The Pennsylvania State University
The Graduate School
Department of Chemical Engineering
DEVELOPMENT OF BIOLOGICAL PLATFORM FOR THE
AUTOTROPHIC PRODUCTION OF BIOFUELS
A Dissertation in
Chemical Engineering
by
Nymul Khan
2015 Nymul Khan
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
May 2015
The dissertation of Nymul Khan was reviewed and approved* by the following:
Wayne Curtis Professor, Chemical Engineering Dissertation Advisor Chair of Committee
Esther Gomez Assistant Professor, Chemical Engineering
Manish Kumar Assistant Professor, Chemical Engineering
John Regan Professor, Environmental Engineering
Phillip E. Savage Walter L. Robb Family Endowed Chair Head of the Department of Chemical Engineering
*Signatures are on file in the Graduate School
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ABSTRACT
The research described herein is aimed at developing an advanced biofuel platform that has the potential to surpass the natural rate of solar energy capture and CO2 fixation. The underlying concept is to use the electricity from a renewable source, such as wind or solar, to capture CO2 via a biological agent, such as a microbe, into liquid fuels that can be used for the transportation sector. In addition to being renewable, the higher rate of energy capture by photovoltaic cells than natural photosynthesis is expected to facilitate higher rate of liquid fuel production than traditional biofuel processes. The envisioned platform is part of ARPA-E’s (Advanced Research Projects Agency - Energy) Electrofuels initiative which aims at supplementing the country’s petroleum based fuel production with renewable liquid fuels that can integrate easily with the existing refining and distribution infrastructure (http://arpa- e.energy.gov/ProgramsProjects/Electrofuels.aspx). The Electrofuels initiative aimed to develop liquid biofuels that avoid the issues encountered in the current generation of biofuels: (1) the reliance of biomass-derived technologies on the inefficient process of photosynthesis, (2) the relatively energy- and resource-intensive nature of agronomic processes, and (3) the occupation of large areas of arable land for feedstock production. The process proceeds by the capture of solar energy into electrical energy via photovoltaic cells, using the generated electricity to split water into molecular hydrogen (H2) and oxygen
(O2), and feeding these gases, along with carbon dioxide (CO2) emitted from point sources such as a biomass or coal-fired power plant, to a microbial bioprocessing platform. The proposed microbial bioprocessing platform leverages a chemolithoautotrophic microorganism (Rhodobacter capsulatus or
Ralstonia eutropha) naturally able to utilize these gases as growth substrates, and genetically modified to produce a triterpene hydrocarbon fuel molecule (C30+ botryococcenes) native to the alga Botryococcus braunii. In addition to the genetic modification and bioreactor performance studies of these organisms for the production of botryococcene or squalene, the research examined the potential economic feasibility of
iv the proposed platform through the use of bioreactor, microbial energetic models and experimentally measured growth yield and maintenance coefficients.
In order to carry out an economic analysis, a process model was created in Aspen with the bioreactor at the center. This is presented in Chapter 2. The model looked at the effects of growth yield and maintenance coefficients of R. capsulatus and R. eutropha, reactor residence time, gas-liquid mass- transfer coefficients, gas composition and specific fuel productivity on the volumetric productivity and fuel yield on H2. It was found that the organism with the lowest maintenance coefficient performed better under very low growth rates evaluated in the model (based on residence time through the reactor) performed the best. The optimum parameter values were then used to determine the capital and operating costs for a 5000 bbl-fuel/day plant and the final fuel cost based on the Levelized Cost of Electricity
(LCOE). It was found that under the assumptions used in this analysis and crude oil prices, the LCOE required for economic feasibility must be less than 2¢/kWh. While not feasible under current market prices and costs, this work identifies key variables impacting process cost and discusses potential alternative paths toward economic feasibility. This was the best case scenario of the two organisms evaluated, and an optimally suited organism with high growth yield and low maintenance coefficient should obviously improve the economics. This economic constraint will improve with the rise of fossil fuel prices, which should occur if the environmentally detrimental effects of their use are factored into the price, through higher taxation, for example.
A review of the current status of metabolic engineering of chemolithoautotrophs is carried out in order to identify the challenges and likely routes to overcome them. This is presented in Chapter 3 of this dissertation. The initial metabolic engineering and bioreactor studies was carried out using a number of gene-constructs on R. capsulatus and R. eutropha. The gene-constructs consisted of Plac promoter followed by the triterpene synthase genes (SS or BS) and other upstream genes. In R. capsulatus, by genetically supplementing the methylerithrotol phosphate (MEP) pathway and supplementing the growth with glucose, it was found that the triterpene synthase enzymes were substrate-limited i.e. depended on
v the carbon-flux to them. A comparison of the production of triterpenes were done in the different growth modes that R. capsulatus was capable of growing – aerobic heterotrophic, anaerobic photoheterotrophic and aerobic chemoautotrophic. Small-scale testing (<50 ml) under typical (un-supplemented) growth conditions showed that the per-cell triterpene production levels were surprisingly similar in all the different growth modes (around 5 mg/gDW). However, the results were much improved when tested in controlled fed-batch bioreactors, capable of reaching significantly higher cell densities. In the heterotrophic case, production was found to increase up to 40 mg/L (~11 mg/gDW), unfortunately inhibited by some sort of toxic effect at OD660 around 12. Autotrophic growth on H2, O2 and CO2, on the other hand, showed no such effect and growth occurred well up to an OD660 of 17 (corresponding to about
7 gDW/L), limited only by the mass-transfer of the gases and triterpene productivity increased continuously to greater than 100 mg/L (16 mg/gDW) in the batch mode. Continuous autotrophic operation further increased the specific titer to 23 mg/gDW, reaching a steady state. The specific productivity was found to be around 0.5 mg/gDW-hr. This demonstrated that autotrophic productivity could likely be improved much further by increasing the available mass-transfer of the reactor. These efforts are presented in Chapter 4 of this dissertation.
