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 data’s 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.

► Neural network modeling has best correlation to evaluating the effects of coal chemical properties on FSI.
► Results from ANN showed that nitrogen (dry), carbon (dry), hydrogen (dry), Btu (dry), volatile matter (dry) and fixed carbon (dry) had positive effects on free swelling index, respectively.
► The negative effects of input parameters were related to moisture, oxygen (dry), ash (dry) and total sulfur (dry), respectively.
► The fixed carbon was found to have the lowest effect (0.0425) on FSI.