Model Construction and Parameters Acquisition of the Predicted Surface Movement Deformation under Thick Loose Layer Mining Area. (4th February 2023)

• Record Type: Journal Article
• Authors: Zhang, Jinman; Li, Jiewei; Xu, Liangji; Xu, Ruirui; Yue, Caiya
• Other Names: Shontz Suzanne M. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2023(2023)
• Publication Date: 2023-02-04
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2023/8796194
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In China, gas and oil reserves are very scarce, but coal resources are abundant in the energy architecture, which decides that coal will remain the dominant energy source for a long time in the future. The accurate prediction of the size and extent of surface movement after coal seam mining is of great significance for the safe promotion of production activities in the mine area and the safety of people's lives and properties in the mine area. The surface movement deformation under thick loose seam conditions indicates the phenomenon of a large subsidence value and influence range. To predict the size and range of surface movement deformation under thick loose layer conditions accurately, a hyperbolic secant model is constructed based on the hyperbolic secant function. For high nonlinearity of the model parameters, the adaptive step fruit fly algorithm (ASFOA) is introduced into the process of solving the model parameters. Simulation experiments are conducted in three aspects: monitoring point antideficiency, antigross error, and parameter stability. The simulation results show that the ASFOA algorithm achieves high accuracy in finding the parameters of the hyperbolic secant model. The hyperbolic secant model was applied to the 11111 working face under the mining conditions of thick loose layer geology in the Huainan mine. The engineering application results indicate that the hyperbolic secant model performs well on the prediction of surface movement deformation under thick loose layer conditions.