Application Of Particle Swarm Optimization Algorithm In Air Volume Optimization Of Mine Ventilation Network

Mine ventilation plays a vital role in mine safety.It is hot and difficult in the research field of mine ventilation safety and energy saving to determine an air control scheme which can not only meet the ventilation needs of the underground tunnels and the limitations of the coal mine production conditions,but also avoid fits of shake of fans and spend least fees in ventilation.This paper elaborates the basic theories and laws in the field of mine ventilation,studies the air supply equipment and air conditioning facilities of the ventilation system,and discusses how to use the improved particle swarm optimization algorithm to optimize and control the air resistance and air volume of each branch of the underground mine in order to ensure that the coal mining work is more energy-saving,safe and efficient.In terms of optimization of branch air volume of mine ventilation network,achieving the minimum total power of mine ventilation network is the goal.Based on such constraints as the balance equation of wind volume,the balance equation of wind pressure,branch resistance equation and fan performance curve equation,a multi-group adaptive particle swarm optimization algorithm(MA-PSO)is proposed to search the optimal for mine ventilation network.First,conducting initialization preprocessing on the randomly generated populations,rank the adaptive value from high to low,divide the population into five subpopulations with the local optimal solution after preprocessing as the center and the average value of the Euclidean distance between local optimal solution and other particles as the radius,and then introduce the topological terms and population exchange factor into the speed updating formula,searching in the solution space with the population as the unit to ensure the diversity of the population,thus to accelerate the population evolution and algorithm convergence;Finally,the global optimization ability and self-learning ability of the algorithm are improved by increasing the adaptive inertia weight and eliminating useless particles.Practice proves that the performance of multi-group adaptive particle swarm optimization is balanced,there is no obvious shortboard when optimizing mine ventilation network,and the solution speed is fast and the convergence accuracy is high.The total power consumed by the optimized ventilation system is 26.78% lower than that of the previous,6.2 % better than that by traditional genetic algorithm,2.4% better than that by immune genetic algorithm,presenting a remarkable energy-saving effect.Finally,the paper discusses how to apply the improved particle swarm optimization algorithm to the actual production of coal mine.In view of the prominent advantages of the complex intelligent optimization algorithm written and calculated by matlab,it is combined with industrial PLC through OPC protocol to realize real-time acquisition,calculation and display of all kinds of ventilation parameters in coal mines.At the same time,the automatic modeling of ventilation network and the call of MA-PSO optimization algorithm are also carried out,and the online optimization and control of ventilation network volume is finally completed in collaboration with each other,guiding the actual mining of coal mine.The paper has certain reference value for the construction of intelligent mine ventilation system,which can make coal mine ventilation system more efficient and energy-saving.