Optimization of enzymatic hydrolysis of corn stover for improved ethanol production

Response surface methodology (RSM) was used to optimize the enzymatic hydrolysis of corn stover (CS), an abundant agricultural residue in the USA. A five-level, three-variable central composite design (CCD) was employed in a total of 20 experiments to model and evaluate the impact of pH (4.1–6.0), s...

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Bibliographic Details
Published in:Energy exploration & exploitation 2012-04, Vol.30 (2), p.193-205
Main Authors: Zambare, Vasudeo P., Christopher, Lew P.
Format: Article
Language:English
Subjects:
Alcohols: methanol, ethanol, etc
Alternative fuels. Production and utilization
Analysis of variance
Applied sciences
Biochemistry
Biofuels
Charge coupled devices
Corn stover
Energy
Enzymatic hydrolysis
Enzymes
Ethanol
Ethanol production
Ethyl alcohol
Exact sciences and technology
Fermentation
Fuels
Glucose
Hydrolysis
Mathematical models
Pretreatment
Saccharification
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Summary:Response surface methodology (RSM) was used to optimize the enzymatic hydrolysis of corn stover (CS), an abundant agricultural residue in the USA. A five-level, three-variable central composite design (CCD) was employed in a total of 20 experiments to model and evaluate the impact of pH (4.1–6.0), solids loadings (6.6–23.4%), and enzyme loadings (6.6–23.4 FPU g−1 DM) on glucose yield from thermo-mechanically extruded CS. The extruded CS was first hydrolyzed with the crude cellulase of Penicillium pinophilum ATCC 200401 and then fermented to ethanol with Saccharomyces cerevisiae ATCC 24860. Although all three variables had a significant impact, the enzyme loadings proved the most significant parameter for maximizing the glucose yield. A partial cubic equation could accurately model the response surface of enzymatic hydrolysis as the analysis of variance (ANOVA) showed a coefficient of determination (R2) of 0.82. At the optimal conditions of pH of 4.5, solids loadings of 10% and enzyme loadings of 20 FPU g−1 DM, the enzymatic hydrolysis of pretreated CS produced a glucose yield of 57.6% of the glucose maximum yield which was an increase of 10.4% over the non-optimized controls at zero-level central points. The predicted results based on the RSM regression model were in good agreement with the actual experimental values. The model can present a rapid means for estimating lignocellulose conversion yields within the selected ranges.
ISSN:0144-5987
2048-4054
DOI:10.1260/0144-5987.30.2.193