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TABLE OF CONTENTS
List of figures ...... ix
List of tables ...... xv
List of symbols and abbreviations ...... xvi
Acknowledgements ...... xvii
Chapter 1 Background and outline...... 1
I. Motivation ...... 1 II. My research ...... 2 III. Rationale for Selection of the Microbial Bioprocessing Platform ...... 6 IV. Metabolic engineering strategy ...... 8 V. Dissertation outline ...... 10
Chapter 2 A process economic assessment of hydrocarbon biofuels production using chemoautotrophic organisms ...... 11
I. Preface ...... 11 II. Specific contributions ...... 11 III. Introduction ...... 12 IV. Model development ...... 13 2.IV.I. Proposed electrofuels production process ...... 13 2.IV.II. Bioreactor modeling ...... 14 V. Results and discussion ...... 22 2.V.I. Sensitivity analysis: effect of reactor residence time (τ) ...... 22 2.V.II. Sensitivity analysis: effect of specific productivity (Rfuel) ...... 24 2.V.III. Sensitivity analysis: effect of gas-liquid mass transfer coefficient (kLa)..... 27 2.V.IV. Process economic analysis of capital and operating costs...... 29 2.V.V. Alternative perspective ...... 33 2.V.VI. Targets based on the analysis ...... 34 VI. Conclusions ...... 36
Chapter 3 Literature review: Metabolic engineering in chemolithoautotrophic hosts for the production of fuels and chemicals ...... 37
I. Preface ...... 37 II. Specific contributions ...... 38 III. Introduction ...... 38 IV. Background ...... 41
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3.IV.I. Carbon Fixation in Chemolithoautotrophs...... 43 3.IV.II. Hydrogen Utilization by Chemolithoautotrophs ...... 47 3.IV.III. Microbial Electrosynthesis via Direct Electron Feeding ...... 49 V. Metabolic Engineering of Natural Chemolithoautotrophic Organisms ...... 50 3.V.I. Status and Constraints for Metabolic Engineering in Chemolithoautotrophs 50 3.V.II. Metabolic Engineering in Aerobic Chemolithoautotrophs ...... 53 3.V.III. Metabolic Engineering in Acetogens ...... 55 3.V.IV. Metabolic Engineering in Other Chemolithotrophs ...... 58 VI. Using Metabolic Engineering to Improve / Creating New Chemolithoautotrophs ... 59 3.VI.I. Engineering Carbon Capture ...... 59 3.VI.II. Engineering improved H2-utilization ...... 62 3.VI.III. Metabolic Engineering of Alternative Electron Delivery Pathways ...... 64 VII. Metabolic Engineering Toolbox for Existing Autotrophs ...... 65 3.VII.I. Genetic Transformation ...... 65 3.VII.II. Plasmids, Promoters and Vectors of Particular Utility for Chemolithoautotrophs ...... 68 VIII. Status of productivities and scale-up considerations for metabolic engineering .... 71 IX. Conclusion ...... 76
Chapter 4 Triterpene hydrocarbon production engineered into a metabolically versatile host – R. capsulatus ...... 78
I. Preface ...... 78 II. Specific contributions ...... 78 III. Introduction ...... 79 IV. Results ...... 83 4.IV.I. Achieving Substrate-Limited Triterpene Biosynthetic Constructs ...... 83 4.IV.II. Productivity Enhancement through Bioreactor Operational Strategies ...... 88 V. Discussion ...... 93
Chapter 5 Ongoing and future work and conclusion ...... 96
I. Triterpene production in R. eutropha...... 96 5.I.I. Pathway enhancements ...... 100 II. Future work ...... 103 III. Conclusion ...... 107
Appendix A Bioreactor material balance equations for section process economic analysis (section 2.III.II) ...... 108
Appendix B A comparison of experimental measurements and thermodynamic predictions of true yield ( ) ...... 111
Calculation of true yield by TEEM ...... 112 Modifications applied to the original TEEM to incorporate autotrophic growth, reversed electron transport, and BPF synthesis for R. eutropha and R. capsulatus ...... 115 Comparison of original TEEM and Modified Electron Balance methods of predicting ...... 118
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Appendix C Measurement of growth yield and maintenance coefficient ...... 121
Growth yield and maintenance coefficient ...... 121 Theory ...... 122 Experimental procedure and results ...... 124
Appendix D Materials and methods...... 129
Bacterial Strains and Growth Conditions ...... 129 Small Scale Phototrophic and Autotrophic Growth Conditions for R. capsulatus Strains130 Heterotrophic Bioreactor Growth Condition ...... 131 Autotrophic Bioreactor Growth Conditions ...... 132 Cloning Procedures ...... 132 Intergeneric Mating Between E. coli S17-1 and R. capsulatus SB1003 ...... 136 Hydrocarbon Analysis ...... 137
Appendix E Background calculations for Figure 4-5...... 139
IV. Aerobic heterotrophic growth on glucose ...... 139 V. Anaerobic photoheterotrophic growth on malate ...... 140 VI. Autotrophic growth on H2 ...... 141 VII. Carbon balance ...... 141 Aerobic heterotrophic growth on glucose ...... 141 Anaerobic photoheterotrophic growth on malate ...... 142 Autotrophic growth on H2 ...... 142
Appendix F Other miscellaneous work ...... 143
VIII. Small scale multiplexed autotrophic screening device...... 143 IX. 100-L plastic bag trickle-bed reactor...... 145 X. Secretion of hydrocarbon by R. capsulatus ...... 146
References ...... 148
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List of figures
Figure 1-1. System load with and without large (16 GW) PV system on two spring days (reproduced from Denholm & Margolis 2006)...... 4
Figure 1-2. Conceptual depiction of an Electrofuels process. Energy from the sun is captured in the form of renewable electricity (depicted as solar photovoltaics, PV) and is used to split water. The resulting O2 and H2 is combined with CO2 and fed into a bioreactor where R. capsulatus consumes these gases in stoichiometric requirement. R. capsulatus is genetically engineered to produce a C30+ triterpene hydrocarbon which can be recovered as an extracellular phase-separated oil...... 5
Figure 1-3. Metabolic engineering strategy for the production of tritepenes in R. capsulatus. 9
Figure 2-1. Process flow diagram of the envisioned Electrofuels process. CO2, H2, O2, fresh nutrient and recycled cells enter the reactor and cells+oil leave the reactor. The hydrophobic top oil layer is separated in oil-water separator and pass through fuel filtration and processed to final product. The remaining cells+water+residual oil from oil-water separator enters a clarifier where the denser cell layer is split into two fractions, one recycled back to the reactor and one sent to sludge processing. The top, less dense overflow from this clarifier enters a second clarifier where cell debris are separated from the water, which is processed through waste-water processing to be recycled to the reactor with fresh nutrients and makeup water...... 14
Figure 2-2. Framework for capturing microbial growth energetics, cellular maintenance and fuel production. (A) Incorporating the experimentally measured biological yield and maintenance coefficients into model reaction/stoichiometry equations suitable for reactor design. (B) Concise version of metabolic pathway for calculating the minimum required Gibbs’ free energy for fuel synthesis in a bacteria...... 17
Figure 2-3. Variation of volumetric productivity, (A), fuel yield on H2, / 2 (B) and cell density, (C) as a function of residence time ( ) through the reactor for R. capsulatus (●) and R. eutropha (o). Simulation parameters: true growth yield ( 2 ) of R. capsulatus = 2.66, R. eutropha = 7.68 gDW/mol-H2, maintenance coefficients ( 2) of R. capsulatus = -3 -3 2.01x10 , R. eutropha = 6.8x10 mol-H2/gDW.hr, kLa = 330/hr, specific productivity = 0.5 g fuel/g biomass.hr, cell recycle efficiency = 95%...... 24
