Estimation of calorific value using an artificial neural network based on stochastic ultimate analysis

Abstract

The main aim of the present study was to estimate the calorific value (CV) by considering the uncertainty in municipal solid waste (MSW) generation using a cohesive Artificial Neural Network (ANN) technique. The parameters from ultimate analysis are taken as input data and CV as output data for 300 samples and seasonal variation is considered for predicting CV. Monte Carlo Simulation Technique is applied to deal with the uncertainty in MSW characteristics and the Simultaneous Backward Reduction (SBR) approach is used to tackle scenarios into a manageable number. Various ANN models were built based on the seasonal variation for three cities: ANNMS, ANNMW, ANNUS, ANNUW, ANNBS and ANNBW. The results depicted that network ANNMS for the architecture (9-21-1) has a minimum MAPE value of 1.22% with an estimation accuracy of 94.61%. The network ANNUS has a minimum MAPE of 1.62% has an estimation accuracy of 93.2% for the architecture (9-23-1). The network ANNBW Baddi City has a minimum MAPE is 2.23% for the network (9-21-1) with an estimation accuracy of 91.77% respectively. The developed ANNs model in the present study is beneficial to estimate the CV of MSW samples using elemental composition considering the uncertainty in the data, leading to more accuracy in the prediction models. It helps to minimize experimental costs and gives a theoretical background for modeling MSW's energy potential based on Waste to Energy (WTE) technologies.