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Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks

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  • Minfu Liang

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Chengjun Hu

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    China Coal Tianjin Underground Engineering Intelligence Research Institute, Tianjin 561000, China)

  • Rui Yu

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Wangjialing Coal Mine, China Coal Huajin Group Co., Ltd., Yuncheng 043000, China)

  • Lixin Wang

    (China Coal Tianjin Underground Engineering Intelligence Research Institute, Tianjin 561000, China)

  • Baofu Zhao

    (China Coal Tianjin Underground Engineering Intelligence Research Institute, Tianjin 561000, China)

  • Ziyue Xu

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

The method of fully mechanized top-coal caving mining has become the main method of mining thick-seam coal. The process parameters of fully mechanized caving will affect the recovery rate and gangue content of top coal. Through numerical simulation software, the top-coal recovery rate and gangue content, under different fully mechanized caving process parameters, were simulated, and the influence law of different fully mechanized caving process parameters on top-coal recovery rate and gangue content was obtained. A decision model for top-coal caving process parameters was established with a BP neural network, and the optimal top-coal caving parameters were obtained for the actual situation of a working face. On this basis, a in-lab similarity simulation test of the particle material was carried out. The results show that the top-coal recovery rate and gangue content were 86.56% and 3.45%, respectively, and the coal caving effect was good. A BP neural network was used to study the decisions optimizing fully mechanized caving process parameters, which effectively improved the decision-making efficiency thereabout and provided a basis for realizing intelligent, fully mechanized caving mining.

Suggested Citation

  • Minfu Liang & Chengjun Hu & Rui Yu & Lixin Wang & Baofu Zhao & Ziyue Xu, 2022. "Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks," Sustainability, MDPI, vol. 14(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1340-:d:733100
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    1. Bhattacharya, Mita & Rafiq, Shuddhasattwa & Bhattacharya, Sankar, 2015. "The role of technology on the dynamics of coal consumption–economic growth: New evidence from China," Applied Energy, Elsevier, vol. 154(C), pages 686-695.
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    Cited by:

    1. Longjun Dong & Yanlin Zhao & Wenxue Chen, 2022. "Mining Safety and Sustainability—An Overview," Sustainability, MDPI, vol. 14(11), pages 1-6, May.
    2. Jiahang Lu & Xiaohua Wu, 2022. "Research on Urban Greenway Alignment Selection Based on Multisource Data," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    3. Deqiu Wang & Yun Zheng & Fulian He & Jiayu Song & Jianlong Zhang & Yanhao Wu & Pengpeng Jia & Xiaohui Wang & Baoping Liu & Feifei Wang & Yajiang Zhang & Kai Tao, 2023. "Mechanism and Control of Asymmetric Floor Heave in the Gob-Side Coal Roadway under Mining Pressure in Extra-Thick Coal Seams," Energies, MDPI, vol. 16(13), pages 1-19, June.

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