Figure 2-4. (A) Variation of electricity requirement for H2 generation (●) and volumetric productivity (o) with specific fuel productivity representing an increasing fraction of energy being converted into fuel instead of biomass (Simulation parameters: residence time = 15 hr; other parameters same as in Figure 2-3). (B) Variation in reactor volume as a function of kLa showing the reduction in culture process volume that is enabled as the mass transfer rate is improved ( ). The range of electricity requirements that would be required to achieve the specified kLa in different bioreactor configuration is shown as the shaded area between the dashed lines. The high and the low value of the electricity requirement at each kLa corresponds to the highest and the lowest P/V able to produce the specified kLa; the range of P/V for a variety of bioreactor configurations are presented in Figure 2-5...... 26
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Figure 2-5. Gas-liquid mass transfer coefficient (kLa) vs. power/volume (P/V) for various reactor types. Surface aerator 1 = wastewater treatment plant demonstration run, Chattanooga, TN (Cosby & Gay, 2003); Surface aerator 2 = wastewater treatment plant demonstration run, Yuba City, CA (Lewis & Gay, 2003); Stirred 1 = stirred tank reactors correlation for electrolytes, (Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 2 = stirred tank reactor correlation for non-electrolytes (Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 3 = stirred tank reactors correlation for electrolytes (Linek et al., 1987), Vs = 0.005 m/s; TBR 1 = trickle-bed reactor correlation set 1 (Reactor parameters: Roininen et al., 2009, kLa correlation: Goto & Smith, 1975, pressure-drop correlation: Larachi et al., 1991); TBR 2 = trickle-bed reactor correlation set 1 (Reactor parameters: Roininen et al., 2009, kLa correlation and pressure- drop correlation: Reiss, 1967); Microbubble – various = experimental data from various investigators (Bredwell & Worden, 1998; Hensirisak et al., 2002). This compilation of literature values focuses on large-scale commercial units (except for microbubble technology)...... 28
Figure 2-6. (A) Capital costs of various process components for a 5000 bbl/day fuel production plant. Results are grouped into high (black bards) and low (dashed bars) range capital costs. (B) A breakdown of process costs (the dominant cost of electricity generation is not presented). Top (grey) group represents the major operating costs which make up 36% of total; middle (hashed) group represents the major non-photovoltaic capital costs which make up 46% of total; bottom (black) group estimates the major non-PV fixed costs corresponding to 17% of the total. (C) A sensitivity analysis of the final fuel cost based on LCOE of various generation methods reported in two different sources: REN21, 2013 (black); IRENA, 2012 (dashed). CSP = Concentrate Solar Power, PT = Parabolic Trough, ST = Solar Tower. The horizontal line indicates year 2020 crude oil price estimate (Gruenspecht, 2012). Off-grid hydropower values were not available in IRENA, 2012...... 31
Figure 3-1. Analogy between direct photosynthetic growth and indirect photosynthetic growth based on the feeding of electrons as reducing power to reduce CO2...... 39
Figure 3-2. An examination of the CO2 reduction reactions from the perspective of the location of the delivery of the reducing power to either the reactor, or to the inside of the cells as a major determinant of the application of these for biotechnological purposes. Light cannot penetrate dense cultures (Beer-Lambert Law), where gasses face limitations of solubility in the liquid phase (Henry’s Law). Electrons are particularly challenging to deliver (Ohm’s Law) either to a reactor electrode or into the cell although this can be facilitated by the reduction of a ‘carrier’...... 40
Figure 3-3. A summary of autotrophic growth modes with the common theme of reducing CO2 to biomass (carbohydrate) using various forms of reducing power. These different autotrophic growth modes are often associated with other classification names that are listed. These common name classifications are useful, but also lead to problems typical of generalizations.42
Figure 3-4. Schematic illustrating the diversity of the cbb operons including paralogs of the various accessory proteins in addition to the large and small subunit (cbbL/S). Form I Rubisco is denoted by cbbLS and Form II by cbbM, both of which are found in R. capsulatus and R. sphaeroides (Panel A&B). R. eutropha also contains two CBB operons, one on the chromosome (Panel C), and one on the megaplasmid pHG1 with a defective transcriptional activator (cbbR*, Panel D). The iron-oxidizing bacteria Acidithiobacillus ferooxidans
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contain four different CBB operons with two encoding Form I Rubisco subunits. Other genes are: cbbF=SBPase/FBPase, cbbP=PRK, cbbT=TKT; cbbA=FBA/SBA, cbbE=PPE, cbbG=GAPDH, cbbK=PGK, cbbZ=phosphoglycolate phosphatase), cbbBXYQ=unknown. The presence of these functional operons in plasmids provided the historical basis of elucidating autotrophic genes including CO2 fixation and hydrogen use, now adapted to metabolic engineering of these phenotypes in chemolithoautotrophs...... 44
Figure 3-5. The various carbon fixing pathways...... 46
Figure 3-6. Schematic representation of carbon and energy metabolism, regulation and gene expression in a typical aerobic chemolithoautotroph...... 48
Figure 3-7. (A) Maximum reported productivities in grams of carbon fixed in products per liter (gC-fixed/L) in autotrophic systems. Native pathways: PHB by R. eutropha (I), acetate by M. thermoacetica (II), ethanol by C. ljungdahlii (III); engineered pathways: botryococcene by R. capsulatus (IV), methyl ketone by R. eutropha (V), butyrate by C. ljungdahlii (VI). All are timecourses adapted from literature reports presented in Table 3 and reflect growth on CO2 only (not chemoautotrophic hosts utilizing organic carbon source). (B) Maximum theoretical yield on H2 for the native molecules...... 75
Figure 4-1. R. capsulatus is a metabolically diverse organism that can utilize and interconvert the energy provided from the sun in a variety of different ways. Metabolism within the dashed line representing Rhodobacter cell are those reported in this study: aerobic chemoautrophic growth consumes H2 and O2 while fixing carbon, with these gases being provided by photovoltaic-driven electrolysis in this scenario (or other natural sources). Aerobic heterotrophic growth is the typical consumption of sugars and associated aerobic respiration, anaeobic photoheterotrophic growth consumes energy poor organic acids with photosystem- II mediated ATP generation using light energy. Depiction of the evolution (green arrows) or uptake (red arrows) of metabolic gases emphasizes the bioreactor requirements for gas- exchange as a major constraint for process design and operation. The lower part of the figure depicts the metabolic engineering strategy within the Rhodobacter cell to achieve the production of the high energy terpenes botryococcene or squalene. The operon includes the enzyme that commits carbon flux to the MEP pathway: (1-Deoxy-D-xylulose 5-phosphate synthase, dxs) and the isopentenyl diphosphate (IPP) isomerase (idi) to enhance the ratio of IPP to dimethylallyl pyrophosphate (DMAPP). This DNA construct also includes the gene for farnesyl diphosphate synthase (fps) which utilizes one DMAPP and two IPP to produce C15 farnesyl pyrophosphate, the substrate for the terpene synthases (botryococcene synthase, BS; squalene synthase, SS)...... 80
