Uncertainty in techno-economic estimates of cellulosic ethanol production due to experimental measurement uncertainty

Biotechnology for Biofuels

Kristin J Vicari²  Sai Sandeep Tallam²  Tatyana Shatova²  Koh Kang Joo²  Christopher J Scarlata³  David Humbird³  Edward J Wolfrum³  Gregg T Beckham¹ 

[1] Department of Chemical Engineering, Colorado School of Mines, Golden, CO, USA

[2] Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

[3] National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO, USA

关键词: Techno-economic modeling;    Biochemical conversion;    Process modeling;    Fermentation;    Enzymatic hydrolysis;    Pretreatment;   

Others  :  798315
DOI  :  10.1186/1754-6834-5-23

 received in 2012-01-12, accepted in 2012-03-29,  发布年份 2012

【 摘 要 】

Background

Cost-effective production of lignocellulosic biofuels remains a major financial and technical challenge at the industrial scale. A critical tool in biofuels process development is the techno-economic (TE) model, which calculates biofuel production costs using a process model and an economic model. The process model solves mass and energy balances for each unit, and the economic model estimates capital and operating costs from the process model based on economic assumptions. The process model inputs include experimental data on the feedstock composition and intermediate product yields for each unit. These experimental yield data are calculated from primary measurements. Uncertainty in these primary measurements is propagated to the calculated yields, to the process model, and ultimately to the economic model. Thus, outputs of the TE model have a minimum uncertainty associated with the uncertainty in the primary measurements.

Results

We calculate the uncertainty in the Minimum Ethanol Selling Price (MESP) estimate for lignocellulosic ethanol production via a biochemical conversion process: dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis and co-fermentation of the resulting sugars to ethanol. We perform a sensitivity analysis on the TE model and identify the feedstock composition and conversion yields from three unit operations (xylose from pretreatment, glucose from enzymatic hydrolysis, and ethanol from fermentation) as the most important variables. The uncertainty in the pretreatment xylose yield arises from multiple measurements, whereas the glucose and ethanol yields from enzymatic hydrolysis and fermentation, respectively, are dominated by a single measurement: the fraction of insoluble solids (f_IS) in the biomass slurries.

Conclusions

We calculate a $0.15/gal uncertainty in MESP from the TE model due to uncertainties in primary measurements. This result sets a lower bound on the error bars of the TE model predictions. This analysis highlights the primary measurements that merit further development to reduce the uncertainty associated with their use in TE models. While we develop and apply this mathematical framework to a specific biorefinery scenario here, this analysis can be readily adapted to other types of biorefining processes and provides a general framework for propagating uncertainty due to analytical measurements through a TE model.

