Application of artificial neural networks to predict chemical desulfurization of Tabas coal

This paper presents a neural network model to predict the effects of operational parameters on the organic and inorganic sulfur removal from coal by sodium butoxide. The coal particle size, leaching temperature and time, sodium butoxide concentration and pre oxidation time by peroxyacetic acid (PAA) were used as inputs to the network. The outputs of the models were organic and inorganic sulfur reduction. Feed-forward artificial neural network with 5-7-10-1 arrangement, were capable to estimate organic and inorganic sulfur reduction, respectively. Simulated values obtained with neural network correspond closely to the experimental results. It was achieved quite satisfactory correlations of R2 = 1 and 0.96 in training and testing stages for pyritic sulfur and R2 = 1 and 0.97 in training and testing stages, respectively, for organic sulfur reduction prediction. The proposed neural network model accurately reproduces all the effects of operational variables and can be used in the simulation of Tabas coal desulfurization plant.