Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction

Abstract

A random forest (RF) model integrated with feature reduction was implemented to predict the properties of torrefied biomass based on feedstock and torrefaction conditions. Four features were selected for the prediction of fuel ratio (FR) and nitrogen content (Nt), and five features were selected for O/C and H/C ratios and HHV values. The results showed that the feature-reduced model had excellent prediction performance with the values of R2 higher than 0.93 and RMSE less than 0.58 for all targets. Moreover, partial dependence analysis (PDA) was performed to quantify the impacts of selected features and torrefaction conditions on the targets. Temperature was the dominant factor for FR, O/C and H/C ratios, and HHV values, whereas Nt was determined most on the nitrogen content in the feedstock (Ni). This study provided comprehensive information for understanding biomass torrefaction.