【 授权许可】

   
2012 Vicari et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

【 参考文献 】

• [1]Aden A, et al.: Lignocellulosic biomass to ethanol process design and economics utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis for corn stover. 2002, -NREL, Editor.
• [2]Aden A, Foust T: Technoeconomic analysis of the dilute sulfuric acid and enzymatic hydrolysis process for the conversion of corn stover to ethanol. Cellulose 2009, 16(4):535-45.
• [3]Himmel ME, et al.: Biomass recalcitrance: Engineering plants and enzymes for biofuels production. Science 2007, 315(5813):804-7.
• [4]Lynd LR, et al.: How biotech can transform biofuels. Nat Biotechnol 2008, 26(2):169-72.
• [5]Laser M, et al.: Comparative analysis of efficiency, environmental impact, and process economics for mature biomass refining scenarios. Biofuels Bioproduct Biorefining-Biofpr 2009, 3(2):247-70.
• [6]Lynd LR, et al.: Consolidated bioprocessing of cellulosic biomass: an update. Curr Opin Biotechnol 2005, 16(5):577-83.
• [7]Lynd LR, et al.: Microbial cellulose utilization: fundamentals and biotechnology. Microb Molec Bio Rev 2002, 66(3):506-77.
• [8]Foust TD, et al.: An economic and environmental comparison of a biochemical and a thermochemical lignocellulosic ethanol conversion processes. Cellulose 2009, 16(4):547-65.
• [9]Tao L, Aden A: The economics of current and future biofuels. In Vitro Cell Dev Biology-Plant 2009, 45(3):199-217.
• [10]Jones SB, et al.: Production of gasoline and diesel from biomass via fast pyrolysis, hydrotreating and hydrocracking: A design case. 2009, -N. PNNL, Editor.
• [11]Anex RP, et al.: Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, gasification, and biochemical pathways. 2010, -Fuel.
• [12]Wright MM, et al.: Techno-economic analysis of biomass fast pyrolysis to transportation fuels. 2010, -Fuel.
• [13]Phillips S, et al.: Thermochemical ethanol via indirect gasification and mixed alcohol synthesis of lignocellulosic biomass. 2007, -NREL, Editor.
• [14]Larson ED, Jin H, Celik FE: Large-scale gasifi cation-based coproduction of fuels and electricity from switchgrass. Biofuels Bioproducts Biorefining-Biofpr 2008, 3:174-94.
• [15]Kunkes EL, et al.: Catalytic conversion of biomass to monofunctional hydrocarbons and targeted liquid-fuel classes. Science 2008, 322(5900):417-21.
• [16]West RM, et al.: Catalytic conversion of biomass-derived carbohydrates to fuels and chemicals by formation and upgrading of mono-functional hydrocarbon intermediates. Catal Today 2009, 147(2):115-25.
• [17]Steen EJ, et al.: Microbial production of fatty-acid-derived fuels and chemicals from plant biomass. Nature 2010, 463(7280):559-U182.
• [18]Atsumi S, Hanai T, Liao JC: Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 2008, 451(7174):86-U13.
• [19]Atsumi S, et al.: Metabolic engineering of Escherichia coli for 1-butanol production. Metab Eng 2008, 10(6):305-11.
• [20]Swatloski RP, et al.: Dissolution of cellose with ionic liquids. J Am Chem Soc 2002, 124(18):4974-5.
• [21]Li CL, et al.: Comparison of dilute acid and ionic liquid pretreatment of switchgrass: Biomass recalcitrance, delignification and enzymatic saccharification. Bioresour Technol 2010, 101(13):4900-4906.
• [22]Singh S, Simmons BA, Vogel KP: Visualization of biomass solubilization and cellulose regeneration during ionic liquid pretreatment of Switchgrass. Biotechnol Bioeng 2009, 104(1):68-75.
• [23]Ramakrishnan S, et al.: Enzymatic hydrolysis of cellulose dissolved in N-methyl morpholine oxide/water solutions. Bioresour Technol 2010, 101(13):4965-4970.
• [24]Elander RT, et al.: Summary of findings from the Biomass Refining Consortium for Applied Fundamentals and Innovation (CAFI): corn stover pretreatment. Cellulose 2009, 16(4):649-59.
• [25]Mosier N, et al.: Features of promising technologies for pretreatment of lignocellulosic biomass. Biores Tech 2005, 96:673-86.
• [26]Wyman CE, et al.: Coordinated development of leading biomass pretreatment technologies. Bioresour Technol 2005, 96(18):1959-66.
• [27]Mosier NS, et al.: Industrial scale-up of pH-controlled liquid hot water pretreatment of corn fiber for fuel ethanol production. Appl Biochem Biotechnol 2005, 125(2):77-97.
• [28]Lau MW, Dale BE: Cellulosic ethanol production from AFEX-treated corn stover using Saccharomyces cerevisiae 424A(LNH-ST). Proc Natl Acad Sci U S A 2009, 106(5):1368-73.
• [29]Dale BE, et al.: Hydrolysis of lignocellulosics at low enzyme levels: Application of the AFEX process. Bioresour Technol 1996, 56(1):111-6.
• [30]Kim S, Holtzapple MT: Lime pretreatment and enzymatic hydrolysis of corn stover. Bioresour Technol 2005, 96(18):1994-2006.
• [31]Lorenz AJ, et al.: Characterization, genetic variation, and combining ability of maize traits relevant to the production of cellulosic ethanol. Crop Sci 2009, 49(1):85-98.
• [32]Templeton DW, et al.: Assessing corn stover composition and sources of variability via NIRS. Cellulose 2009, 16(4):621-39.
• [33]Sluiter JB, et al.: Compositional Analysis of Lignocellulosic Feedstocks. 1. Review and Description of Methods. J Agric Food Chem 2010, 58(16):9043-53.
• [34]Templeton DW, et al.: Compositional Analysis of Lignocellulosic Feedstocks. 2. Method Uncertainties. J Agric Food Chem 2010, 58(16):9054-62.
• [35]Nguyen QA, et al.: NREL/DOE ethanol pilot-plant: Current status and capabilities. Bioresour Technol 1996, 58(2):189-96.
• [36]Schell DJ, et al.: Contaminant occurrence, identification and control in a pilot-scale corn fiber to ethanol conversion process. Bioresour Technol 2007, 98(15):2942-8.
• [37]Schell DJ, et al.: Dilute-sulfuric acid pretreatment of corn stover in pilot-scale reactor - Investigation of yields, kinetics, and enzymatic digestibilities of solids. Appl Biochem Biotechnol 2003, 105:69-85.
• [38]Schell DJ, et al.: A bioethanol process development unit: initial operating experiences and results with a corn fiber feedstock. Bioresour Technol 2004, 91(2):179-88.
• [39]Humbird D, et al.: Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover. 2011, -NREL.
• [40]Weiss ND, et al.: A Simplified Method for the Measurement of Insoluble Solids in Pretreated Biomass Slurries. Appl Biochem Biotechnol 2010, 162(4):975-87.
• [41]Maroto A, et al.: Evaluating uncertainty in routine analysis. Trends Anal Chem 1999, 18(9–10):577-84.