Assessment of Biocatalytic Production Parameters to Determine

compared to conventional petrochemical production processes. The economic and environmental benefits of improving titer beyond 125 g/l and volumetric ...
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Research Article pubs.acs.org/journal/ascecg

Assessment of Biocatalytic Production Parameters to Determine Economic and Environmental Viability Sampath Gunukula, Troy Runge, and Robert Anex* Biological Systems Engineering Department, University of Wisconsin, 460 Henry mall, Madison, Wisconsin 53706, United States S Supporting Information *

ABSTRACT: The minimum selling price (MSP), specific energy consumption, and greenhouse (GHG) emissions resulting from biobased production of adipic acid, succinic acid, 1,3-propanediol, 3-hydroxy propionic acid, and isobutanol were estimated for various combinations of titer, yield, and volumetric productivity. The MSP, energy consumption, and GHG emissions of anaerobic biobased commodity chemical processes were found to be nearly the same for a given titer, yield, and productivity. The estimated MSP of biobased commodity chemicals produced via aerobic respiration was found to be nearly 30% higher than those of produced through anaerobic fermentation. It was determined that biocatalyst yields of ≥0.32 g/g and titers of ≥45 g/L result in lower production cost, energy consumption, and GHG emissions, when compared to conventional petrochemical production processes. The economic and environmental benefits of improving titer beyond 125 g/L and volumetric productivity beyond 2 g/L·h were found to be low when producing biobased commodity chemicals using a biocatalyst. Comparative economic analysis indicated that provision of feedstock is the dominant cost in commercially viable biobased commodity chemical production systems. KEYWORDS: Sustainability, Renewable chemicals, Biofuels, Techno-economic analysis, Life cycle analysis, Biomass valorization

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INTRODUCTION

Typically, inherent trade-offs exist among biocatalyst yield, titer, and production rates. For example, lower fermentation yields may be offset by achieving higher production rates and vice versa. Determining such trade-offs is necessary to guide biocatalyst technology development by setting performance targets to the technology development team.7−9 The aim of this work is to determine the existence of generalities in the area of biobased commodity chemical production, which can be used by technology development teams to quickly assess the economic potential and environmental sustainability of early stage biocatalytic technologies as well as to guide the development of these new technologies. In this work, we analyzed the economic and environmental performance of a range of biobased commodity chemical production processes that were chosen to be representative of a wide range of such production processes to determine if general trends in the area of biobased commodity chemical production exist. Criteria including nature of cultivation (aerobic/ anaerobic), the type of product separation and purification processes, and the availability of data for the process simulations were considered while selecting the range of biobased commodity chemical production processes. Specifically, the economic and environmental potential of processes for the biocatalytic production of succinic acid, adipic acid, isobutanol, 1,3-propanediol, and 3-hydroxy propionic acid (3HPA) were determined and used for this analysis.

The depletion of fossil feedstocks, economic and market risks to investments, and growing concern over global warming impacts are driving interest in the development of new technologies for the conversion of agricultural and forestry materials to fuels and chemicals.1−4 One way to convert such biobased feedstocks to valuable products is using biocatalysts.5 Advances in the fields of synthetic biology and metabolic engineering have made it possible to modify microbial metabolism to develop efficient industrial biocatalysts that are used to make commodity chemicals and fuels from biobased feedstocks. Significant investment has been made by both government and industry in research and development (R&D) of new biocatalytic technologies for the conversion of biobased feedstocks to fuels and chemicals. Many of these investments have incurred losses and, so far, few technologies have been commercialized.6 To avoid potential losses to R&D investments, it is necessary to screen early stage biocatalytic technologies before large investments are made and to ensure the technologies developed are environmentally benign. Development of environmentally benign biocatalytic technologies is necessary to reduce GHG emissions of chemical and fuel industry. Assessing the economic and environmental potential of new biocatalytic technologies requires expertise of process modeling, technoeconomic analysis (TEA), and life cycle analysis (LCA).7 Often, the biocatalyst development teams do not have expertise in these areas and such assessments require considerable resource investment. © 2017 American Chemical Society

