Method for adjusting air volume of mine ventilation network based on DE-GWO algorithm

In order to meet the wind demand of a certain underground location in time, an intelligent emergency air regulation scheme that selects different branches according to the expected value of branch air volume was proposed. Firstly, the nonlinear optimization mathematical model of ventilation network was established with the goal of maximizing the adjustable air volume of the branch of mine ventilation network. Aiming at the constraint conditions of air volume balance and air pressure balance in the optimization model, the non differentiable exact penalty function was used to transform it into the penalty term in the target model. Then, the optimal adjustable branch set and wind resistance regulation range were obtained by calculating the wind network sensitivity matrix, and the optimization was realized based on gray wolf algorithm. In order to make up for the defect that the gray wolf algorithm was easy to fall into local optimum in solving complex problems, a differential evolution gray wolf optimizer(DE-GWO) was proposed, which was mainly based on the continuous iterative updating of individual position of population by gray wolf algorithm, adding mutation, crossover and selection operations of differential evolution algorithm, so as to maintain the diversity of population. Finally, based on the experimental platform of mine intelligent control system, the feasibility of emergency air conditioning scheme was verified. The results show that compared with GWO algorithm, the DE-GWO algorithm proposed in this paper has a significant improvement in the optimization performance and stability, and can be used to adjust the air volume in time. © 2021, Central South University Press. All right reserved.