基于MSCALA的矿井巷道需风量调节方法OA

To address insufficient precision in determining ventilation resistance adjustment ranges and the susceptibility of coordinated multi-branch airflow regulation to disturbance-induced imbalance in conventional required-airflow regulation for mine roadways,this study proposed a method for regulating required airflow in mine roadways based on the Multi-Strategy Collaborative Artificial Lemming Algorithm(MSCALA).An on-demand airflow optimization and regulation model was established with airflow in the target air-demand branch as the objective.An exact penalty function method was used to transform constraints during optimization,and airflow sensitivity theory was used to select the resistance-adjustment branch set and determine reasonable ventilation resistance adjustment ranges.MSCALA,developed by incorporating a good point set population initialization strategy,a strategy combining an adaptive weight factor and a nonlinear escape coefficient,an Artificial Bee Colony global exploration strategy,and a Cauchy mutation strategy into the Artificial Lemming Algorithm(ALA),was then used to determine the optimal ventilation resistance adjustment value for each resistance-adjustment branch,thereby achieving precise airflow regulation.Experimental results showed that when the gas concentration in the air-demand branch exceeded the limit,the maximum airflow optimized by MSCALA for the target branch reached 7.01 m3/s,66.11％higher than the initial airflow of 4.22 m3/s.MSCALA outperformed comparison algorithms including ALA,the Dung Beetle Optimizer,the Black-Winged Kite Algorithm,and the Enhanced Adaptive Lemming Algorithm in global search capability,convergence speed,and optimization performance.The method enables rapid,precise,and dynamic regulation of airflow in air-demand branches and effectively addresses insufficient airflow when the gas concentration exceeds the limit.