Received: May 31, 2017 Revised: July 18, 2017 Published: July 23, 2017 8119

DOI: 10.1021/acssuschemeng.7b01729 ACS Sustainable Chem. Eng. 2017, 5, 8119−8126

Research Article

ACS Sustainable Chemistry & Engineering Table 1. Model Molecules for Analyzing Multiple Microbial Pathways Using the Feasible Space Approach biobased commodity chemical

resistance to pHa

type of separation

type of cultivation

theoretical yield (g/g)b [source]

anaerobic/ microaerobic anaerobic anaerobic microaerobic aerobic anaerobic microaerobic

0.57 [32]

succinic acid

high (2−3)

distillation

adipic acid isobutanol 1,3-propanediol 1,3-propanediol 3-HPA 3-HPA

low (5−7) high (7−9) high (7−9) high (7−9) medium (4−5) medium (4−5)

distillation distillation LLEc LLE LLE LLE

0.52 0.35 0.51 0.34 0.60 0.55

[29] [33] [35] [34] [30] [31]

a

Values in the parentheses represent the desired pH during the production of biocommodity chemicals. bAll these products are growth-associated products. Above theoretical yields are computed at the specific growth rate of 0.1 g biomass/g glucose·h. cLiquid-Liquid Extraction. After the cell clarification step, adsorption, distillation, or solvent extraction processes can be used to extract a product from the clarified culture media.15 The low capacity and troublesome solids handling of adsorption process made this process not suitable for the extraction of a product from the clarified culture media.19 In the third and final step, the product is purified using processes such as crystallization or ionexchange.16 Among the three separation and purification process steps, the second step dominates the total separations cost, energy consumption, and GHG emissions. This is due to the fact that energy consumption and operating costs of a separation unit process are directly related to the volume of a feed from which the product is extracted.16 Processes with intracellular chemical production require two additional process steps after separating the cellular material from the culture media. The first additional step involves lysing microbial cells to extract the product, and in the second step cell debris are removed.16 A homogenization process can be used for lysing microbial cells, and a membrane filtration process is used to remove the cell debris.16 The increase in capital and operating costs due to the addition of homogenization and membrane filtration processes could make intracellular product accumulation economically unattractive for biobased commodity chemical production. Unfortunately a lack of type and concentration of cellular material data have prevented us from testing this hypothesis in this analysis. Process Flow Diagram (PFD) of Biocommodity Chemical Production. We created PFDs to produce biobased commodity chemicals listed in the Table 1 using information obtained from the patents and published articles (Figures S1−S3). The major process sections of a PFD are the production of sugar from corn through drymill process, conversion of sugar to the biobased commodity chemical, separation and purification of biobased commodity chemical, and the production of dried distillers grains with solubles (DDGS). The biobased commodity chemical production plant was assumed to be located in the Midwest region of United States. The sugar was assumed to be derived from corn, as it is produced abundantly in the Midwest. We obtained modeling parameters to produce sugar and DDGS from Kwiatkowski et al.20 The detailed description of PFDs and the modeling parameters of biobased commodity chemical production are provided in the Supporting Information on this paper. We assumed the nth plant of its kind for the plant design, and therefore, did not account special costs associated with the first of a kind plants. The annual plant capacity of 150 000 MT of corn conversion to a biobased commodity chemical, the plant life of 20 years, and the annual operating days of 330 were assumed. Estimation of Capital and Operating Costs. Each chemical production process was modeled using the SuperPro Designer simulation software. The costs of standard equipment (distillation, evaporators, and heat exchangers) were estimated using purchase-cost charts.21,22 The costs of aerobic vessel, microaerobic vessel, anaerobic fermentor, and seed reactors were determined from vendor quotes and a personal communication with consulting firms. The costs of equipment used in the corn dry grind process and DDGS dryer were obtained from Kwiatkowski et al.,20 and the six-tenths rule was employed to calculate the cost of a required size. The total capital investment of chemical production plant was estimated using a

