Prediction of pyrite oxidation in a coal washing waste pile using a hybrid method, coupling artificial neural networks and simulated annealing (ANN/SA)

Abstract

This paper presents a novel hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA to predict the fraction of pyrite remaining and therefore the pyrite oxidation rate in the wastes at different depths of a coal washing pile in the Alborz Markazi Coalfield, in northeast Iran. Waste depth, oxygen mole fraction and initial pyrite content in the waste particles were used as inputs to the network. The output of the network was the amount of pyrite content remaining. An ANN/SA model with Levenberg-Marquardt algorithm and a 3-4-3-1 arrangement showed a great capability. The network was used to predict the pyrite content remaining at two trenches E and F over the study waste pile once it was trained with the field-measured data. Simulated results obtained by the ANN/SA model were very closer to the experimental data compared to the outputs of simple ANN and multivariable least squares regression methods. The correlation coefficient (R) value, by the ANN/SA model, was 0.999 for training set, and in testing stage it was 0.998 and 0.99957 for trench E and trench F respectively which shows the model prediction was quite satisfactory. The performance of the model on the training and testing data, mean squared error (MSE) and mean absolute percent error (MAPE), indicate that it has both good predictive ability and generalisation performance.