Optimization of Stope Structure Parameters in E’xi Iron Mine Based on Rock Stress Analysis

Abstract

Underground mining causes the redistribution of surrounding rock stress. The value of rock stress is one of the important factors that should be considered when stope structure size is designed. E’xi iron ore body is gently declined in attitude, thin in thickness, and large in occurrence area. According to mining technological conditions and the selected mining method of E’xi iron mine, a typical section of the mine is selected, and it is abstracted into a plane strain problem. In terms of optional stope structure parameters, 9 calculation schemes are planed. Rock stress distributions of stope caused by mining are simulated by the Finite Element Method. Rational stope parameters are chosen by comparing stress distributions of all calculation schemes. Analysis result shows that 8m net distance between pillars, and 4m pillar width are the optimum stope parameters, which not only stabilize the stope but also improve mining efficiency. Choosing stope parameters based on rock stress distributions calculated with numerical simulations, is provided a scientific analysis method for determining rational mining parameters of underground mines.

Stope Structural Parameters Design towards Green and Deep Mining: A Review

“…Underground stope design involves determining the geometric shape and size of underground stopes, which play a crucial role in safe production and have economic benefits [1][2][3]. Designing the stope structural parameters entails tackling a complex optimization problem involving numerous decision variables [4,5], which include the occurrence of the ore body, geological structures [1,6,7], rock mass quality [8,9], mining-induced ground pressure [10][11][12], the ore loss rate, the dilution rate [13,14], and the required production capacity of the mine, among others [15,16]. The interdependencies among these variables are challenging to express using precise mathematical or mechanical formulations, and the correlation between each decision variable is often weak.…”