Analysis of Pretreated Biomass

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.

Prairie cordgrass (PCG), Spartina pectinata, is considered an energy crop with potential for bioethanol production in North America. The focus of this study was to optimize enzymatic hydrolysis of PCG at higher solids loadings using a thermostable cellulase of a mutant Penicillium pinophilum ATCC 200401. A three variable, five-level central composite design of response surface methodology (RSM) 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 dry matter, DM) on glucose yield from a thermo-mechanically extruded PCG. The extruded PCG was first hydrolyzed with the crude P. pinophilum cellulase 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 showed a coefficient of determination (R2) of 0.89. At the optimal conditions of pH of 4.5, solids loadings of 10% and enzyme loadings of 20 FPU/g DM, the enzymatic hydrolysis of pretreated PCG produced a glucose yield of 76.1% from the maximum yield which represents an increase of 15% over the non-optimized controls at the 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. Furthermore, statistical optimization of solids and enzyme loadings of enzymatic hydrolysis of biomass may have important implications for reduced capital and operating costs of ethanol production.