Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks
In this research, the effect of chemical properties of coals on coal free swelling index has been studied by artificial neural network methods. Artificial neural networks (ANNs) method for more than 300 datasets was used for evaluating free swelling index value. In this investigation, some of input parameters (nearly 10) were used. For selecting the best model for this study, outputs of models were compared. A three-layer ANN was found to be optimum with architecture of 12 and 5 neurons in the first and second hidden layer, respectively, and 1 neuron in output layer. In this work, training and test datas square correlation coefficients (R2) achieved 0.99 and 0.92, respectively. Sensitivity analysis shows that, nitrogen (dry), carbon (dry), hydrogen (dry), Btu (dry), volatile matter (dry) and fixed carbon (dry) have positive effects and moisture, oxygen (dry), ash (dry) and total sulfur (dry) have negative effects on FSI. Finally, the fixed carbon was found to have the lowest effect (0.0425) on FSI.
coal chemical properties free swelling index artificial neural networks (anns) cokeability back propagation neural network (bpnn)