On the estimation of higher heating value of municipal wastes using soft computing approaches

High values of higher heating value (HHV) for municipal solid wastes (MSWs) turn them into promising sources of energy nowadays. Accurate prediction of HHV values is investigated using intelligent algorithms of multilayer perceptron artificial neural network (MLP-ANN) and least squares support vector machine (LSSVM). The proposed methods will give the HHV values as a function of different chemical species' mass percentages. These models compared with previously reported correlations. Better performance achieved for the proposed MLP-ANN and LSSVM regarding mean squared error (MSE) values of 0.2325 and 0.000186. Models considered as reliable predictive tools to estimate the HHV values of solid wastes.