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

Zhenguo Yan, Huachuan Wang (), Huicong Xu (), Jingdao Fan and Weixi Ding
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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
Huicong Xu: State Key Laboratory of Green Low Carbon Development of Oil-Rich Coal in Western China, Xi’an 710054, China
Jingdao Fan: College of Safety Engineering, 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

Sustainability, 2024, vol. 16, issue 3, 1-14

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.

Keywords: intelligent coal mining; gently inclined; coal pillar; physical indicators; prediction methodology; LSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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