schedule Plant Salvage Value Startup Time Modified Accelerated Cost Recovery System 1 M Valuee Description 10 % 60 % of total capital cost 40 % of total capital cost 30 years 35 % 8 % annually 10 years 5 % of capital cost (excluding land) 7 years MACRS schedule 1 None 6 months Agency (EPA) suggests an installation scaling factor of 17% of capital costs [34]. Additionally, to account for electrical, instrumentation, piping, valve, fitting, structural and contingency costs, percentage assumptionss of processs totals for capital and installation costs were applied [35]. Processing mass losses validated with biological experimentation were applied to all sub-processes. Specific inputs and assumptions at the process level are provided in Table B.1 and detailed mass balances for all sub-processes are provided in Table B LCA Model Development Mass and energy requirements from the systems engineering process model were integrated with life cycle inventory data to develop a life cycle analysis of the delac-to-fuell pathway. LCA work focused on understanding the environmental

31 18 impact and energy consumption of the fuel production pathway. Consistent with TEA models, the system boundary encompasse es all processes from inoculation to hydroprocessing, resulting in drop-in given year, or 329 days. Two metrics were used to fuel. Additionally, the biorefinery is assumed functional for 90% of any understand the environmental impact of the biorefinery; net energy ratio (NER) and greenhouse gas (GHG) emissions Net Energy Ratio The conversion pathway NER was calculated based on the energy to process the biofuel divided by the energy from the produced biofuel. Requirements associated with processing the biofuel include energy demands associated with heating, pressure, mixing, and pumping requirements for all sub-processes. Cumulative energy requirements for the processing of biofuel were then dividedd by the energy in the fuel produced. Energy credits were applied to the sub-processes of fermentation and HTL associated with the use of heat exchangers, capable of recovering 70% of sub-process outflow temperatures. Similarly, an energy efficiency of 60% was applied to alll pump motors. Detailed energy requirements for all sub-process are provided in Table C Greenhouse Gas Emissions The conversion pathway global warmingg potential was evaluated based on the metric of carbon dioxide equivalent emissions, or GHGs. Life cycle inventory data for material consumption and energy was usedd to determine the total life cycle emissions [26, 27, 28]. Carbon dioxide, methane and dinitrogen oxide emissionss

32 19 were combined based on the IPCC 100 year global warming equivalency factors of 1, 25, and 298, respectively for a total global warming potential presented as a carbon dioxide equivalent (CO2-eq). System inputs that are constant for all processes include 521 g CO2-eq (kwh) -1 for electricity and 469 g CO2-eq (kwh) -1 for natural gas [32, 36, 37]. Emission credits were applied for fuel displaced by the conversion process and the displacement of nutrient recycled through CHG. Fuel displacement credit results from the modeled system having both a biofuel product and an HTL solids co-product. The biofuel is assumed to displace traditional petroleum-based diesel, thus a carbon credit corresponding to the carbon contained in the petroleum fuel was applied. The HTL solids co-product is assumed to be land distributed. Considering the carbon source for this conversion process, delac, is typically discharged to wastewater treatment facilities or is land distributed as fertilizer, eventually being converted to carbon dioxide, no carbon credit was assumed as a similar end fate of carbon dioxide results. Additionally, a credit was applied for the 90% recoverability of nitrogen as ammonium sulfate through the sub-process of CHG. Detailed GHG emissions tracking for all subprocesses are provided in Table C.2.

