An AGCRN Algorithm for Pressure Prediction in an Ultra-Long Mining Face in a Medium–Thick Coal Seam in the Northern Shaanxi Area, China

Abstract

Due to the increase in the length of the mining face, the pressure characteristics and spatial distribution in fully-mechanized mining faces are different from those in typical mining faces, which leads to great challenges in roof management and the intelligent control of ultra-long mining faces. Taking the ultra-long mining face of a medium–thick coal seam in the northern Shaanxi mining area as an example and using field monitoring data for the working resistance of the hydraulic supports, a non-linear prediction method was used to extract the features of the dynamic data sequence of the working resistance of the hydraulic supports, and a deep learning method was used to establish a pressure prediction model for ultra-long mining faces based on the adaptive graph convolutional recurrent network (AGCRN) algorithm. In the proposed model, the supports in the fully mechanized mining face were regarded as the logic nodes of a topological structure, while the time-series resistance data for the supports were regarded as data nodes on a graph. The AGCRN model was used to determine the spatiotemporal relationship between the working resistance data of adjacent hydraulic supports, thereby improving the accuracy of the proposed model. The MAE and MAPE were employed as performance evaluation indices. When the node-embedding dimension was set to 10 and the time window was set to 16, the corresponding MAE and MAPE values of the prediction model were the minimum values. Compared with the reference models (i.e., the BP, GRU, and DCRNN models), the MAE and MAPE of the AGCRN model were 38.75% and 23.49% lower, respectively, indicating that the AGCRN model effectively demonstrates high accuracy in predicting the working resistance of supports. The AGCRN model was applied in the prediction of the working resistance of the supports of the ultra-long fully mechanized mining face. The results revealed that the working resistance of the supports in the lower and upper areas was relatively small along the strike, whereas the working resistance of the supports in the middle area was large, exhibiting a zoning pattern of “low-high-low” in terms of the average working resistance. In conclusion, the proposed model provides data references for the state of the hydraulic supports, pressure identification, and intelligent control of the ultra-long mining faces of the medium–thick coal seams in northern Shaanxi.

A Prediction Approach Based on Clustering Reconstruction for Abnormal Mining Pressure of Longwall Face under Residual Coal Pillars

“…In the formula, N is the sample number, and yi is the actual value of the sample num- MAE is a common evaluation metric for hydraulic support working resistance predictions, which can accurately reflect the difference between the MPTS predicted value and the actual value, that is, the accuracy of the prediction. The specific calculation method is shown in Formula (15).…”

“…Gao used the field monitoring data of the hydraulic support working resistance, used the nonlinear prediction method to extract the dynamic data sequence characteristics of the hydraulic support working resistance, and applied the AGCRN model to the hydraulic support working resistance prediction of the super-long comprehensive mining face. The predicted result is that the average working resistance in the middle of the working face is higher [15]. Dong proposed a multivariate linear regression model to predict the pressure when the coal mine roof breaks and obtained the linear regression coefficient and pressure prediction value of the model.…”

Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models

“…The primary research areas related to this include: gas concentration prediction through data analysis 13 , accident prevention and early warnings, especially for incidents like gas explosions and coal and gas herniations 14 , and digital twin modelling 15 , whom focusing on the coordination of coal mining equipment 16 . Other areas include safety monitoring and risk warning related to gas concentration 17 , roof pressure prediction 18 , multifactor coupling and decoupling of the coal mining face 19 , etc. Despite the diversity of these studies, the analysis based on gas concentration remains the predominant research direction.…”