Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization

Abstract

Torrefaction is an effective technology to overcome the defects of biomass which are adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential to characterize the properties of torrefied biomass. However, the preparation and characterization of torrefied biomass often consume a lot of time, costs, and manpower. Developing a reliable method to predict the fuel properties of torrefied biomass while avoiding various experiments and tests is of great value. In this study, a machine learning (ML) model of back propagation neural network (BPNN) hybridized with genetic algorithm (GA) optimization was developed to predict the important properties of torrefied biomass for the fuel purpose involving fuel ratio (FR), H/C and O/C ratios, high heating value (HHV) and the mass and energy yields (MY and EY) based on the proximate analysis results of raw biomass and torrefaction conditions. R2 and RMSE were examined to evaluate the prediction precision of the model. The results showed that the GA-BPNN model exhibited excellent accuracy in predicting all properties with the values of R2 higher than 0.91 and RMSE less than 1.1879. Notably, the GA-BPNN model is applicable to any type of biomass feedstock, whether it was dried or not before torrefaction. This study filled the gap of ML application in predicting the multiple fuel properties of torrefied biomass. The results could provide reference to torrefaction technology as well as the design of torrefaction facilities.