Precise wind allocation scheme decision based on attraction-repulsion algorithm

To address the issue of fluctuating branch airflow in the ventilation system caused by changes in mine ventilation facilities and air network structure during underground production operations, which in turn leads to insufficient airflow at consumption points, a precise wind allocation algorithm based on the Attraction-Repulsion Optimization Algorithm (AROA) is proposed. The ventilation fan power consumption minimization was set as the optimization objective, with the required airflow for working and standby faces as constraints, and a mathematical model of the mine ventilation system was established. By employing AROA, the ventilation fan and existing underground ventilation facilities were precisely controlled, and an optimized solution was iteratively generated. During the optimization process, an improved Brownian motion, trigonometric function transformation, random solution selection mechanism, and memory-based local search operator were integrated to dynamically filter and fine-tune candidate solutions, ultimately achieving an optimal precise wind allocation plan with the lowest ventilation operation cost. Performance test results showed that AROA had a significant advantage in comprehensive optimization performance compared to Genetic Algorithm (GA), Simulated Annealing-Improved Particle Swarm Optimization (SA-IPSO), and Monotonic Basin Hopping (MBH). When solving the Ackley function, AROA required fewer iterations to obtain the optimal and average optimal solutions compared to GA, SA-IPSO, and MBH. Case study results showed that the precise wind allocation scheme determined by the AROA-based algorithm resulted in a 50.4% adjustment in the air window area. The left-wing fan power decreased from 131.72 kW to 97.95 kW (a reduction of 25.6%), and the right-wing fan power decreased from 188.22 kW to 146.62 kW (a reduction of 22.1%), achieving a total energy-saving rate of 23.56%. Actual application results in a coal mine demonstrated that the AROA-based algorithm reduced the fan airflow by 11.2%, while the fan air pressure decreased by 10.1%, ultimately achieving a 20.7% reduction in power consumption. The precise wind allocation scheme determined by the AROA-based algorithm reduced fan air pressure by 10.1%, fan airflow by 11.2%, and power by 20.7%.