A reliable ventilation system is essential for maintaining a comfortable working environment and ensuring safety production in an underground coal mine. The automated fan switchover technique was developed for changing the main fan for maintenance with lower air flow volatility of underground mine in the switchover process. This article established the optimization model in the main fans switchover process, used the improved particle swarm optimization algorithm to solve the model, and achieved minimum air flow volatility in the fans switchover process. Compared to previous studies, computer simulations have shown that the proposed algorithm can effectively find the global optimal solution with less initial parameters and achieved lower air flow volatility in underground mine. The particle swarm optimization solution, searching diversity, prevents it from confining to local optimal solutions and enhances convergence. The reasonable step length is beneficial to reduce the air flow volatility and main fans switchover time. The air flow volatility is larger comparatively when some doors are nearly open or closed fully at the start—stop phase of the switchover process. A case application in a China's domestic coal mine shows that the air flow volatility of the underground mine in the main fans switchover process is no more than 0.4%.
H. Ge et al., "A Mine Main Fans Switchover System with Lower Air Flow Volatility based on Improved Particle Swarm Optimization Algorithm," Advances in Mechanical Engineering, vol. 11, no. 3, SAGE Publications, Mar 2019.
The definitive version is available at https://doi.org/10.1177/1687814019829281
Keywords and Phrases
Air flow fluctuation; Constraint optimization model; Fan switchover; Particle swarm optimization; Underground coal mine
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01 Mar 2019