Research on Intelligent Selection of Hydraulic Support for Gently Inclined Comprehensive Mining Face Based on PSO-BP Neural Network

As an important part of coal mining mechanization, the reliability of hydraulic support is an important guarantee for the hydraulic support system of the comprehensive mining face, and the fast, accurate, and intelligent selection of hydraulic support determines its reliability. To realize the intelligent selection of hydraulic support for the slow inclined comprehensive mining face, and to improve the efficiency and accuracy of the selection of the hydraulic support, a hydraulic support selection method based on the PSO-BP neural network is proposed in this paper. A relationship model between geological parameters and hydraulic support technical parameters is established, and the model is trained by using hydraulic support selection case data as samples. Based on the training results, the model can be used to input new geological parameters to intelligently recommend the selection plan of hydraulic support technical parameters. The actual selection scheme of a coal mine is analyzed as a case study, and the results are compared with the traditional BP neural network selection results. The results indicate that the PSO-BP neural network can efficiently generate the hydraulic support technical parameter selection program. It outperforms the traditional BP neural network in terms of computational accuracy and convergence speed. This provides a reliable reference for intelligent hydraulic support selection.