Formulating a novel combined equation for coal calorific value estimation by group method data handling type neural network

Abstract

The accurate estimation of the calorific value of coal with the help of models is very important for the design and operation of coal-based technologies. This study proposes a new combined estimation equation for the calorific value of coal. The modeling process in this study consists of two stages. In Stage-1, a high-performance gross calorific value (GCV) prediction equation is developed using the Group Method Data Handling type Neural Network (GMDH-type NN) method based on proximate analysis components. In addition, by comparing the performance of the developed GCV equation with the performance of the model equations in the literature, three model equations providing the best estimation performance are determined. In Stage-2, a combined equation for GCV prediction is formed based on GMDH-type NN. This combined equation uses the estimation results of the best three equations as model inputs, determined in Stage-1. A large dataset consisting of 8501 coal samples (as-received basis) is used in the development and testing of GCV estimation equations. According to the results obtained from estimation equations, the estimation equation developed in Stage-1 provides better estimation performance than the equations in the literature. It is found that the combined equation developed in Stage-2 improves considerably the prediction performance values.