The GHG emissions (g CO2 equiv/kg), energy consumption (MJ/kg), and the MSP ($/kg) of analyzed biobased commodity chemical production processes were found to be nearly same for a given combination of titer, yield, and productivity. However, these performance metrics did vary with the nature of cultivation (aerobic/anaerobic). General cost, energy, and GHG contour plots were created for the aerobic and anaerobic/micro-aerobic production of biobased commodity chemicals, and these plots can be used to determine economic and environmental feasibility. The results of the plots can also serve as a guide in the development of new processes to produce biobased commodity chemicals using biocatalysts.

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METHODOLOGY

Criteria for the Selection of Chemicals. In this study, we selected multiple biobased commodity chemical processes that meet the following criteria of process characteristics: aerobic/anaerobic/ microaerobic, extracellular product formation, biocatalyst resistance to pH changes, and distillation/solvent processes for the extraction of a product from the clarified culture media (Table 1). For the detailed description of the metabolic route for each of these chemicals listed in Table 1, please refer to the Supporting Information. The criteria for the selection of a range of biobased commodity chemical processes were determined by considering major process steps that influence the economic and environmental performance of biobased commodity chemical production. The major process steps of a biobased commodity chemical production using a biocatalyst include feedstock production, conversion of a feedstock to the chemical in a bioreactor, and the separation and purification of the chemical.10,11 The contribution of feedstock cost, energy consumption, and the GHG emissions to the total production cost, energy consumption, and GHG emissions of a process for the production of a biobased commodity chemical are directly related to the process parameter yield.12 The amount of an acid or base added to the bioreactor to maintain pH of the cultivation process is one of the factors affecting the cost of a cultivation media. The addition of acid or base to a media can be minimized by developing a biocatalyst that is tolerant to a wide range of pH values.13 The bioreactor cost is a function of volumetric productivity (process parameter) and the type of a cultivation process (aerobic/anaerobic/microaerobic).14 The total cost, energy consumption, and GHG emissions of separation and purification processes used for the extraction of a chemical from the cultivation broth are driven by titer (process parameter), nature of a product accumulation (intracellular/extracellular), and the type and number of unit processes used for the extraction of a product after aerobic/anaerobic/microaerobic cultivation.15 In the case of extracellular chemical production, the first step of the downstream processing involves separation of microbial cells from the culture media.16 However, a cell clarification step is not required in the production of few biobased commodity chemicals. For instance, culture broths containing isobutanol and ethanol are directly sent to a distillation column after the fermentation process.17,18 8120

