Predicting calorific value through proximate analysis of municipal solid waste using soft computing system

This study investigated the accurate prediction of the calorific value of municipal solid waste (MSW) using soft computing systems, namely artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFIS), support vector machine (SVM), and multi-layer perceptron (MLP). Calorific value of MSW is a crucial factor that exhibits the energy content of MSW, however, determining calorific value from conventional laboratory methods is quite expensive and difficult. The research focused on proximate analysis parameters obtained from the laboratory and utilized the measured calorific value to develop predictive models. All the models demonstrated a very good correlation between input and output, with consistently strong values of the coefficient of determination (R2). ANN SVM, MLP, and ANFIS models have respective R2 values of 0.9397, 0.8195, 0.7212, and 0.9979. ANFIS showed the best correlation with exceptional predictive power. Statistical parameters were determined to compare model accuracy, with ANFIS exhibiting the top performance, followed by ANN, and then MLP, which had the lowest values of mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) at 8.704E-07, 0.00019, 0.00016, and 1.295E-05 respectively. However, SVM has the least capability to predict calorific values accurately compared to other models. Soft computing Models, particularly ANFIS, demonstrated remarkable accuracy in predicting the calorific value.