Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm

Abstract

A support vector machine (SVM) model with RBF kernel function combined with sparrow search algorithm (SSA) optimization was developed to predict the HHV and nitrogen content (No) values of torrefied biomass based on the feedstock properties and torrefaction conditions. Results showed that SSA optimization significantly improved the prediction performance of the SVM model for both HHV and No. A coefficient of determination (R²) larger than 0.91 was achieved when the SSA-SVM model was implemented, and the values of RMSE were also fairly acceptable. The agreement between experimental data and SSA-SVM predicted values demonstrated the high predictive precision of the model. This study provides a reference for the utilization of torrefied biomass in solid fuels and the design of torrefaction facilities.