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ACS Sustainable Chemistry & Engineering method based on delivered cost of process equipment.22 Capital costs of biocommodity chemical production plants will be different for different titers and productivities. A combination of titer of 100 g/L and productivity of 2 g/L·h has been chosen to represent capital costs of biocommodity chemical production processes. These numbers will provide a general idea of the capital costs associated with the production of biocommodity chemicals. The total capital costs ($ MM) associated with the production of adipic acid, succinic acid, 1,3propanediol (microaerobic conditions), 1,3-propanediol (aerobic conditions), 3-HPA (microaerobic conditions), 3-HPA (anaerobic conditions), and isobutanol are 149, 151, 134, 272, 145, 139, 137, respectively. The material and energy balances of each chemical production process were obtained from the process simulations. The material balance was used to calculate the required quantity of raw materials, and the energy balance was utilized to determine steam and electricity requirements. The raw material and utility prices, labor and maintenance costs, and local taxes used in this analysis are listed in Table S1. The discounted cash flow analysis (DCA) method was used to compute the biobased commodity chemical MSP. A discount rate of 10% was employed in the DCA.16 Estimation of Life Cycle Energy Use and GHG Emissions. The cradle-to-gate energy consumption and GHG emissions of biobased commodity chemical production processes were calculated using LCA methodology. The LCA system boundary covers all activities from corn production to the production of a biobased commodity chemical. The functional unit was defined as 1 kg of biocommodity chemical production. The life cycle GHG emissions and energy consumption of steam, electricity, and corn production processes were obtained from the Ecoinvent database in SimaPro 7.2 software.23 The 2007 Intergovernmental Panel on Climate Change (IPCC) Global Warming Potentials (GWPs) method was employed to convert GHG emissions to CO2 eq emissions. The economic allocation approach was used to partition energy consumption and GHG emissions of biobased commodity chemical production process among the product and coproduct DDGS.24 We calculated MSP, energy consumption, and GHG emissions of biobased commodity chemical production processes for a wide range of yield, titer, and volumetric productivity values. The performance contour plot for cost was created by mapping biobased commodity chemical MSP to the corresponding yield, titer, and volumetric productivity. Similarly, performance contour plots for energy and GHG were created. The energy consumption and GHG emissions of petroleum based adipic acid, succinic acid, isobutanol, 3-HPA, and 1,3propanediol production processes were obtained from the literature.25,26 The market prices of these chemicals were obtained from ICIS chemicals.27 The performance metrics of conventional processes and performance contour plots were used to determine feasible curves of cost, energy, and GHG. The feasible space of each biobased commodity chemical production process was defined by graphing the feasible curves of cost, energy, and GHG along with yield, titer, and volumetric productivity constraints. The maximum attainable yield, titer, and production rates were used to determine yield, titer, and volumetric productivity constraints, respectively.7

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and productivity. But, for a particular fermentation yield only a 200% increase in MSP is noticed when the titer is reduced to 10 from 200 g/L. The high effect of yield is mainly because the entire upstream of biobased commodity chemical production (including production of corn, production of glucose from corn using the dry grind process, and the production of a chemical from glucose in a bio reactor) are affected by any variation in the fermentation yield value. The MSP of biobased commodity chemicals, except those produced aerobically, is found to be nearly constant for a given titer, yield, and productivity. For example, the calculated MSP of 1,3-propanediol that is produced under microaerobic fermentation is 1.13 ($/kg) for the yield of 0.6 g/g, volumetric productivity of 2 g/L·h, and the titer of 50 g/L (Figure S5). For the same process parameter values, the MSP of aerobically produced 1,3-propanediol is estimated at 1.66 ($/kg). Such an increase of MSP is due to the requirement of high capital and operating costs for the aerobic cultivation as compared to anaerobic/microaerobic fermentation. The current available agitator size limits the aerobic reactor volume to 4000 kL.14 The cost advantages due to economies of scale are, therefore, minimized for an aerobic reactor, which increases the capital cost of aerobic cultivation processes. The compressor energy requirements and the energy losses in gassing systems increase the operating costs of aerobic cultivation processes.14 The addition of acid or base to maintain medium or high pH in the production fermentor is found to have negligible impact on the production cost of biobased commodity chemicals. For example, ammonium hydroxide is added to the fermentor during adipic acid production to maintain the pH around 5.28 The addition of such base is not necessary for the succinic acid fermentation as the genetically modified yeast strain can make succinic acid under low pH conditions.28 However, the estimated production costs of adipic and succinic acids for a given combination of titer, yield, and productivity are nearly the same (Figure S6). It should be noted that the salts formed due to the addition of acid or base will end up in the DDGS, and it is assumed in this analysis that the presence of salts do not affect the market price of DDGS. Please refer to the Supporting Information on this paper for the sensitivity analysis if the salt is assumed to be a waste stream and the impact that the additional cost of salt waste treatment will have on the MSP of the biocommodity chemical. The downstream processing costs of biobased commodity chemical production processes are found to be nearly the same for a given titer even with different number and type of separation processes. For example, distillation and solvent extraction processes are used to extract isobutanol and 3-HPA from the culture media, respectively. Moreover, the total number of unit processes for the purification of isobutanol is low compared to the purification of 3-HPA. Even with those differences, the MSP of isobutanol and 3-HPA are found to be nearly the same for a given titer, yield, and volumetric productivity. For the detailed description of PFD of isobutanol and 3-HPA production processes, please refer to the Supporting Information. General Cost Contour Plots of Biobased Commodity Chemical Production. Since the MSP of a biobased commodity chemical varies with the titer, yield, volumetric productivity, and the nature of cultivation, we generated general cost contour plots in terms of yield, titer, and volumetric productivity for the aerobic and anaerobic/microaerobic production of biobased commodity chemicals (Figure 1). The

