Predicting the Biomethanation Potential of Some Lignocellulosic Feedstocks using Linear Regression Models: The Effect of Pretreatment

Abstract

Lignocellulose is an important feedstock for bioenergy production via an anaerobic digestion pathway. To evaluate the biochemical methanation potential (BMP) of each biomass sample, linear regression equations have been developed. Herein the application of the linear correlation of cellulosic compositions and ultimate BMP to simplify a mathematic equation for further prediction was investigated. The study focused on the effect of a pretreatment operation on the prediction. The model hypothesized that the pretreatment process would change the ratio between easily digestible fraction (NDS), cellulose (Cel) and lignin (ADL), which changes methane production. The prediction equations provided R 2 = 0.5897 and for non-pretreated biomass and BMP for biomass after pretreatment provided R 2 = 0.7450. These showed clearly that NDS and Cel provided positive methane production and ADL was a retarding factor. Via the pretreatment process, the coefficient of NDS increased and during ADL decreased significantly, the calibrated coefficient can still be applied (p-value < 0.05). This equation is applicable for methane production from lignocellulosic biomass feedstock where shoreter time and lower cost for methane estimation in BMP assays will be promoted.