33 20 CHAPTER 6 RESULTS AND DISCUSSION Experimental results from yeast fermentation and HTL conversion are presented followed by TEA and LCA outcomes. TEA and LCA results are presented on a sub-process level to illustrate the largest contributors with a sensitivity analysis used to highlighting areas for further experimental optimization and improved scalability of the proposed process. 6.1 Biological Experimentation Lipid optimization The use of concurrently developed systems engineering models provided data feedback to biological experimentation allowing C. curvatus growth mediums to be optimized with respect to economic impact forr the delac-to-biofuel pathway. Initial growths were performed on undiluted delac, maximizing use of the industrial effluent in the interest of the dairy industry. However, these growths were unsuccessful due to the presence of growth inhibitors, an excess of precipitates in delac, and an insufficient amount of available nitrogen. Therefore, it was necessary to understand the effects of varying delac concentrations and nitrogen supply ratios on yeast productivity. Initial studies weree focused on understanding the productivity potential of yeast on varying concentrations of carbon as undiluted delac did not support growth. Optimization of the dilution ratio wass evaluated in combination with a

34 21 supplemental nitrogen source, ammonium sulfate, held at constant 1:80 nitrogento-medium ratio. The highest lipid productivity was exhibited in a 1:4 delac to growth media ratio with subsequent growths performed at this optimized dilution (results provided in Tables A.1 and A.2). Similarly, ammonium sulfate concentrations were optimized with growths performed in constant 1:4 delac growth media. Experimental results (Table 2) show an increase in biomass production with increasing ammonium sulfate concentrations while lipid (percentage of biomass) decreases. Maximum lipid productivity was achieved at 1.0 g L -1 ammonium sulfate. Table 2: Growth results for the cultivation of C. curvatus while increasing nitrogen source without increasing carbon source (in control (glucose) and delac) Ammonium Biomass Lipid (% of Lipids Productivity Sulfate (g L -1 ) Yield (g L -1 ) biomass) (g L -1 ) 0.25 g/l a ± ± ± g/l a ± ± ± g/l a ± ± ± g/l a ± ± ± g/l a ± ± ± 4.3 a Carbon source (delac) is kept constant at 4 fold dilution Experimental results show an increase in biomass production with increasing ammonium sulfate concentrations while lipid (percentage of biomass) decreases. Maximum lipid productivity was achieved at 1.0 g L-1 ammonium sulfate. All growth results were integrated into systems engineering modeling with data feedback

35 22 determining growth mediums composed of 5 g L-1 ammonium sulfate and 1:4 diluted delac to be optimal for further industrial-scale analyses on the basis of economics and environmental impact HTL Conversion of Biomass HTL experimental work investigated biocrude recovery efficiencies by running yeast samples with and without a catalyst, sodium carbonate, Na2CO3 (5% w/w). C. curvatus growths initially containing 32% lipids were run through HTL reactors without a catalyst resulting in a biocrude conversion efficiency of 49.1%. The efficiency of biocrude recovery was improved to 52.6% with the use of sodium carbonate as a catalyst. Results from the HTL conversion experimentation weree integrated into systems engineering modeling with data feedback determining improved yields of 52.6% biocrude to be optimal for further industrial-scale analyses on the basis of economics and environmental impact. 6.2 Techno Economicc Analysis Experimental results and literature data were used to validate the engineering system model which was leveraged for the TEA analysis. Multiple scenarios, growth results of Table 2, in combination with recovery efficiencies from HTL results, were evaluated as the total yield of the system is a function of the biomass productivity and the HTL conversionn efficiency. Despite maximum lipid productivity seen from growths containing 1.0 g L -1 urea, the TEA results show growths results from 5.0 g L - 1 ammoniumm sulfate to be the most economical due to increased biomass yield. Additionally, HTL biocrude efficiencies of 52.6%, obtained by using the catalyst

36 23 sodium carbonate were advantageous. The outlined conversion pathway, integrated with optimized biological experimentation yields, is defined here as the baseline pathway. TEA results for the baseline production pathway yield a minimum fuel selling price (MFSP) $4.56 per gasoline gallon equivalent (GGE), Figure 2. The MFSP is defined here as the selling price of fuel that results when the net present value of the modeled biorefinery is equal to zero based on the economic assumptions outlined in Table 1. Results can be directly compared to an established soybean conversion pathway of $4.15 per GGE, as well as U.S. average prices for convention diesel in 2014 of $3.38 per GGE [38]. The dominate contributors to the MFSP are operational demands of the yeast inoculation, yeast fermentation and HTL processes. Specifically, the largest contributor to the MFSP, 13.6%, came from the operational requirements for biocrude recovery within the HTL reactors. The next largest contributor to the MFSP was raw inoculation ingredients for YPD glucose, 13.2%. Other input parameters that account for significant portions of the MFSP are capital costs associated with HTL technology (9.3%), capital costs of compressors for aerating yeast during fermentation (6.7%), the operational energy requirements for mixing the yeast culture during fermentation (4.0%), and catalyst consumption in the hydrothermal gasification (3.9%).

