Statistical clustering of biomass to predict biogas yields

Abstract

A priori prediction of biogas yields from the biomass composition is difficult owing to the inter and intra variation in the biomass composition. A statistical clustering based on the composition of the biomass has been attempted in this study. Twenty-five biomass feedstocks were clustered into three groups; group 1, rich in extractives; group 2, having a high concentration of holocellulose and group 3, predominantly lignin-rich feedstock. Based on the clustering, two multiple linear regression equations were obtained. The correlations had an R2 > 0.95, showing a good statistical fit. Concentrations of lignin and extractives (L/E) play an important role in the overall biogas yields. Segregation of biomass based on L/E can be used to differentiate feedstocks for efficient biogas production.