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

Minfu Liang, Chengjun Hu, Rui Yu, Lixin Wang, Baofu Zhao and Ziyue Xu
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Minfu Liang: School of Mines, 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
Rui Yu: School of Mines, China University of Mining and Technology, Xuzhou 221116, 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

Sustainability, 2022, vol. 14, issue 3, 1-17

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.

Keywords: top-coal caving mining; process parameters; decision model; BP neural network; similarity simulation test (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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