37 24 Figure 2: Techno eco onomic assessment process breakdown with results for the industrial scale delac to biofuel pathway, resulting in a MFSP of $4.56 per GGE. The TEA model incorporates currently available industrial equipment capable of handling the required sub-process flows with some equipment being slightly oversized due to off-the-shelf, fixed capacities.. By increasing biorefinery processing capacities above sized equipment specification ns, step-wise inconsistencies became negligible and an optimized MFSP $3.66 per GGE was determined, Figure 3. The reduction in production cost is primarily attributed to the decrease in capital costs associated with the system and is secondarily impacted through decreased operational costs.

38 25 Figure 3: TEA optimization of the baselinee pathway with respect to process flows (plotted on a logarithmic scale (log 1 0)) resulting in a MFSP of $3.66 per GGE. Optimized production costs of $3.66 per GGE demonstrate a price point that is competitive with current costs for biofuel from soybeans,, $4.15 per GGE. Further improvements in the system, such as decreasing fermenter energy, optimization of the HTL biocrude yields and HTL operating temperature make meeting current biofuel cost targets of $3.00 per GGE by 2022 realizable through the proposed process [30]. Furthermore, the baseline pathway is capable of contributing 184 million gallons of biofuel per year, or 14.5% off current U.S. biofuel production,

39 26 calculated from approximately 90 million pounds of annual delac disposal in the United States [39, 40]. 6.3 Life Cycle Analysis Mass and energy requirements from the engineeringg system analysis weree used as primary inputs for the LCA. The environmental assessment of the baseline pathway is presented through two metrics, NER and GHGs. Similarly to the TEA system inputs, multiple scenarios were evaluated as C. curvatus lipid productivity and HTL conversion efficiencies directly impact NER and GHGs. Results from yeast growth on 5..0 g L-1 ammonium sulfate was shown to have the smallest environmental impact with detailedd calculations providedd in Table B.2. The baseline pathway yields an NER of 0.81 and a global warming potential of g CO2-eq MJ- in 1. NER and GHG results for the baseline pathway are presented on a process level Figure 4 and Figure 5, respectively, with a direct comparison to soy-based biofuel and conventional diesel [26, 41]. Biofuel generated from the baseline pathway resulted in a favorable NER of Although an NER of less than 1 is desirable, when compared to traditional soybean-based biofuel or conventional diesel, the proposed process does not compete energetically. The biocrude recoveryy method, specifically HTL, has a conceptual advantage to traditional conversionn technologies, such as lipid extraction or pyrolysis, for yeast biomass due to the ability to process wet material. However, the high energy requirements for the HTL process, attributed to the required temperatures and pressures, make it energetically demanding. HTL

40 27 Figure 4: Life cycle assessment results compared with traditional and non pathway resulted in 0.81, compared to soybean biofuel at 0.22 and traditional methods for obtaining biofuel. NER results for the baselinee conventional diesell at 0.19 [41] operational requiremen nts account for 57.3% of the baseline pathway energy requirements compared to 15.3% from the hexane extraction methods of soybean- upgrading phase, specifically hydrotreating, which accounts for 18.2% of the total energy. The majority of the energy consumed in the hydrotreating process can based biodiesel pathway. A lipid extraction pathway for the proposed system is energetically detrimental due to the water removal requirements. The second largest energetic requirement for the baseline pathway results from the catalytic be