RESULTS AND DISCUSSION

Cost Contour Plots of Biobased Commodity Chemical Production. The MSP of adipic acid, succinic acid, 1,3propanediol, 3-HPA, and isobutanol are calculated for various combinations of volumetric productivity, titer, and yield. The cost contour plots that represent the relationship between MSP, yield, titer, and volumetric productivity are created for biobased commodity chemical production processes (Figures S5−S7). The comparison of cost contour plots shows that biobased commodity chemical MSP is more sensitive to yield than titer and productivity. For example, there is a nearly 1000% increase in the biobased commodity chemical MSP, when the yield is decreased from 1 to 0.1 g/g for a certain titer 8121

DOI: 10.1021/acssuschemeng.7b01729 ACS Sustainable Chem. Eng. 2017, 5, 8119−8126

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ACS Sustainable Chemistry & Engineering

Figure 1. General cost contour plots for anaerobic production of a biobased commodity chemical with a productivity of (a) 1, (b) 2, and (c) 3 g/L·h; for aerobic production of a commodity chemical with a productivity of (d) 1, (e) 2, and (f) 3 g/L·h. The cost contour lines represent MSP ($/kg) of biocommodity chemicals.

productivity to 3 from 2 g/L·h causes only a slight decrease in the MSP of a biobased commodity chemical (Figure 1). For a given titer and yield, the biobased commodity chemical MSP is reduced approximately 3.5% when the productivity is improved to 3 from 2 g/L·h (Figure 1). A similar comparison for the aerobic based commodity chemical production demonstrates a

data of MSP of 3-HPA is used to generate cost contour plots for the anaerobic/microaerobic process, and the data of MSP of 1,3-propanediol that is produced via aerobic cultivation is used to generate general cost contour plots for the aerobic process. Comparison of general cost contour plots of anaerobic/ microaerobic processes indicates that improving volumetric 8122

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ACS Sustainable Chemistry & Engineering

Figure 2. General energy and GHG contour plots. (a) Energy plot for the anaerobic/microaerobic production of biobased commodity chemical (b) and for aerobic production of biobased commodity chemical; (c) GHG plot for the anaerobic/microaerobic production of biobased commodity chemical (d) and for aerobic production of biobased commodity chemical; energy contour lines represent total energy consumption (MJ/kg) of biocommodity chemical production; GHG contour lines represent total GHG emissions (g CO2 equiv/kg) of biocommodity chemical production.

investments to develop a biocatalyst that synthesize glucaric acid from glucose will not be profitable. Performance cost targets can be set to the biocatalyst development team using the general contour plots of cost. For example, the average market price of adipic acid is 1.80 ($/kg.).27 For this target price, general cost contour plots in Figure 1 shows various economically viable combinations of productivity, yield, and titer for making the adipic acid from glucose using a biocatalyst. The yield, titer, and volumetric productivity targets for the technology development team can be determined by selecting one viable combination. This selection can be done by comparing development time and costs that are required to develop a biocatalyst that exhibits each viable combination of process parameters. Such development time and costs can be qualitatively computed by the management and research teams. General Energy and GHG Contour Plots of Biobased Commodity Chemical Production. The data of estimated energy consumption and GHG emissions of biobased commodity chemical production processes are used to create contour plots of energy and GHG, respectively (Figures S8 and S9). The energy, GHG, and cost contour plots of biobased commodity chemical production processes have resulted in a similar shape (Figures S5, S8, and S9). This indicates that there

