Optimization And Application Of Underground Mine Ventilation System Based On Improved Sparrow Search Algorithm

The ventilation system is a crucial part of the mine production system,playing an e xtremely important role in ensuring mine safety and preventing fire and explosion accid ents.In order to improve the efficiency of mine ventilation,achieve on-demand air distri bution,energy conservation and consumption reduction in the ventilation system,this pa per proposes a mine ventilation network optimization method based on the Sparrow Sea rch Algorithm(ELFASSA).By optimizing the energy consumption objective function of the fan,the air volume and resistance of each tunnel are calculated,Corresponding mea sures shall be taken to regulate the air volume,so as to ensure the safety,energy conserv ation and efficiency of underground job security.The main work content includes the fol lowing four parts:(1)In response to the current situation of underground mining,the significance of o ptimizing the mine ventilation system is elaborated.Summarized the solutions of domes tic and foreign scholars to the optimization problems of mine ventilation systems,and pr oposed the importance of optimizing underground mine ventilation networks.(2)Comprehensively analyzethe basic theories related to underground mine ventila tion network,with the optimization goal of minimizing the total power consumption of t he ventilation network,modeling based on the three basic laws of ventilation,and consid ering constraints such as the limitation of mine branch air volume and the rationality of f an operating conditions,establish a ventilation network optimization model.In order to ensure the accuracy and stability of the model solution,the external point penalty functi on method is used to add the nonlinear constraint conditions to the objective function thr ough the penalty function to complete the ventilation network constrained optimization modeling.(3)In response to the numerous constraints and the widespread occurrence of multi peak phenomena in the optimization process of underground mine ventilation network,the Sparrow Search Algorithm(SSA)is combined with the optimization problem of min e ventilation network.At the same time,in order to improve the performance of SSA in t he optimization of mine ventilation network,the algorithm was improved and optimized by introducing elite Reverse learning strategy,improved firefly disturbance strategy and dynamic boundary strategy to increase the number of subspaces in the search space,im prove the search range of sparrows,and effectively solve the problems of population div ersity decline and easy to fall into local extreme values in the later stage of algorithm op timization.Comparative experiments were conducted on the improved sparrow search al gorithm(ELFASSA),the basic sparrow search algorithm(SSA),and four advanced swa rm intelligence optimization algorithms.The experimental results showed that ELFASS A’s optimization speed and ability were superior to the other five comparative algorithm s.(4)ELFASSA was applied to the optimization of actual ventilation systems,and an implementation plan for optimizing and regulating mine air volume was designed and v erified.The experimental results have verified the feasibility of the ELFASSA based opt imization method for mine ventilation network with the goal of seeking minimum energ y consumption under the conditions of meeting ventilation needs.It has certain reference significance for improving the ventilation efficiency of mine ventilation systems,reduci ng fan energy consumption,and building smart mines.