41 28 attributed to hydrogen for fractionation of biocrude, supplied at 57 MJ (kg crude oil) -1. Figure 5: Greenhouse gas emissions for the baseline pathway compared with traditional and non traditional methods for obtaining biofuel. GHGG results for the baseline pathway yielded g CO 2 eq MJ 1, compared to soybean biofuel at g CO 2 eq MJ 1 and conventional diesel at g CO 2 eq MJ 1 [26] GHG emissions for the delac-to-biofuel pathway are g-co2-eq MJ -1. The baseline pathway benefits from GHG emission credits for carbon displacement of conventional diesel production and nitrogen displacement from ammonium sulfate recovery through CHG. However, the GHG emissions are ~176% of those from the production of conventional diesel at g-co 2 -eq MJ -1 and ~148% of current

42 29 soybean-based biofuel GHGs of g-co 2 -eq MJ -1. The largest contributor to GHGs came from the growth phase, specifically fermentation, accounting for 46.1% of the baseline pathway emissions. The growth phase encompasses two components of GHGs, emissions associated with energy demands from processing requirements and direct emissions from the C. curvatus organisms as they digest nutrients. Processing emissions account for 81.6% of the growth phase whereas direct emissions from yeast fermentation are 18.4%. The second largest contributor to GHGs resulted from heating requirements associated with HTL technology, accounting for 42.3% of the output emissions. Finally, the third largest contributor to the baseline pathway GHGs was the energy required to produce glucose for yeast inoculation, 7.2% of total emissions, respectively. Similar to the economic optimization, biorefinery equipment sizing optimization was applied to LCA model to evaluate optimal NER and GHGs. Results yielded NER and GHG totals approaching.74 and g CO 2 -eq MJ -1, respectively, presented in Figure C.2 and Figure C.3. The decrease in values is directly attributed to a reduction in operational energy. Optimization of the baseline pathway GHGs demonstrates emissions that are competitive to those of soybean-based biodiesel and additionally an optimized NER of 0.74 is less than 1 and thus an energetically desirable fuel conversion pathway. It is expected through processing improvements the NER and GHG emission can be further decreased.

43 Sensitivity Analysis A sensitivity analysis was performed to determine system inputs that have the largest impact on the basis of economics and environmental impact. Input parameters that fall outside of the t-critical value, or 95% confidencee interval, are shown to statistically significantly impact the results. Figures 6 and 7 present the results for a two-tailed distribution based on a 95% confidence interval for the baseline pathway economics and GHGs (results for NER provided in Figure C.1).. Additional variables were evaluated as a part of the sensitivity analysis with only the eight most sensitive presented. HTL biocrude conversion efficiency, C. curvatus biomass productivity and hydrotreating efficiency are shown to be sensitive input variables to the baselinee pathway economics as their t-ratios fall outside of the 95% confidence interval, shown in blue (t 95 =2.36). These specific process inputs, resulting outside of the 95% confidence interval, have the potential, through research and development, to significantly impact the MFSP. Input variables inside the 95% confidence interval show small statistical impactt to the pathway economics. Sensitivity analysiss results for the baseline pathway GHGs, Figure 7, highlight C. curvatus biomass productivity, carbon content of the biomass, and HTL conversion efficiency to be the three most sensitive input t variables to the baseline pathway GHGs as they fall outside the 95% confidence interval (t 95 =2.36). Input variables within the 95% confidence interval do not significantly impact the baseline pathway GHGs. Additional variables were evaluated as a part of the sensitivity analysis with only the eight most sensitive presented. Focusing

44 31 Figure 6: Sensitivity analysis results of input variables on pathway economics highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway production cost. Figure 7: Sensitivity analysis results of input variables on pathway environmental impact highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway GHGs. research and development on optimizing statistically sensitive variables can reduce the baseline pathway GHGs closer to those of soybean-based biodiesel and conventional diesel.

