Dataset Information

Impact of pretreatment and downstream processing technologies on economics and energy in cellulosic ethanol production.

ABSTRACT:

Background

While advantages of biofuel have been widely reported, studies also highlight the challenges in large scale production of biofuel. Cost of ethanol and process energy use in cellulosic ethanol plants are dependent on technologies used for conversion of feedstock. Process modeling can aid in identifying techno-economic bottlenecks in a production process. A comprehensive techno-economic analysis was performed for conversion of cellulosic feedstock to ethanol using some of the common pretreatment technologies: dilute acid, dilute alkali, hot water and steam explosion. Detailed process models incorporating feedstock handling, pretreatment, simultaneous saccharification and co-fermentation, ethanol recovery and downstream processing were developed using SuperPro Designer. Tall Fescue (Festuca arundinacea Schreb) was used as a model feedstock.

Results

Projected ethanol yields were 252.62, 255.80, 255.27 and 230.23 L/dry metric ton biomass for conversion process using dilute acid, dilute alkali, hot water and steam explosion pretreatment technologies respectively. Price of feedstock and cellulose enzymes were assumed as $50/metric ton and 0.517/kg broth (10% protein in broth, 600 FPU/g protein) respectively. Capital cost of ethanol plants processing 250,000 metric tons of feedstock/year was $1.92, $1.73, $1.72 and $1.70/L ethanol for process using dilute acid, dilute alkali, hot water and steam explosion pretreatment respectively. Ethanol production cost of $0.83, $0.88, $0.81 and $0.85/L ethanol was estimated for production process using dilute acid, dilute alkali, hot water and steam explosion pretreatment respectively. Water use in the production process using dilute acid, dilute alkali, hot water and steam explosion pretreatment was estimated 5.96, 6.07, 5.84 and 4.36 kg/L ethanol respectively.

Conclusions

Ethanol price and energy use were highly dependent on process conditions used in the ethanol production plant. Potential for significant ethanol cost reductions exist in increasing pentose fermentation efficiency and reducing biomass and enzyme costs. The results demonstrated the importance of addressing the tradeoffs in capital costs, pretreatment and downstream processing technologies.

SUBMITTER: Kumar D

PROVIDER: S-EPMC3179443 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Publications

Impact of pretreatment and downstream processing technologies on economics and energy in cellulosic ethanol production.

Kumar Deepak D Murthy Ganti S GS

Biotechnology for biofuels 20110905

<h4>Background</h4>While advantages of biofuel have been widely reported, studies also highlight the challenges in large scale production of biofuel. Cost of ethanol and process energy use in cellulosic ethanol plants are dependent on technologies used for conversion of feedstock. Process modeling can aid in identifying techno-economic bottlenecks in a production process. A comprehensive techno-economic analysis was performed for conversion of cellulosic feedstock to ethanol using some of the co ...[more]

PMID: 21892958

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Project description:BackgroundAs one of the clean and sustainable energies, lignocellulosic ethanol has achieved much attention around the world. The production of lignocellulosic ethanol does not compete with people for food, while the consumption of ethanol could contribute to the carbon dioxide emission reduction. However, the simultaneous transformation of glucose and xylose to ethanol is one of the key technologies for attaining cost-efficient lignocellulosic ethanol production at an industrial scale. Genetic modification of strains and constructing consortia were two approaches to resolve this issue. Compared with strain improvement, the synergistic interaction of consortia in metabolic pathways should be more useful than using each one separately.ResultsIn this study, the consortia consisting of suspended Scheffersomyces stipitis CICC1960 and Zymomonas mobilis 8b were cultivated to successfully depress carbon catabolite repression (CCR) in artificially simulated 80G40XRM. With this strategy, a 5.52% more xylose consumption and a 6.52% higher ethanol titer were achieved by the consortium, in which the inoculation ratio between S. stipitis and Z. mobilis was 1:3, compared with the Z. mobilis 8b mono-fermentation. Subsequently, one copy of the xylose metabolic genes was inserted into the Z. mobilis 8b genome to construct Z. mobilis FR2, leading to the xylose final-consumption amount and ethanol titer improvement by 15.36% and 6.81%, respectively. Finally, various corn stover hydrolysates with different sugar concentrations (glucose and xylose 60, 90, 120 g/L), were used to evaluate the fermentation performance of the consortium consisting of S. stipitis CICC1960 and Z. mobilis FR2. Fermentation results showed that a 1.56-4.59% higher ethanol titer was achieved by the consortium compared with the Z. mobilis FR2 mono-fermentation, and a 46.12-102.14% higher ethanol titer was observed in the consortium fermentation when compared with the S. stipitis CICC1960 mono-fermentation. Furthermore, qRT-PCR analysis of xylose/glucose transporter and other genes responsible for CCR explained the reason why the initial ratio inoculation of 1:3 in artificially simulated 80G40XRM had the best fermentation performance in the consortium.ConclusionsThe fermentation strategy used in this study, i.e., using a genetically modified consortium, had a superior performance in ethanol production, as compared with the S. stipitis CICC1960 mono-fermentation and the Z. mobilis FR2 mono-fermentation alone. This result showed that this strategy has potential for future lignocellulosic ethanol production.

Project description:UnlabelledBackgroundCost-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.ResultsWe 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 (fIS) in the biomass slurries.ConclusionsWe 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.

Dataset Information

Impact of pretreatment and downstream processing technologies on economics and energy in cellulosic ethanol production.

Background

Results

Conclusions

Publications

Impact of pretreatment and downstream processing technologies on economics and energy in cellulosic ethanol production.

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