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Area of Science:

  • Mining Engineering
  • Computational Science

Background:

  • Coal spontaneous combustion (CSC) poses significant risks in mining operations.
  • Accurate temperature prediction is crucial for preventing CSC-related accidents and ensuring mine safety.

Purpose of the Study:

  • To develop and validate a robust prediction model for coal temperature during CSC.
  • To enhance the accuracy and reliability of CSC risk assessment in coal mines.

Main Methods:

  • Utilized a large experimental device to collect characteristic temperature data from gas coal.
  • Developed a simulated annealing-support vector machine (SA-SVM) model for nonlinear mapping of gas production to coal temperature.
  • Compared SA-SVM with back-propagation neural network (BPNN) and single support vector machine (SVM) models.

Main Results:

  • The SA-SVM model demonstrated superior prediction accuracy, robustness, and error tolerance compared to BPNN and single SVM.
  • BPNN exhibited overfitting issues with small sample sizes, while single SVM showed unstable outputs due to hyperparameter sensitivity.
  • SA-SVM effectively optimized hyperparameters through global optimization, leading to improved performance.

Conclusions:

  • The SA-SVM model offers a reliable and accurate method for predicting coal temperature and assessing CSC risk.
  • Findings provide practical significance for mitigating CSC hazards in gobs and enabling timely safety warnings.