Research on Intelligent Design and Visualization of Gas Extraction Drilling Based on PSO–LSTM

Abstract

Under the background of intelligent construction of coal mines, gas extraction design is still based on manual design, which is complex, time–consuming, and error–prone, and its automation degree needs to be improved. In order to solve this problem, taking the 1302 working face of a mine in Shanxi Province as the research object, this paper carried out relevant research. Firstly, the influencing factors of gas extraction were determined, and the influence rules of different parameters on the extraction effect were studied by numerical simulation. Secondly, an intelligent optimization method of gas extraction drilling parameters based on deep mining called the PSO–LSTM model, is proposed. This model uses the PSO algorithm to optimize the parameters of the LSTM model, so as to improve the accuracy of the LSTM model results. Finally, a quantitative expression algorithm of 3D spatial information of gas extraction drilling holes based on Python is proposed, which can automatically generate 3D spatial models of bedding or through gas extraction drilling holes using optimized drilling parameters and known 3D information of coal seams. This study shows that the results obtained using the PSO–LSTM model are the same as the drilling parameters obtained using numerical simulation, which verifies the accuracy of the PSO–LSTM model. According to the optimized drilling parameters, a 3D model of gas extraction drilling is quickly generated, which greatly reduces the tedious work of drawing construction drawings for coal mine enterprises and improves the intelligence level of coal gas extraction drilling.