Study on HHV prediction of municipal solid wastes: A machine learning approach

Abstract

In this work, artificial neural network (ANN) models and particle swarm optimization (PSO) models based on machine learning were built to predict the HHVs of MSW quickly. Four kinds of BP ANN models and two PSO models were built using proximate analysis and ultimate analysis of 33 MSW samples as input variables. As a comparison, three classical linear empirical models employed from publications were also used. The modeling results show that the input variables had significant influence on the prediction accuracy. The ANN model based on proximate analysis had lower precision for HHV estimation. The ultimate analysis had better prediction performance while the combination of ultimate analysis and proximate analysis (ANN-4 model) had the best accuracy. With regard to ANN-4 model, the largest relative deviation for all samples was lower than 10%. Due to the complex composition of MSW, the linear empirical model was not suitable for accurate prediction of the calorific value of MSW. Nonlinear empirical formulas obtained by PSO models improved the prediction performance for most samples. In general, the ANN modeling method could predict the thermochemical properties of MSW and provide rapid and effective guidance for the operation of MSW incineration process.