Fundamental study of structural features affecting enzymatic hydrolysis of lignocellulosic biomass

Lignocellulose is a promising and valuable alternative energy source. Native lignocellulosic biomass has limited accessibility to cellulase enzyme due to structural features; therefore, pretreatment is an essential prerequisite to make biomass accessible and reactive by altering its structural features. The effects of substrate concentration, addition of cellobiase, enzyme loading, and structural features on biomass digestibility were explored. The addition of supplemental cellobiase to the enzyme complex greatly increased the initial rate and ultimate extent of biomass hydrolysis by converting the strong inhibitor, cellobiose, to glucose. A low substrate concentration (10 g/L) was employed to prevent end-product inhibition by cellobiose and glucose. The rate and extent of biomass hydrolysis significantly depend on enzyme loading and structural features resulting from pretreatment, thus the hydrolysis and pretreatment processes are intimately coupled because of structural features. Model lignocelluloses with various structural features were hydrolyzed with a variety of cellulase loadings for 1, 6, and 72 h. Glucan, xylan, and total sugar conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the simplified HCH-1 model is valid for predicting lignocellulose digestibility. Carbohydrate conversions at a given time versus the natural logarithm of cellulase loadings were plotted to obtain the slopes and intercepts which were correlated to structural features (lignin content, acetyl content, cellulose crystallinity, and carbohydrate content) by both parametric and nonparametric regression models. The predictive ability of the models was evaluated by a variety of biomass (corn stover, bagasse, and rice straw) treated with lime, dilute acid, ammonia fiber explosion (AFEX), and aqueous ammonia. The measured slopes, intercepts, and carbohydrate conversions at 1, 6, and 72 h were compared to the values predicted by the parametric and nonparametric models. The smaller mean square error (MSE) in the parametric models indicates more satisfactorily predictive ability than the nonparametric models. The agreement between the measured and predicted values shows that lignin content, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility, and that biomass digestibility can be predicted over a wide range of cellulase loadings using the simplified HCH-1 model