Evaluation Of The Effect Of Coal Chemical Properties On The Hardgrove Grindability Index (HGI) Of Coal Using Artificial Neural Networks

In this investigation, the effects of different coal chemical properties were studied to estimate the coal Hardgrove Grindability Index (HGI) values index. An artificial neural network (ANN) method for 300 data-sets was used for evaluating the HGI values. Ten input parameters were used, and the outputs of the models were compared in order to select the best model for this study. A threelayer ANN was found to be optimum with architecture of five neurons in each of the first and second hidden layers, and one neuron in the output layer. The correlation coefficients (R2) for the training and test data were 0.962 and 0.82 respectively. Sensitivity analysis showed that volatile material, carbon, hydrogen, Btu, nitrogen, and fixed carbon (all on a dry basis) have the greatest effect on HGI, and moisture, oxygen (dry), ash (dry), and total sulphur (dry) the least effect. Keywords coal chemical properties, Hardgrove Grindability Index, artificial neural network, back-propagation neural network.