16% decrease in MSP (Figure 1). This is mainly due to there being no economy of scale advantage for aerobic processes. A nonlinear relationship is found between the biocommodity chemical MSP and the product titers. The economic benefits of improving fermentation titer beyond 125 g/L are found to be low for both aerobic and anaerobic/microaerobic biobased commodity chemical production processes. For a certain productivity and yield, increasing titer to 200 from 125 g/L causes the biocommodity chemical MSP to drop only by 2% (Figure 1). Thus, investments for pushing titers beyond 125 g/ L must be avoided. Use of General Cost Contour Plots of Biobased Commodity Chemical Production. The economic viability of new processes for the production of biobased commodity chemicals using biocatalysts can be determined by utilizing general cost contour plots. For example, glucaric acid can be made from sugar using E. coli under anaerobic conditions.28 The theoretical yield and production rates of glucaric acid production are estimated at 0.3 g/g and 2 g/L·h, respectively, from stoichiometric calculations.29 The current market price of glucaric acid is 0.80 ($/kg).27 For this market price, cost contour plots in Figure 1 shows that yields of greater than 0.8 g/g are necessary to make a process for the production of glucaric acid that is economically viable. Since the required glucaric acid yield is greater than the theoretical yield, 8123

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Figure 3. Feasible space of processes for the production of (a) 3-HPA (anaerobic), (b) 1,3-propanediol (anaerobic), (c) adipic acid, (d) succinic acid, (e) isobutanol, and (f) 3-HPA (microaerobic). The green curve represents the feasible cost curve, the red curve represents the feasible energy curve, and the blue curve represents the feasible GHG curve. The purple vertical straight line indicates theoretical yields of biocommodity chemical production.

is a correlation between economic and environmental performance metrics for the cases studied here. The comparison of GHG and energy contour plots indicates that the energy consumption and GHG emissions of biobased commodity chemical production processes, except those made using aerobic cultivation, are found to be nearly the same for a given combination of titer, yield, and productivity (Figures S8 and S9). In addition, the decrease in the energy consumption and GHG emissions of anaerobic/microaerobic processes are found to be very low (≈0) when the volumetric productivity is increased from 1 to 2 g/L·h. The decrease in the energy consumption (MJ/kg) and GHG emissions (kg CO2 equiv/kg) of the aerobic process is around 2% and 0.2%, respectively, when the productivity is increased from 1 to 2 g/L·h for a given titer and yield (data is not shown). Like the biobased commodity chemical MSP, the general counter plots of GHG and energy are generated for the processes of biobased commodity chemical production (Figure 2). Like the MSP contour plots, the general GHG and energy contour plots shown in Figure 2 can be used to screen early stage biobased commodity chemical processes in terms of environmental performance and to set environmental performance targets for new biocatalytic technology developments by governmental policy makers. 2D-Feasible Spaces of Biobased Commodity Chemical Production. Conventionally, adipic acid, succinic acid, isobutanol, 1,3-propanediol, and 3-HPA can be produced from a petroleum feedstock.25,26 The market prices ($/kg) of adipic acid, succinic acid, isobutanol solvent, 1,3-propanediol, and 3HPA are 1.80, 1.80, 2.2, 1.70, and 1.60 respectively. These

