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Construction and Application of an Intelligent Prediction Model for the Coal Pillar Width of a Fully Mechanized Caving Face Based on the Fusion of Multiple Physical Parameters

Author

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  • Zhenguo Yan

    (College of Safety Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Huachuan Wang

    (State Key Laboratory of Green Low Carbon Development of Oil-Rich Coal in Western China, Xi’an 710054, China
    Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, UK)

  • Huicong Xu

    (State Key Laboratory of Green Low Carbon Development of Oil-Rich Coal in Western China, Xi’an 710054, China
    College of Energy Resources, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Jingdao Fan

    (College of Safety Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    College of Energy Resources, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Weixi Ding

    (College of Energy Resources, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams.

Suggested Citation

  • Zhenguo Yan & Huachuan Wang & Huicong Xu & Jingdao Fan & Weixi Ding, 2024. "Construction and Application of an Intelligent Prediction Model for the Coal Pillar Width of a Fully Mechanized Caving Face Based on the Fusion of Multiple Physical Parameters," Sustainability, MDPI, vol. 16(3), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:986-:d:1324944
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    References listed on IDEAS

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    1. Zhou, Luming & Zhu, Zhende & Xie, Xinghua & Hu, Yunjin, 2022. "Coupled thermal–hydraulic–mechanical model for an enhanced geothermal system and numerical analysis of its heat mining performance," Renewable Energy, Elsevier, vol. 181(C), pages 1440-1458.
    2. Xuelong Li & Xinyuan Zhang & Wenlong Shen & Qingdong Zeng & Peng Chen & Qizhi Qin & Zhen Li, 2023. "Research on the Mechanism and Control Technology of Coal Wall Sloughing in the Ultra-Large Mining Height Working Face," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    3. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
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