Assessment of Coal Spontaneous Combustion Potential Using Statistical and Machine Learning Approaches
DOI:
https://doi.org/10.17159/Keywords:
Coal spontaneous combustion potential, Classical statistical models, Intelligent modeling techniques, Intrinsic coal properties, Crossing point temperature (CPT)Abstract
Spontaneous combustion of coal is one of the most critical safety hazards in underground coal mining, posing serious risks to human life, economic sustainability, and the environment. The aim of this study is to evaluate and predict the spontaneous combustion potential of coal seams using a combination of classical statistical and intelligent modeling approaches, with particular emphasis on intrinsic coal properties. To achieve this objective, a comprehensive database consisting of 165 coal samples was compiled from published experimental studies. The crossing point temperature was employed as a quantitative index to characterize coal spontaneous combustion potential. After data preprocessing, the relationships between intrinsic coal parameters and CPT values were investigated using regression models, artificial neural networks, including multilayer perceptron and radial basis function networks, as well as a decision tree–based classification model. The results indicate that the relationships between intrinsic coal properties and spontaneous combustion potential are predominantly nonlinear and multivariate. To assess model generalization, the developed models were validated using coal seam data from the Parvadeh coal mines in the Tabas coalfield, Iran. The validation results showed good agreement between predicted and observed spontaneous combustion tendencies, confirming the reliability of the proposed framework.
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Copyright (c) 2026 Hasan Ghasemzadeh, Hasan Madani, Farhang Sereshki, Sajjad Afraei
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