Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks. Issue 11 (2nd June 2016) - Li, Hao; Yang, Shuangjun; Zhao, Weiqi; Xu, Zhihan; Zhao, Shiyu; Liu, Xifeng
ABSTRACT: This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.