Research On Application Of Improved Grey Wolf Algorithm In Optimization And Regulation Of Air Volume Of Mine Ventilation Network

The mine ventilation system can provide sufficient fresh air to all the underground places where the wind is used,dilute and discharge the underground gas,dust and other toxic and harmful gases.It is a prerequisite for safe production in coal mines and an important guarantee for the health of underground operators.Therefore,improving the ventilation capacity of the mine,realizing the on-demand air distribution and emergency air adjustment of the ventilation system have important and practical significance for the efficient production of the mine and the safety of the miners.First of all,in order to meet the demand air volume of the wind branch in different wind network states,an intelligent air regulation scheme is proposed that selects the number of different adjustment branches according to the expected value of the branch air volume,and the maximum adjustable air volume of the required branch of the ventilation network is proposed.The goal is to establish a non-linear mathematical model for air volume adjustment of the ventilation network.Aiming at the constraint conditions of the wind network balance law,branch minimum air demand,and fan operating conditions in the model,a non-differentiable precise penalty function is used to convert it into a penalty term in the target model,thereby establishing a wind network air volume optimization adjustment.Non-linear unconstrained mathematical model.Then,in order to determine the optimal air volume adjustment scheme,the concept of wind network sensitivity is introduced,the nature of the wind network sensitivity matrix and its solution method are studied,and the wind network sensitivity matrix is applied to mine air volume adjustment.Choose the optimal adjustable branch set and wind resistance adjustment range.At the same time,the sensitivity matrix is also applied to the optimal layout of wind speed monitoring points and the abnormal analysis of wind volume monitoring data.Then,the gray wolf algorithm is used to solve the air volume optimization adjustment model.Aiming at the problem that the gray wolf algorithm has low accuracy and easy to fall into local optimization when solving complex optimization problems,a multi-strategy fusion improved gray wolf optimization algorithm(MGWO)is proposed.The gray wolf algorithm is improved with the help of the four strategies of good point set initialization,differential mutation,nonlinear control parameters and segmented step size update,and the overall improvement of the gray wolf algorithm's solution accuracy and optimization performance.Finally,based on the mine intelligent ventilation experimental measurement and control platform,the air volume adjustment technology was researched and verified,the ventilation system network model of the experimental platform was constructed,the mine intelligent air regulation and optimization control system was designed,and the related functional modules of the mine intelligent ventilation were developed.And the MGWO algorithm is applied to the air volume optimization adjustment of the actual system.The feasibility of the intelligent air regulation scheme is verified by the mine ventilation experiment measurement and control platform,and it provides certain theoretical and practical guidance for the research on the air volume optimization adjustment of the mine intelligent ventilation system.The thesis has 45 pictures,16 tables,and 101 references.