An Artificial Intelligence-based Model for the Prediction of Spontaneous Combustion Liability of Coal Based on Its Proximate Analysis

ABSTRACT

Coal undergoes self-heating resulting in spontaneous combustion when exposed to oxygen in the air. The determination of various constituents within coal, especially the ultimate analysis and petrographic composition requires the use of sophisticated equipment and expertise, unlike the proximate analysis. In this study, an attempt has been made to predict the spontaneous combustion liability of Witbank coal, South Africa using both experiment and artificial neural network (ANN) based on the proximate analysis. The experimental tests show that the coal properties vary from one sample to another. The predictive models obtained from the ANN were compared with the conventional multilinear regression analysis (MLR) conducted. The obtained results from the predictive models showed that the ANN model is most suitable for the prediction of liability indices. The influence of the input parameters on the predicted liability index was investigated using a partial derivative method (PD). The PD of the moisture (M) and volatile matter (VM) are all positive indicating that an increase in M and VM will increase liability index, while the PD of the liability index with respect to ash (A) and fixed carbon (FC) are both negative indicating that as the value of A and FC decrease, the liability indices increases.

KEYWORDS:

• Artificial intelligence
• partial derivative method
• coal
• crossing point temperature
• proximate analysis
• spontaneous combustion

Acknowledgments

The authors acknowledged the Goldfields laboratory, School of Mining Engineering, University of the Witwatersrand Johannesburg, South Africa for using their facilities.