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Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction

Author

Listed:
  • Xianzhong Li

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Shigang Hao

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Tao Wu

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Weilong Zhou

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jinhao Zhang

    (School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

Abstract

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K 1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.

Suggested Citation

  • Xianzhong Li & Shigang Hao & Tao Wu & Weilong Zhou & Jinhao Zhang, 2023. "Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11523-:d:1202373
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    Cited by:

    1. Longlong He & Ruiyu Pan & Yafei Wang & Jiani Gao & Tianze Xu & Naqi Zhang & Yue Wu & Xuhui Zhang, 2024. "A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process," Mathematics, MDPI, vol. 12(7), pages 1-31, April.

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