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<p>Spontaneous combustion of coal poses a significant safety and operational risk in mining, highlighting the need for reliable and industry-oriented predictive indicators. The Crossing Point Temperature (CPT) is widely used to assess coal self-heating tendency, where lower CPT values indicate higher spontaneous combustion risk. CPT is primarily influenced by coal compositional parameters, including moisture, ash, volatile matter, and fixed carbon, which can be readily determined through proximate analysis. In this study, CPT values were experimentally measured for 32 coal samples, and a regression-based model was developed to quantify the relationship between coal composition and CPT. This model was subsequently employed as the objective function in a constrained optimization framework using two metaheuristic algorithms: Black Widow Optimization (BWO) and Giza Pyramids Construction (GPC). Optimization was performed under physically meaningful constraints, including compositional summation and parameter bounds relevant to industrial practice. The results indicate that the maximum CPT obtained by both algorithms was 204.224&#xa0;°C, corresponding to low moisture (2.05%) and volatile matter (18.68%) contents and high ash content (67.36%), representing a low-risk spontaneous combustion scenario. Conversely, the minimum CPT was 146.83&#xa0;°C, associated with higher moisture (8.99%) and volatile matter (30.37%) and lower ash content (54.41%), indicating increased combustion risk. Both algorithms produced consistent results, demonstrating stable convergence and computational efficiency .It should be noted that the analysis is limited to the investigated dataset and coal samples, and the findings should not be generalized to all coal types without further validation. Within these limitations, the proposed approach provides a practical tool for identifying critical CPT conditions based on standard coal properties, supporting risk assessment and decision-making in coal mining and handling operations.</p>

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Optimization of crossing point temperature for predicting coal spontaneous combustion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Hossein Khakshour,
  • Nafiseh Ayeen,
  • Ramin Rafiee

摘要

Spontaneous combustion of coal poses a significant safety and operational risk in mining, highlighting the need for reliable and industry-oriented predictive indicators. The Crossing Point Temperature (CPT) is widely used to assess coal self-heating tendency, where lower CPT values indicate higher spontaneous combustion risk. CPT is primarily influenced by coal compositional parameters, including moisture, ash, volatile matter, and fixed carbon, which can be readily determined through proximate analysis. In this study, CPT values were experimentally measured for 32 coal samples, and a regression-based model was developed to quantify the relationship between coal composition and CPT. This model was subsequently employed as the objective function in a constrained optimization framework using two metaheuristic algorithms: Black Widow Optimization (BWO) and Giza Pyramids Construction (GPC). Optimization was performed under physically meaningful constraints, including compositional summation and parameter bounds relevant to industrial practice. The results indicate that the maximum CPT obtained by both algorithms was 204.224 °C, corresponding to low moisture (2.05%) and volatile matter (18.68%) contents and high ash content (67.36%), representing a low-risk spontaneous combustion scenario. Conversely, the minimum CPT was 146.83 °C, associated with higher moisture (8.99%) and volatile matter (30.37%) and lower ash content (54.41%), indicating increased combustion risk. Both algorithms produced consistent results, demonstrating stable convergence and computational efficiency .It should be noted that the analysis is limited to the investigated dataset and coal samples, and the findings should not be generalized to all coal types without further validation. Within these limitations, the proposed approach provides a practical tool for identifying critical CPT conditions based on standard coal properties, supporting risk assessment and decision-making in coal mining and handling operations.