Optimization of the non-stop switchover system control for the main fans used in mining applications

Abstract

A stable ventilation system is an essential guarantee for the efficient production and safety of underground workers. In order to solve the big changes in underground air quantity, gas accumulation, and other problems caused by mine main fans switchover. This paper proposes a non-stop switchover system of the mine main fans based on intelligent control and establishes a dynamic optimization model for the switchover process of the mine main fans. The equilibrium optimizer algorithm is improved by chaos mapping and opposition learning machine based on refraction principle to solve the model, and the simulation experiment is carried out with MATLAB. The results show that the proposed method can effectively mitigate the change of underground air quantity during the switchover process of mine main fans. In the 120 s of system operation, the change rate of underground air quantity is consistently within 0.4%, and the two mine main fans always work in the stable interval, which proves the system's high efficiency, stability and safety.

Keywords

• mine main fans switchover system
• dynamic optimization model
• equilibrium optimizer algorithm
• chaotic mapping
• opposition learning machine