45 32 CHAPTER 7 CONCLUSIONS The economic and environmental feasibility of producing biofuel from yeast fermentationn in combination with hydrotherm mal liquefaction was determined. Biological feasibility of the process was demonstrated using C. curvatus when grown on an industrial dairy effluent, delac in combination with oil conversion technologies of HTL. Tracking of mass and energy requirements for biological experimentation, HTL conversion and catalyticc upgradingg of biocrude were developed into systems engineering process models. Process models were leveraged to develop encompassing techno-economic and life cycle analyses to evaluate the fuel conversion pathway on economic and environmental bases. TEA and LCA dataa feedback integrated into biological experimentation and the fuel conversion pathway was optimized with respect to economics and environmental impact. Results indicated a minimum fuel selling price $4.56 per GGE, a net energy ratio of 0.81, and greenhouse gas emissions of f g CO 2 -eq MJ -1. Development of TEA and LCA models for the baseline pathwayy identifies a first round investigation of using delac as a feasible feedstock for the production off biofuel.

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50 APPENDICES 37

51 38 APPENDIXX A BIOLOGICAL EXPERIMENTATION Table A.1: Growth results for the cultivation of C. curvatus while increasing carbon source without increasing nitrogen source (in control (glucose) and delac simulated media) Growth Media Dry Weight (g L -1 ) Lipid (% of biomass) Lipids Productivity (g L -1 ) Glucose 40 g/l a g/l a g/l a Angerbauer 40 g/l a g/l a g/l a a Nitrogen source is kept constant at 0.5 g L 1 ammonium sulfate Table A. 2: Growth results for thee cultivation of C. curvatus while ncreasing carbon source and nitrogen source proportionately (in control (glucose) and delac simulated media) Growth Media Dry Weight (g L -1 ) Lipid (% of biomass) Lipids Productivity (g L -1 ) Glucose 40 g/l a g/l a g/l a Angerbauer 40 g/l a g/l a g/l a a Nitrogen source, ammonium sulfate, is kept at a constant proportion of 80: :1 to carbon source and thus ranging from 0.5 to 1.5 g L 1

52 39 APPENDIXX B TEA MODELING Table B.1: System inputs for TEA that were applied to all processes of the baseline biofuel pathway Description Electricity 0.07 Natural Gas 0.02 Water 0.00 Glucose 0.18 Peptone 0.99 Yeast Extract 0.40 Ammonium Sulfate Natural Gas Installation Costs 17 Mass Lost Processing 5.0 Units $ (kwh $ (kwh $ (gallon) -1 $ (pound) -1 $ (pound) -1 $ (pound) -1 $ (pound) -1 $ (MMBtu) -1 % of capital costs % of process mass Applied to processes not validated by biological experimentation and scaling, i.e.. concentrationn and HTL h) -1 h) -1 Table B.2: Economic optimization, reported as $ GGE 1, of C. curvatus growth and lipid yields combined with HTL conversion efficiencies Ammonium Sulfate Concentration (g L -1 ) Non-catalytic (49.11% conversion) $7.36 $6.22 $5.64 $5.35 $4.87 Catalytic (52.61 % conversion) $6.91 $5.83 $5.29 $5.03 $4.56

53 40 Table B.3: System modeling mass inputs for all processes of the baseline biofuel pathway Process Description Units Inoculation Glucose 25,678 pounds (day) -1 Peptone 25,678 pounds (day) -1 Yeast Extract 12,839 pounds (day) -1 Water 153,846 gallons (day) -1 Total volume inoculated 153,846 gallons (day) -1 Fermentation Feedstock (delac) amount 1,000,000 gallons (day) -1 Feedstock radio to medium 0.25 vol. delac (vol. medium) -1 Water 2,988,701 gallons (day) -1 Supplemental nitrogen (ammonium sulfate) 5.0 grams (liter) -1 Developed yeast from inoculation vol. grown yeast (vol. medium) -1 Developed yeast density 8.1 grams (liter) -1 Natural gas for heating 16,402 cubic meters (day) -1 Total volume fermented 4,153,846 gallons (day) -1 Concentration HTL CHG Percent solids of centrifuge 24 % solids (w/w%) Output volume to HTL 711,522 gallons (day) -1 Catalyst (Na 2 CO 3 ) 56,331 pounds (day) -1 Biocrude (to hydroprocessing) 563,075 pounds (day) -1 Char 240,532 pounds (day) -1 Aqueous Phase (to CHG) 5,745,109 pounds (day) -1 Gas 13,294 pounds (day) -1 Recyclable Water 649,287 gallons (day) -1 Flue Gas 211,682 pounds (day) -1 Hydroprocessing Hydrotreating biocrude 563,075 pounds (day) -1 Biofuel yield 393,151 pounds (day) -1 Heavy oil for hydrocracking 48,216 pounds (day) -1 Biofuel yield 31,664 pounds (day) -1 Biofuel Yield 58,443 gallons (day) -1 Mass flows of HTL include results from separation process as equipment is cohesive