Figure 4-2. Engineering of triterpene synthases with the native or engineered MEP pathways of E. coli DH5α. Constructs were engineered as a single polycistron under the control of the PLac promoter with triterpene synthase only (BS, botryococcene synthase; or SS, squalene synthase); in combination with a prenyltransferase (fps); or with one plasmid-borne copy of the engineered MEP pathway (dxs-idi-fps) or two plasmid-borne copies. Five biological replicates of each strain were grown aerobically for 24 hours in 2xYT-1% glycerol and then extracted with 1:1 acetone and hexane to determine triterpene content...... 84
Figure 4-3. R. capsulatus triterpene metabolic engineering. (a) Constructs containing botryococcene synthase (BS, upper panel) or squalene synthase (SS, lower panel) with
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increasing enhancements in metabolic flux by including the avian farnesyl pyrophospate synthase (fps), and putative rate-limiting enzymes in the MEP pathway. (b) Accumulation of triterpenes in R. capsulatus grown in standard growth medium (Std. medium) and that supplemented with an additional carbohydrate source, 80 mM glucose (+Glu suppl.) for the adjacent constructs: botryococcene for the upper panel, and squalene for the lower panel. The level of enhancement beyond standard growth medium alone is indicated by the extended bars (open bars). Experimental values were determined from five replicate cultures; error bars depict standard error of the mean...... 85
Figure 4-4. Shake flask autotrophic time-course of R. capsulatus harboring pBBR:Plac:BS-dxs-idi- fps and pBBR:Plac:BS only (Top panel) and pBBR:Plac:SS-dxs-idi-fps and pBBR:Plac:SS only (Bottom panel)...... 86
Figure 4-5. Exploring alternative trophisms for hydrocarbon production in Rhodobacter. (a) Triterpene specific productivity of R. capsulatus with pBBR:BS-dxs-idi-fps (blue-left panel), and pBBR: SS-dxs-idi-fps (red-right panel). (b) Reaction for hydrocarbon (HC) production for given tropism. (c) Trophims are presented quantitatively in terms of thermodynamic energy required to assimilate a mole of carbon into pyruvate as well as the energy required to produce an ATP normalized to a molar carbon basis. The carbon balance is shown to illustrate the relative magnitude of carbon produced by aerobic and photo-heterotrophic growth as compared to autotrophic CO2 assimilation...... 88
Figure 4-6. Heterotrophic bioreactor growth of R. capsulatus pBBR:Plac:BS-dxs-idi-fps. R. capsulatus genetically engineered with the above expression vector was grown in a 5 L BioFlo with glucose provided as the sole initial carbon and energy source, followed by fed- batch supplementation based on feedback control of dissolved oxygen (DO). Ammonia was also added in a fed-batch manner for pH control. (a) Growth monitored as OD660 (black circles) and botryococcene accumulation (blue squares) respectively. (b) The DO (red squares) and cumulative glucose supplementation (black line), where glucose fed batch was initiated at ~40 hr, and the approximate times for incremental increases in O2 supplementation (light blue arrows) are noted. Cultures stopped growing and accumulating hydrocarbon at about 70 hr; respiration slowed considerably in stationary phase as indicated by the sharp rise in DO which could not be recovered with additional glucose feed...... 89
Figure 4-7. Growth tests with heterotrophic bioreactor culture and culture supernatant. (a and b) Replicate samples from the bioreactor inoculated into RCVB (-malate) minimal media supplemented with 10 g/L glucose. (c) Sample from the bioreactor inoculated into YCC complex medium supplemented with 10 g/L glucose. (d) Fresh culture of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into bioreactor supernatant supplemented with concentrated YCC medium nutrients (to make the final concentration similar to YCC). (e and f) Replicate fresh cultures of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into bioreactor supernatant supplemented with concentrated RCVB (-malate) medium nutrients (to make the final concentration similar to RCVB). (g) Fresh culture of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB (-mal) medium supplemented with 27 g/L glucose. (h) Fresh culture of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB (-mal) medium supplemented with 10 g/L glucose...... 90
Figure 4-8. Performance of R. capsulatus pBBR:PLac:BS-dxs-idi-fps in an autotrophic bioreactor. R. capsulatus expressing this plasmid was grown with gas phase feeding of H2, O2 and CO2
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(for growth) and liquid phase feeding of ammonia (for pH control) in batch operation for the first 110 hours and under continuous operation after that. Continuous flow was established with 12.5 g/L flow of the CA medium, which corresponds to 10% of µmax of R. capsulatus. (a) The inlet gas composition profile. During the batch growth mode, the compositions were varied to accommodate the growth demand, based on outlet gas composition measurement. Gas consumption did not vary greatly during continuous flow and the inlet composition was essentially kept constant. (b) The OD660 and botryococcene profile during batch and continuous operation. Cells grew exponentially to OD 5 and linearly after that reaching a maximum of 17. Botryoccocene accumulation increased proportionally as the cells and achieved a maximum volumetric accumulation of 110 mg L-1 botryococcene at the end of batch growth. Under continuous operation, the cells reached a steady state within about 130 hrs of starting flow, reaching an OD of ~7 and maintaining a relatively constant level 60 mg L-1 botryococcene. (c) The specific botryococcene productivity profiles. The specific botryococcene levels increased steadily during batch growth to about 17 mg/gDW. Batch specific productivities were around 0.5 mg/gDW-hr. Specific botryococcene continued to increase during the continuous flow and reached 23 mg/gDW. However, specific productivity decreased somewhat and reached a steady level of about 0.3 mg/gDW-hr. 92
Figure 5-1. Triterpene production by R. eutropha transformed with pRK:Plac:SS-fps. The three different colored bars indicate three different clones...... 97
Figure 5-2. Heterotrophic reactor growth of R. eutropha pRK:Plac:SS-fps. (A) Defined medium fed-batch with fructose. (B) LB medium fed-batch with fructose...... 98
Figure 5-3. Comparison between two backgrounds of R. eutropha: PHB- and wild-type in terms of triterpene production, transformed with pRK:Plac-SS-FPS. The cultures are grown in LB medium supplemented with indicated levels of fructose...... 99
Figure 5-4. Bioreactor growth of R. eutropha wild-type transformed with pRK:Plac-SS-FPS. 100
Figure 5-5. Triterpene production by R. eutropha PHB- pRK:Plac:SS-fps + pBBR:Plac:MevT- MBIS (dual plasmid)...... 103
Figure 5-6. Triterpene production by R. eutropha H16 wild-type transformed with pBBR:Plac:BS- dxs-idi-fps...... 101
Figure 5-7. Desired mega-plasmid containing all the MVA pathway genes, the triterpene synthase and fps...... 104
Figure A1. Bioreactor flow streams and mass-balance envelops. 1. Cell separator and recycle, 2. bioreactor and cell separator, 3. bioreactor only...... 108
Figure C1: Experimental setup for the measurement of growth yield and maintenance coefficient.125
Figure C2. Media reservoir weight throughout the experiment. The first derivative of y gives the instantaneous media flow rate, while the second derivative gives the deceleration rate. Media flow rates varies from 10.03 g/hr at the initial steady state to 6.92 g/hr at the final steady state, with a deceleration rate of -0.0974 g/hr2...... 127
