Quantitative Analysis for Biomass Energy Problem Using a Radial Basis Function Neural Network

Abstract

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article,a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be comparedwith a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionalityof data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principalcomponents for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research,3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ashand char are used as response variables. The comparison results of two responses will be shown.