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The interaction of coal and oxygen releases heat that promotes oxidation process when it constantly accumulates. If heat released is more than heat dissipated during the oxidation process, the temperature increases. When the temperature of the coal reaches its ignition point, the coal ignites and potentially result in spontaneous combustion. This study evaluated and developed reliable predictive models using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multilinear regression (MLR) analyzes based on data obtained from the proximate and ultimate analyses for coal samples collected from the Witbank Coalfields, South Africa. Thirty-five (35) coal samples data have been used, including proximate analysis, ultimate analysis, and spontaneous combustion liability indices [crossing point temperature (XPT), Wits-Ehac Index and FCC (Feng, Chakravorty, Cochrane) Index]. This study indicated that some of the samples that were categorized to have moderate and high risks according to the XPT risk rating are not in-line with those from the Wits-Ehac and FCC tests for both the experimental and published data. The results of the models showed that ANN performed better for the XPT and Wit-Ehac predictions, the ANFIS provides a better prediction of the FCC Index, while the MLR has the highest error values.

Computational intelligence-based models for predicting the spontaneous combustion liability of coal