54 41 APPENDIXX C LCA MODELING Table C.1: LCA evaluation of HTL conversion efficiencies, with and without catalyst, in combination with C.. curvatus growth yields LCA NER GWP (g CO 2 -eq MJ -1 ) Ammonium Sulfate Concentration (g L -1 ) Non- catalytic Catalytic Non- catalytic Catalytic a HTL conversion efficiency of 49.11% b HTL conversion efficiency of 52.61% using catalyst sodium carbonate

55 42 Table C.2: System modeling emissions resulting from all processes of the baseline biofuel pathway Process Description Emissions (kg CO 2 -eq(day) -1 ) Inoculation Yeast jet-mixing motor 27,994 Water pump motor 56 Transfer pump motor 560 Natural gas for supplemental heating 1,818 YPD Glucose 30,461 Fermentation Makeup water pump 286 Transfer pump motor 1,680 Compressor motor 41,991 Mixing motor 20,774 Natural gas for supplemental heating 94,875 Nitrogen source pump motor 28 Heat exchange (credit) -24,427 Concentration Centrifuge motor 4,666 Progressive cavity pump motor 933 HTL Natural gas for supplemental heating 539,472 Catalyst (Na 2 CO 3 ) 8,517 Mixing Motors 17,312 Heat exchanger (credit) -134,443 Pressure Requirements 4,438 Separation 1,120 CHG System 5,178 Hydroprocessing Hydrotreatment 34,177 Hydrocracking 2,955 Hydrogen 29,412 Catalyst 38 Credits Biofuel energy 583,105 Nitrogen displacement 226,553 Mass flows of HTL include results from separation process as equipment is cohesive

56 43 Table C.3: System modeling energy requirements for all processes of the baseline biofuel pathway Process Inoculation Fermentation Concentration HTL CHG Hydroprocessing Description Energy Requirements (kwh (day) -1 ) Yeast jet-mixing motor 53,690 Transfer pump motor 1,790 Water Pump 107 Natural gas for supplemental heating 59,464 Makeup water pump motor 548 Transfer pump motor 3,221 Compressor motor 80,536 Yeast jet-mixing motor 39,843 Nitrogen source pump motor 54 Natural gas for supplemental heating 202,293 Heat exchanger (credit) 52,082 Centrifuge motor 8,948 Progressive cavity pump motor 1,790 Natural gas for supplemental heating 1,138,813 Mixing Motors 33,203 Heat exchanger (credit) 286,658 Pressure Requirements 8,511 Separation 2,148 System 11,041 Hydrotreatment 59,459 Hydrocracking 28,750 Hydrogen 229,036

57 44 Figure C.1: Sensitivity analysis resultss of input variables on pathway environmental impact highlighting variables with significant impact, results outside 95% confidence intervals, on the baseline pathway NER. Figure C.2: LCA optimizatio on of the biorefinery NER with respect to plant equipment sizing and processing rates (plotted on a logarithmic scale (log 10 )). The optimized NER approaches 0.74.

58 45 Figure C.3: LCA optimization of the biorefinery GHGs with respect to plant equipment sizing and processing rates (plotted on a logarithmic scale (log 10 )). The optimized GHGs approached g CO 2 eq MJ 1.