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

by 1,2, 3,4,*, 1,5, 4, 4 and 1,*
1
School of Mines, China University of Mining and Technology, Xuzhou 221116, China
2
School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China
3
School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
4
China Coal Tianjin Underground Engineering Intelligence Research Institute, Tianjin 561000, China
5
Wangjialing Coal Mine, China Coal Huajin Group Co., Ltd., Yuncheng 043000, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Longjun Dong, Yanlin Zhao and Wenxue Chen
Sustainability 2022, 14(3), 1340; https://doi.org/10.3390/su14031340
Received: 31 December 2021 / Revised: 19 January 2022 / Accepted: 20 January 2022 / Published: 25 January 2022
(This article belongs to the Topic Mining Safety and Sustainability)
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. View Full-Text
Keywords: top-coal caving mining; process parameters; decision model; BP neural network; similarity simulation test top-coal caving mining; process parameters; decision model; BP neural network; similarity simulation test
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MDPI and ACS Style

Liang, M.; Hu, C.; Yu, R.; Wang, L.; Zhao, B.; Xu, Z. Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks. Sustainability 2022, 14, 1340. https://doi.org/10.3390/su14031340

AMA Style

Liang M, Hu C, Yu R, Wang L, Zhao B, Xu Z. Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks. Sustainability. 2022; 14(3):1340. https://doi.org/10.3390/su14031340

Chicago/Turabian Style

Liang, Minfu, Chengjun Hu, Rui Yu, Lixin Wang, Baofu Zhao, and Ziyue Xu. 2022. "Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks" Sustainability 14, no. 3: 1340. https://doi.org/10.3390/su14031340

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