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Figure C3. Change of culture density with media flow rate. At the initial steady state the culture density remains around 1 g/L, increasing with decrease of flow rate to about 1.4 g/L during the final steady state...... 127
Figure C4. Plot of µ/Y vs. µ for two D-stat experiments covering two different ranges of dilution rates. Inverse of the slope gives the growth yield and the intercept gives the maintenance coefficients...... 128
Figure F1. (Top) A multiplexed autotrophic growth device design for continuous monitoring of growth using LED/phododiode assemblies at the base of the culture. Gas inlet to multiplexed cultures is achieved with a very small sapphire orifice manifold. (Bottom) Examples of online optical density collected from the screening device while growing different strains of R. capsulatus...... 144
Figure F2. A 100-L low cost plastic bag trickle bed that has been assembled to explore regions of high mass transfer efficacy operation for autotrophic growth...... 146
Figure F3. Hydrocarbon secretion in R. capsulatus autotrophic bioreactor cultures. Triterpene levels in cells, media and total culture during late lag and stationary phase of two independent batch cultures...... 147
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List of tables
Table 2-1. Parameter values used in final scaled-up design. 29
Table 3-1. Catalytic properties of various hydrogenases (compiled from BRENDA). 65
Table 3-2. Comparison of productivities of different compounds in various autotrophic and non-
autotrophic hosts. 75
Table D1. Strains and plasmids used in this study. 130
Table D2. Nucleotide sequences of the wildtype and R. capsulatus codon-optimized genes used. 133
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List of symbols and abbreviations a, b, c, d stoichiometric coefficients in cell growth equation bbl US fluid barrel -1 concentration of fuel in the bioreactor, mol·L -1 liquid phase concentration of i-th gas component, mol·L 2 -1 diffusion coefficient of i-th gas component, m ·s -1 -1 rate of consumption of i-th gas component, mol·L ·h rate of substrate utilization for growth, fuel synthesis, maintenance requirements and total -1 -1 respectively, mol·L ·h EROI Energy Return on Investment F rate of liquid feed into reactor, L·h-1 gDW grams dry weight -1 -1 gas transfer rate of i-th gas component, mol·L ·h -1 Henry’s law coefficient of i-th gas component, atm·L·mol IGCC Integrated Gasification Combined Cycle -1 gas-liquid mass transfer coefficient of the i-th component, h LCOE Levelized Cost of Electricity -1 -1 maintenance coefficient of cells on H2 and O2 respectively mol·gDW ·h , NGCC Natural Gas Combined Cycle PC Pulverized Coal -1 -1 volumetric fuel productivity, g-fuel·L ·h total pressure, atm -1 -1 specific fuel productivity, g-fuel·gDW ·h -1 -1 rate of growth of cells, gDW·L ·h V reactor volume, L X cell density, gDW·L-1 -1 yield of fuel on H2, g-fuel· (mol-H2) / gas phase mole fraction of i-th component -1 true growth yield of cells on H2, gDW·mol ε cell recycle efficiency -1 µ, µmax specific growth rate and maximum specific growth rate of cells, h τ residence time through reactor, V/F, h-1
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Acknowledgements
I would like to thank Prof. Wayne Curtis for his excellent support and mentoring throughout my
PhD. Prof. Joseph Chappell at University of Kentucky provided valuable insights and direction for the project. My colleagues Dr. S. Eric Nybo at University of Kentucky, Dr. Alex Rajangam and Ryan
Johnson at Penn State University were directly involved in the project and provided great assistance in major aspects of the project. My PhD cohorts, classmates and friends Sergio Florez and Dr. Trevor
Zuroff, provided both intellectual and material help. I am immensely grateful to the numerous excellent undergraduate students who voluntarily put their time and effort for the project - Stephanie Tran, Bill
Muzika, Andrew Barmasse, John Myers, Erik Wolcott and Justin Yoo. Microbial energetic model modifications developed by Amalie Tuerk (Masters from Curtis lab, currently doing her PhD at
University of Delaware) were used in the process economic analysis. She also provided major inputs and insights to help write the manuscript related to this. Ben Woolston (honors student from Curtis lab, currently doing his PhD at Massachusetts Institute of Technology) provided valuable inputs for the writing of the literature review on metabolic engineering. Dr. Steven Singer at Lawrence Berkeley
National Lab kindly provided two strains of R. eutropha. Financial assistance for this project was provided by ARPA-E electrofuels award DE-AR0000092.
Chapter 1
Background and outline
I. Motivation
Energy crisis and environmental concerns for CO2 emission have been continuing challenges for mankind in the 21st century. Fossil fuels have provided a readily accessible and energy-dense fuel source to pave the way for science and technology advancements. But their supply is limited, and their use contribute towards the increase greenhouse gas emissions, adversely affecting global climate. Thus sustainable and renewable sources of biofuels are needed.
Global carbon cycle is maintained by photosynthetic plants and microorganisms sequestering CO2 into biomass using Sun’s energy, providing sources of food and energy for life on earth. In the process of their consumption by humans and animals, the CO2 is released back into the atmosphere. In the last hundred years or so, the exponential growth of human civilization and technologies, and thus the use of fossil fuels, have significantly outpaced natural rate of carbon fixation. Maintenance of economic growth has relied on using millions of years of natural accumulation in fossil fuel reserves within a very short period of time. Further implication of this is the increase of atmospheric CO2 by greater than 10%, which is believed to be causing global adversities (Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe 2014). Estimates indicate that the current rate of consumption may be sustained for a few more centuries (IEA
2013), but more catastrophic consequences of rising CO2 levels are prompting research towards reduced greenhouse gas (GHG) emission and carbon capture and storage (CCS) technologies.
These technologies, however, offset the real problem of renewable use of carbon. In addition,
2 depleting reserves and requirement of advanced technologies for unlocking more difficult underground resources mean that prices of fossil fuels will continue to rise in the long term. Only recently have these problems attracted serious attention and concerns from general public and policy-makers and as such, numerous approaches are being funded to develop transformative technologies to address these problems.
Notable efforts in this direction include bioethanol and biodiesel, which are first generation biofuels, i.e. produced directly from food crops (for example corn in US, sugarcane in
Brazil etc.). Corn-ethanol, currently the most widely produced biofuel in the United States, still made up less than 5% of transportation fuels in 2011 (www.eia.gov). Furthermore, there is ongoing controversy related to corn-ethanol contributing to higher food prices. Second generation, non-food biofuels, such as cellulosic ethanol are only recently starting to be commercialized but are limited by natural biomass supply. Third generation algae-biofuels are under active research and development. Even with increasing degree of productivity and sustainability, these technologies still rely on the natural rate of photosynthesis and carbon capture, and this needs to be surpassed if renewable liquid fuels are to be reasonably inexpensive.
