Predicting the Heating Value of Municipal Solid Waste-based Materials: An Artificial Neural Network Model

Abstract

Municipal solid waste has been recently considered as one of the important renewable energy resources. For municipal solid waste-based energy systems, municipal solid waste heating value is a critical variable. In this study, an artificial neural network model based on Levenberg-Marquardt backpropagation learning algorithm has been developed to predict higher heating value of municipal solid waste using their contents of water, carbon, hydrogen, nitrogen, oxygen, sulfur, and ash. Artificial neural network model has been able to predict the higher heating values of municipal solid waste with a tangent sigmoid transfer function at hidden layer with five neurons and a linear transfer function at output layer. Mean-squared error of prediction results in the validation process has been found to be 0.0137. The regression between network outputs and corresponding targets has been showed to be acceptable with a high correlation coefficient of 0.991. It can be deduced that the developed model is a robust estimation tool for higher heating value of municipal solid waste-based materials.