Prediction of mine strata behaviors law and main control factors in the fully mechanized caving face of Hujiahe Coal Mine

Abstract

Among the existing prediction methods for strata behaviors law in working faces, the methods based on numerical simulation and statistical regression cannot achieve real-time and precise prediction of strata behaviors law in working faces. Deep learning methods have problems such as a large number of hyperparameters that are difficult to set and slow model training speed. In order to solve the above problems, based on the time-series data of internal stress changes in the coal body monitored during the mining process of the 402102 working face in Hujiahe Coal Mine, the particle swarm optimization based gate recurrent unit (PSO-GRU) is applied to predict the strata behaviors law in the working face. The PSO algorithm is used to optimize GRU. The PSO-GRU model is constructed to achieve automatic optimization of hyperparameters, thereby improving the training speed and prediction precision of GRU. Based on the prediction results, the analytic hierarchy process (AHP) is used to establish the evaluation index system of the main control factors of the strata behaviors of the 402102 mining face. The roof conditions, mining technology, coal seam occurrence, and geological structure are identified as the first level indicators affecting the strata behaviors of the working face. The 14 representative second level indicators are further subdivided. The test results show the following points. ① Compared with the unoptimized GRU model, the mean square error (MSE) of the PSO-GRU model is reduced by 83.9%, the root mean square error (RMSE) is reduced by 59.8%, the mean absolute error (MAE) is reduced by 59.0%, and the coefficient of determination R2 is increased by 28.9%. ② The PSO-GRU model has a fitting degree of over 0.980 for predicting strata behaviors data, demonstrating good nonlinear fitting and generalization capabilities. ③ The occurrence factors of coal seams in geological conditions have the greatest impact on the strata behaviors of the mining face, with a weight of 0.47. Among the factors that can be intervened by humans, the impact of advancing speed on the strata behaviors of the working face is the greatest, with a weight of 0.13.

Keywords

• fully mechanized caving working face
• prediction of strata behaviors law
• pso-gru model
• analytic hierarchy process
• main controlling factors
• evaluation index system
• time-series data