Abstract
In multi-coal seam mining, when the lower coal seam mining face passes over the goaf, residual coal pillars, and other geological anomaly areas of the overlying coal seam, abnormal mine pressure appears, and the hydraulic support monitoring system is inaccurate in identifying the pressure, which brings great hidden dangers to the safe production of the mining face. It is very necessary to carry out the prediction and early warning of the mine pressure of this kind of mining face. In order to improve the reliability of the prediction model, this paper takes the 31317 mining faces of the Chahasu coal mine as the engineering background, studies the mechanism of the disaster caused by the abnormal mine pressure of the residual coal pillar, uses the clustering analysis algorithm to divide the abnormal mine pressure area of the mining face, reconstructs the abnormal mine pressure type and number based on the prediction results of CEEMDAN−Transformer deep learning, and proposes the disaster criterion of the abnormal mine pressure. The research results show that, when the 31317 mining face enters the goaf of the overlying 31203 and 31201 coal seams, the residual coal pillars are accompanied by the instability of the interlayer rotation, and the dynamic and static loads are superimposed to form the additional stress of the residual coal pillars and transfer downward, causing the abnormal mine pressure of the mining face to appear; based on the hydraulic support resistance data of the mining face within the range of 3921.4−5050.4 m advance, the clustering analysis results show that there are six abnormal mine pressures during this period, and the types are cutting eye, residual coal pillar, square breaking, previous working face goaf square breaking, double square breaking, and geological damage zone. The clustering analysis is used to reconstruct the abnormal mine pressure area based on the prediction results of the mine pressure time series (MPTS) after interpolation completion, decomposition, and noise reduction preprocessing, and the MAE values are all lower than 2000 kN, predicting that there will be one abnormal pressure between the 80#−129# hydraulic supports in the process of advancing to 5050.4−5219.5 m, corresponding to the 18th square breaking area of the working face. Through the verification in the actual production, the prediction result is accurate; when the predicted value of the hydraulic support working resistance is greater than 19,000 KN, measures should be taken to speed up the advancing speed of the mining face, quickly pass through the abnormal mine pressure area, and prevent the disaster caused by the abnormal mine pressure. The prediction clustering analysis reconstruction abnormal pressure analysis method based on mining working face mine pressure data proposed in this paper provides a new direction and guidance for the abnormal mine pressure prediction analysis of mining working face and has good foresight, good intelligent prediction, and a good analysis method for the intelligent empowerment of mine safety production.