II. My research
My research aimed at developing a more advanced biofuel platform that could have the potential to surpass the natural rate of solar energy capture and CO2 fixation. The underlying concept is to use the electricity from a renewable source, such as wind or solar, to capture CO2 via a biological agent, such as a microbe, into liquid fuels that can be used for the transportation sector. The envisioned platform is part of ARPA-E’s (Advanced Research Projects Agency -
Energy) Electrofuels initiative which aims at supplementing the country’s petroleum based fuel
3 production with renewable liquid fuels that can integrate easily with the existing refining and distribution infrastructure (http://arpa-e.energy.gov/ProgramsProjects/Electrofuels.aspx).
In addition to being renewable, the higher rate of energy capture by photovoltaic cells than natural photosynthesis is expected to facilitate higher rate of liquid fuel production than traditional biofuel processes. A conservative estimate of efficiency of today’s photovoltaic cells is around 10% (Peng et al. 2013). Using a maximum biomass yield of 7.68 gDW/mol-H2 for R. eutropha (measured in this work), biomass heat content of 5.411 kcal/gAFDW (Ho & Payne
1979) and electrolyzer energy requirement of 53.4 kWh/kg-H2 (Ivy 2004), we can estimate that the overall efficiency of a system converting CO2 into biomass using H2 should be around 4.3% in terms of energy captured in biomass. Improved efficiency of photovoltaic or other solar energy capture technologies (Bartels et al. 2010) in future should improve this efficiency further.
Compared to this, the best case of sugarcane production (based on a yield of 75 tons/hectare/yr) has an efficiency of 0.38% in terms of total energy captured in biomass (Rosa 2005). Even with a theoretical maximum yield of 280 tons/hectare/yr, the efficiency could only get to 1.4%.
Due to the inherent fluctuations of renewable energy sources, the future integration of renewables into the power grid remains a challenge. Because of operational issues such as non- coincident peaks, non-dispatchability and the stability of the power supplied due to their inherent fluctuating nature (Figure 1-1), there are very legitimate concerns about reliably operating an electrical grid that derives a large fraction of its power renewable sources. Also, solar photovoltaics produce low-voltage DC electricity, which incurs substantial loss in efficiency when converting and stepping up to high voltage AC and subsequently stepping down to low voltage DC for most of its use. Much of the renewable electricity generation is also quite distributed and there is significant cost to create transmission and distribution infrastructure for this electricity.
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Figure 1-1. System load with and without large (16 GW) PV system on two spring days (reproduced from Denholm & Margolis 2006).
The transportation sector is the largest consumer of liquid fossil fuels (13.44 million bbl/day in 2011, www.eia.gov/forecasts/aeo/MT_liquidfuels.cfm) that cannot be readily replaced by electricity. It is also the second largest source of CO2 emission (1.9 billion metric tons in 2008, http://www.eia.gov/oiaf/1605/ggrpt/carbon.html) after power plants. Thus, storing the intermittent and variable energy of renewable electricity into the chemical bonds of a fuel molecule may be a viable alternative to other forms of storage technologies. Also, when comparing liquid fuel products to other energy storage forms, it is apparent that a liquid hydrocarbon fuel is a much more efficient energy carrier by weight and volume than thermal, mechanical, battery and hydrogen energy storage.
The Electrofuels initiative aimed to develop liquid biofuels that avoid the issues encountered in the current generation of biofuels: (1) the reliance of biomass-derived technologies on the inefficient process of photosynthesis, (2) the relatively energy- and resource- intensive nature of agronomic processes, and (3) the occupation of large areas of arable land for feedstock production. To address these issues, the Electrofuels initiative funded research efforts that sought to develop biological processes that would convert distributed, off-grid, renewable electricity into alternative liquid fuels. The logic of this approach rests in its ability to provide a
5 reliable energy source for the transportation sector by storing transiently-available electrical energy in a chemical bond (
Figure 1-2). In addition, the Electrofuels approach is synergistic with advances in photovoltaic cells.
SUN Hydrocarbons Metabolic CO2 Engineering Bioreactor CO Engineering RuBP Botryococcene P.V. 2 3GP Platform O Squalene 2 Rhodobacter Organism e- MVA Engineering IPP NADP H2 MEP H20 Process Modeling NADPH & Economics H2
Figure 1-2. Conceptual depiction of an Electrofuels process. Energy from the sun is captured in the form of renewable electricity (depicted as solar photovoltaics, PV) and is used to split water. The resulting O2 and H2 is combined with CO2 and fed into a bioreactor where R. capsulatus consumes these gases in stoichiometric requirement. R. capsulatus is genetically engineered to produce a C30+ triterpene hydrocarbon which can be recovered as an extracellular phase-separated oil.
Under the Electrofuels initiative, a range of approaches to this challenge were funded
(summary found in (A. Tuerk 2011)). The concept for our approach is depicted schematically in
Figure 1-2 and involves collecting and transporting electrons to a centralized bioreactor for biological capture of the reducing power in the chemical bonds of a hydrocarbon fuel. This proceeds by: (1) the capture of solar energy into electrical energy via photovoltaic cells (with demonstrated laboratory efficiencies upwards of 40%, a seven-fold improvement on photosynthesis), (2) the use of the generated electricity to split water into molecular hydrogen
(H2) and oxygen (O2), and (3) feeding these gases, along with carbon dioxide (CO2) emitted from point sources such as a biomass or coal-fired power plant, to a microbial bioprocessing platform.
6 Our proposed microbial bioprocessing platform leverages a chemolithoautotrophic microorganism (R. capsulatus or R. eutropha) naturally able to utilize these gases as growth substrates, and genetically modified to produce a triterpene hydrocarbon fuel molecule (C30+ botryococcenes) native to the alga Botryococcus braunii. The details and rationale for these choices are discussed below.
III. Rationale for Selection of the Microbial Bioprocessing Platform
Biological approaches for producing alternative fuels are benefitting from advances in molecular biology. These developments have increased the range of fuel molecule targets that can be synthesized by living organisms (Kim et al. 2006; Farmer & Liao 2001), as well as expanded the selection of alternative hosts for production through the development of new genetic engineering tools (Steinbrenner & Sandmann 2006). However, current approaches most frequently use E. coli or yeast as the microbial host with carbohydrate (i.e. fixed-carbon) based substrates. Our approach to the Electrofuels initiative leverages developments in alternative hosts capable of autotrophic growth on H2 as well as developments in fuel targets.
1.III.I.I Microbial Host Selection
For my work, I studied two different chemolithoautotrophs (microbes growing on H2, O2 and CO2) based on specific differences in their physiology, metabolism and energetic yields – R. capsulatus and R. eutropha. I have primarily looked at R. capsulatus, a purple non-sulfur facultative phototroph, as a candidate because of its capability of diverse metabolic modes
(including autotrophic), ability to utilize a range of growth substrates, and innate high level production of carotenoids (Hunter et al. 2009) indicating that pathways for isoprenoid
7 biosynthesis are active in this organism. On the other hand, the chemoautotrophic growth mode was developed as an alternative approach to bioprocessing using R. eutropha (Tanaka et al.
