(428a) Bioprocessing of Switchgrass to Ethanol Utilizing Thermostable Cellulases

Switchgrass (Panicum virgatum), a perennial grass native to North America, is a promising energy crop for bioethanol production. The aim of this study was to optimize the enzymatic saccharification of thermo-mechanically pretreated switchgrass using a thermostable cellulase from Geobacillus sp. in a three-level, four-variable central composite design of response surface methodology (RSM). Different combinations of solids loadings (5 to 20%), enzyme loadings (5 to 20 FPU g^-1 DM), temperature (50 to 70 ^oC), and time (36 to 96 h) were investigated in a total of 30 experiments to model glucose release from switchgrass. All four factors had a significant impact on the cellulose conversion yields with a high coefficient of determination of 0.96. The use of higher solids loadings (20%) and temperatures (70 ^oC) during enzymatic hydrolysis proved beneficial for the significant reduction of hydrolysis times (2.67-times) and enzyme loadings (4-times), with important implications for reduced capital and operating costs of ethanol production. At 20% solids, the increase of temperature of enzymatic hydrolysis from 50 ^oC to 70 ^oC increased glucose concentrations by 34%. The attained maximum glucose concentration of 23.52 g L^-1 translates into a glucose recovery efficiency of 46% from the theoretical yield. Following red yeast fermentation, a maximum ethanol concentration of 11 g L^-1 was obtained, accounting for a high glucose to ethanol fermentation efficiency of 92%. The overall conversion efficiency of switchgrass to ethanol was 42%. High solids bioprocessing of switchgrass to ethanol at elevated temperatures could bring about significant savings of capital and operating costs, as it reduces the amount of enzyme and hydrolysis time needed, size of reaction vessels, water usage, and waste water treatment costs. The use of RSM for optimization of process parameters saves both time and costs, maximizing the amount of information that can be obtained while limiting the number of individual experiments. Furthermore, the RSM predicts the interaction between the independent variables, which results in improved accuracy and precision of the research data and facilitates their interpretation.