market prices are obtained from the ICIS chemicals pricing report.27 The cradle-to-gate energy consumption (MJ/kg) of conventional adipic acid, succinic acid, isobutanol, 1,3-propanediol, and 3-HPA production processes are 124, 110, 60, 150, and 120, respectively.25,26 The cradle-to-gate GHG emissions (kg CO2 equiv/kg) of conventional adipic acid, succinic acid, isobutanol, 1,3-propanediol, and 3-HPA production processes are 9, 12, 3, 12, and 7, respectively.25,26 The feasible cost curves are determined using both the market prices of chemicals and general cost contour plots. For example, the market price of adipic acid is 1.80 ($/kg.).27 The contour line of 1.80 in Figure 1b represents the feasible MSP curve for adipic acid production. Similarly, feasible energy and GHG curves are determined using general energy and GHG contour plots as well as cradle-to-gate energy consumption and GHG emissions of conventional processes, respectively. The 2D-feasible space of biobased adipic acid, succinic acid, isobutanol, 1,3-propanediol, and 3-HPA production processes are defined by graphing feasible cost, energy, and GHG curves along with yield, titer, and productivity constraints (Figure 3). The theoretical yields of biobased commodity chemicals are used to determine the yield constraints (Table 1). Titer of 200 g/L and productivity of 2 g/L·h are assumed as titer and volumetric productivity constraints, respectively. We have provided justification for the selected limits on titer and productivity in the supplement materials. The bounded space between feasible energy or GHG curve and constraints of yield and titer is defined as the environmental feasible space (Figure 3). Similarly, feasible cost space is defined as a space between the feasible cost curve and constraints of yield and titer (Figure 8124

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ACS Sustainable Chemistry & Engineering 3). For the selected constraint values, the feasible space is not found for the production of 1,3-propanediol via aerobic cultivation. This is because the requirement of high capital and operating costs for the aerobic cultivation process. The comparison of feasible spaces of processes for the production of biobased commodity chemicals shows that a biocatalyst must exhibit titers of at least 45 g/L (Figure 3). If the toxicity of a biobased commodity chemical to biocatalyst limits the concentration of a chemical in the fermentation systems to lower than 45 g/L, the additional investments to increase the yield and product rates of the biocatalyst will become unviable (Figure 3). In such cases, the investments should be diverted to develop an alternate biocatalyst to make the biobased commodity chemical. Similarly, a biocatalyst development team must achieve biobased commodity chemical yields of at least 0.32 g/g (Figure 3). To avoid potential losses, R&D investments to develop biocatalysts should be avoided when theoretical yields of biobased commodity chemicals are less than 0.32 g/g. These values of yield and titer can be used as general guidelines in the development of biocatalytic technologies. The comparison of feasible spaces of processes for the production of biobased commodity chemicals shows that the feasible cost curve is always above the feasible GHG and energy curves (Figure 3). What that means is feasible environmental space is always larger than the feasible cost space. This finding indicates that if a biocommodity chemical production process is found to be economically feasible, then the environmental performance of the biobased commodity chemical route will be equal or better than that of conventional petrochemical route. Feedstock is the dominant cost in any commercially viable biobased commodity chemical production system. To test this hypothesis, the MSP of biobased commodity chemicals estimated at the yield of 0.4 g/g (0.36 g/g for isobutanol), volumetric productivity of 2 g/L·h, and titer of 150 g/L is segmented into individual cost components. As shown in Figure 3, it is commercially viable to make a biocommodity chemical for these process parameter values. It has been found from the comparison of individual cost components, that the feedstock cost dominates the MSP of a biobased commodity chemical by >45% (data is not shown). In this study, we analyzed economic and environmental potential of multiple biobased commodity chemical processes. The general cost, energy, and GHG contour plots that are determined in this study can be used to quickly assess economic and environmental viability of early stage biocatalyst based commodity chemical technologies. The use of these contour plots, therefore, avoids missing opportunities for investments in the development of potential biocatalytic technologies. In addition, process performance targets can be set for use by technology development teams using these general contour plots and the general rules found from this study. Using these targets and guidelines, firms can be more effective in research and development of emerging biocatalytic technologies to produce biobased commodity chemicals.

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Detailed PFDs, modeling assumptions, microbial production pathways, operating cost assumptions, microaerobic reactor cost, DDGS price sensitivity, and additional information about results (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Sampath Gunukula: 0000-0001-7297-3993 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Foundation [grant no. EEC-0813570/1158833]. REFERENCES

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DOI: 10.1021/acssuschemeng.7b01729 ACS Sustainable Chem. Eng. 2017, 5, 8119−8126