1995). R. eutropha is a well-studied chemoautotroph, whose use was initially motivated by its robust growth and the production of the industrially relevant biopolymer poly-hydroxy butyrate
(PHB). High-density growth (40 – 90 gDW/L) of R. eutropha on gaseous substrates was achieved in high gas-liquid-mass-transfer bioreactors with controlled addition of inorganic nutrients, demonstrating the technical feasibility of such a process. As will be discussed later, R. capsulatus is less efficient in utilizing H2 for growth, but has lower maintenance requirements than R. eutropha, differences which provide economic tradeoffs in an Electrofuels process. The range of yield and maintenance parameters between R. capsulatus and R. eutropha is likely representative of most chemoautotrophs and we were interested in quantifying how these contrasting energetic needs affect the overall economics of the process.
1.III.I.II Hydrocarbon Fuel Molecule Selection
One of the largest barriers faced by alternative energy technologies is poor economics relative to fossil fuels. Having been the most widely developed biofuel to date, ethanol has achieved the most success competing economically with gasoline. However, ethanol’s relative competitiveness results in part from governmental subsidies and mandates that ensure lower cost of ethanol feedstocks and higher selling prices (Solomon et al. 2007). Estimates of the Energy
Return on Investment (EROI) of corn-derived ethanol currently range from a mere 0.7 to 1.65, while those estimated for cellulosic ethanol range from 4.40 to 6.61 (Hammerschlag 2006; de
Castro et al. 2014).
We have chosen the hydrocarbon botryococcene as the product of the Electrofuels process as a means of addressing issue of energy-intensive industrial processing, which is a major
8 drawback to ethanol production. In choosing this fuel molecule target, the project leveraged our experience in working with the alga Botryococcus braunii (Khatri et al. 2014) and the associated discovery of the enzymes responsible for the synthesis of C30 squalene and botryococcene (Okada et al. 2004). C30+ triterpenes include methylated botryococcenes and squalenes, and are produced by B. braunii race B, a colonial green algae that accumulates these hydrocarbon oils up to 30 percent of its dry weight composition (Metzger & Largeau 2005). Ancestors of this alga have been implicated in producing up to 1.4 percent of the total hydrocarbon in Maar-type oil shales based on the unique carbon footprint of C34 botryococcene oil (Derenne et al. 1997). They have properties similar to crude oil (Hillen et al. 1982), a higher energy density than ethanol (38.1 vs.
23.5 kJ/L) (A. Tuerk 2011) and can be easily separated from an aqueous phase by decantation
(Khatri et al. 2014). Given these advantages, it was anticipated that the production of botryococcenes could achieve a higher EROI than ethanol.
IV. Metabolic engineering strategy
The synthesis of triterpenes occur via the terpenoid synthesis pathways. There are two distinct natural terpenoid pathways – the mevalonic acid (MVA) patway and the methylerithritol phosphate (MEP) pathway. The metabolic engineering strategy relied on introducing one or both of these pathways into the autotrophic host in addition to the specific triterpene synthase – squalene or botryococcene synthase (SS or BS). The strategy is schematically depicted in Figure
1-3.
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This is the preferred means for engineering high levels of FPP biosynthesis into Rhodobacter
Operon 1 Operon 2 Operon 4
Pro ERG10 HMGS tHMGR Pro ERG12 ERG8 MVD1 Idi1 FPS Pro SS/BS TMT
Acetyl-CoA Acetoacetyl-CoA HMG-CoA Mevalonate Mev-P Mev-PP IPP DMAPP
Mevalonate (Mev) pathway (eukaryotic) 2x TRITERPENES
FPP
Methyl erythritol phosphate (MEP) pathway (prokaryotic) 2x METHYLATED Pyruvate TRITERPENES + DXP X MEP CDP-ME CDP-ME2P ME-2,4cPP HMB4PP IPP DMAPP G3P
Knockout mutation to be introduced This pathway exists and into the DXP Pro DXS Idi2 FPS Operon 3 operates in Rhodobacter reductoisomerase natively – but is subject locus to endogenous regulatory mechanisms controlling carbon flux down the pathway. This is the alternative approach to engineering high FPP levels in Rhodobacter. But this is not the preferred approach because it relies on too many of the “native” Rhodobacter genes, which will be subject to endogenous regulatory mechanisms.
Figure 1-3. Metabolic engineering strategy for the production of tritepenes in R. capsulatus.
MEP pathway is naturally found in many bacteria including the ones relevant for this work – E. coli, R. capsulatus and R. eutropha. Therefore, the natural metabolic flux of these bacteria can be channeled into the target triterpene molecule by introducing only the key limiting enzymes of this pathway. This is designated as Operon 3 in Figure 1-3. On the other hand, all the enzymes of the MVA pathway must be installed in order for this pathway to be functional in the hosts of our choice. This involves eight enzymes in total and therefore the pathway is broken down into two synthetic operons – Operon 1, converting acetyl-coA to MVA and Operon 2, converting MVA into farnesyl pyrophosphate (FPP). Farnesyl pyrophosphate synthase (FPS) is technically not part of either of these pathways but it is also found to be a rate-limiting enzyme and therefore are included in both of these pathways. More details can be found in Chapter 4.
10 V. Dissertation outline
The project can be divided into three major aspects: 1. Modeling and economic assessment of the electrofuels process, 2. Engineering of the organisms and pathways for the production of biofuel, 3. Bioreactor design and scale-up to improve productivity. The economic assessment provides the potential for economic feasibility of the electrofuels process under some given scenarios as well as identifies the areas for improvement that will have the highest impact.
The electrofuels scenario is quite different from the existing biofuel (e.g. corn or cellulosic ethanol) production processes. No model for this process thus exists for the prediction of the performance and cost of the fuel from this process. In Chapter 2, I describe a framework for integrating the different aspects of the electrofuels process as well as develop bioreactor and organism model that are used for carrying out a process economic assessment. This is published as an article in Bioresource technology (Khan et al. 2014).
Chapter 3 is a literature review of the current status of metabolic engineering of chemolithautotrophic hosts. It briefly covers chemolithoautotrophic metabolism in terms of electron capture and carbon fixation and more in-depth the recent advancements in genetic engineering of these organisms to produce fuels and chemicals, performance of the native and engineered systems and finally the major current and future challenges. It is submitted as an invited review in the journal Metabolic engineering. Chapter 4 describes our efforts to genetically engineer R. capsulatus to produce C30 triterpene hydrocarbons and studying its performance under different conditions including autotrophic.
Chapter 2
A process economic assessment of hydrocarbon biofuels production using chemoautotrophic organisms
I. Preface
In this chapter, the economic analysis of an ARPA-e Electrofuels process is presented, utilizing metabolically engineered R. capsulatus or R. eutropha to produce the C30+ hydrocarbon fuel, botryococcene, from hydrogen, carbon dioxide, and oxygen. This is published as an article in the journal Bioresource Technology. The analysis is based on an Aspen plus® bioreactor model taking into account experimentally determined R. capsulatus and R. eutrohpha growth and maintenance requirements, reactor residence time, correlations for gas-liquid mass-transfer coefficient, gas composition, and specific cellular fuel productivity. Based on reactor simulation results encompassing technically relevant parameter ranges, the capital and operating costs of the process were estimated for 5000 bbl-fuel/day plant and used to predict fuel cost. Under the assumptions used in this analysis and crude oil prices, the Levelized Cost of Electricity (LCOE) required for economic feasibility must be less than 2¢/kWh. While not feasible under current market prices and costs, this work identifies key variables impacting process cost and discusses potential alternative paths toward economic feasibility.
II. Specific contributions
I acted as first author, wrote major portion of the text and facilitated submission of the manuscript to Bioresource Technology. I carried out the measurement of the growth yield and
12 maintenance coefficients for R. capsulatus and R. eutropha, refined the microbial energetic model for fuel production, developed the bioreactor model in Aspen plus® to integrate the experimentally measured parameters with the energetic model of fuel production to predict the operating cost of the process. Amalie Tuerk assisted in refinement of writing and energetic analysis. John Myers facilitated the capital cost estimation of the process and had done considerable writing associated with his thesis. Erik Wolcott helped with the compilation of the gas-liquid mass-transfer coefficients for the various reactor systems.
III. Introduction
This exercise of using process economic analyses to research priorities is an important aspect of the ARPA-E program. An initial analysis of the economic feasibility of the microbial process, based on microbial energetic theory, can be found in an extensive thesis (A. Tuerk
2011). A preliminary process economic model can also be found in (Myers 2013). In this part of the work, I expand upon these previous analyses by constructing a more detailed bioreactor model in Aspen Plus® (described in section 2.IV.II). Furthermore, I examined specific scenarios based on reactor residence time (τ), specific fuel productivity ( ) and gas-liquid mass transfer coefficient (kLa) to predict their effect on the overall process volumetric fuel productivity ( ) and fuel cost ($/bbl-fuel). The ultimate goal of this analysis was to identify limiting parameters for the process and ranges of these variables needed to achieve economic feasibility.
13 IV. Model development
2.IV.I. Proposed electrofuels production process
The process flow diagram of the proposed Electrofuels production process is depicted in
Figure 2-1. Production of the C30+ hydrocarbon fuel by the host organism occurs in the bioreactor,
which is fed the gaseous substrates H2, O2 and CO2. The relative amount of these gases fed is equal to the ratio that supports growth and fuel production by the bacteria. The determination of this stoichiometric ratio is discussed in further detail under Section 2.IV.II.IV. Other nutrients required for cell growth, such as salts and vitamins, are fed to the bioreactor via a liquid stream.
The bioreactor is operated continuously and the bioreactor effluent contains biomass, botryococcene fuel product, and spent culture medium. An underlying assumption critical to this process configuration is that the botryococcene fuel is excreted and deposited as a hydrophobic layer on top of the culture, which can be separated by decantation (oil-water separator). The decanted hydrocarbon layer then flows from the oil-water separator to a fuel filtration system where the residual aqueous phase is removed, and the fuel molecules can then exit the process as the crude hydrocarbon product. We did not include subsequent processing, such as hydrocracking, within the scope of this economic analysis, since the point of comparison is crude oil, which would require similar downstream processing (Hillen et al. 1982). The aqueous phase that exits the oil-water separator flows to a cell clarifier. This concentrates the biomass phase before returning a portion of the biomass to the bioreactor, and the remaining biomass to sludge processing to avoid accumulation of dead cells and cellular waste (perfusion mode operation).
The spent media passes through a waste treatment process (a second clarifier or filter) to remove cellular debris and waste material, and is recycled as fresh medium after supplementing with salts and other nutrients.
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Figure 2-1. Process flow diagram of the envisioned Electrofuels process. CO2, H2, O2, fresh nutrient and recycled cells enter the reactor and cells+oil leave the reactor. The hydrophobic top oil layer is separated in oil-water separator and pass through fuel filtration and processed to final product. The remaining cells+water+residual oil from oil-water separator enters a clarifier where the denser cell layer is split into two fractions, one recycled back to the reactor and one sent to sludge processing. The top, less dense overflow from this clarifier enters a second clarifier where cell debris are separated from the water, which is processed through waste-water processing to be recycled to the reactor with fresh nutrients and makeup water.
2.IV.II. Bioreactor modeling
The biological processes taking place in the bioreactor (cell growth, maintenance and fuel molecule synthesis) determine the rate of substrate consumption (H2, CO2, O2), and therefore the gas-liquid mass-transfer (kLa) requirements, which are the most critical components of the bioreactor operating cost. The bioreactor volumetric fuel productivity (dependent on the specific fuel productivity, residence time and kLa) and the scale of the process (bbl-fuel/day) determine the required size of all associated process equipment, and thus the capital and fixed costs. The
15 capital investment at that plant size can then be amortized at a nominal internal rate of return and combined with the operating costs to arrive at an estimated cost for crude fuel production ($/bbl- fuel). A detailed model of the bioreactor processes associated with hydrocarbon fuel production, which includes cell growth and maintenance, fuel synthesis, and gas transport, is therefore a critical prerequisite to accurately estimating the capital and operating costs. The assumptions related to the bioreactor and development of the associated material balance equations are described in Appendix A.
In aerobic H2-utilizing chemoautotrophic metabolism (referred to as ‘autotrophic’ metabolism from here on), electrons are transferred from H2 to O2 through a series of steps which generate the energy needed for various cellular processes, including CO2 fixation for growth and fuel synthesis. Thermodynamic models (Mccarty 1971; McCarty 2007; A. Tuerk 2011) can be used to calculate these requirements (examples of these methods for calculating the true yield are demonstrated in Appendix B). The microbial substrate requirements ultimately reduce to stoichiometric equations that can be incorporated in a bioreactor model. To integrate the bioreactor model with the full model of the Electrofuels process, we used Aspen plus® software.
We selected Aspen plus® as it can solve complicated material and energy balance equations with rigorous physical property determinations, and we selected the RCSTR reactor model to incorporate the microbial energetic equations described below in sections 2.IV.II.I through
2.IV.II.IV.
2.IV.II.I Substrate requirement for cell growth
Figure 2-2A depicts the conceptual framework for modeling cell growth and maintenance processes. In this context, growth processes refer to the use of energy and carbon substrates for the production of new cellular biomass, whereas cellular maintenance is the use of energy
16 substrates for non-growth processes such as motility, ion pumping, futile cycling etc. (van
Bodegom 2007; Pirt 1965). For autotrophic metabolism, the process of cell growth (excluding energy demands for maintenance) can be represented by a stoichiometric relationship, the
‘growth equation’ as shown below in Equation 2-1.