Research And Application Of Mine Ventilation Network Air Volume Optimization Based On Fireworks Algorithm

The mine ventilation system is responsible for the continuous delivery of fresh air to the mine,which is responsible for the safety of coal production and the personal safety of the miners,and plays an important role.Aiming at the problem of the variable demand of ventilation system and the prominent problems of ventilation energy consumption,this paper conducts air volume optimization research on mine ventilation network based on fireworks algorithm(FWA).To seek a scheme of controlling the wind which not only meets the ventilation safety requirements,but also realizes wind distribution on demand,energy saving and consumption reduction,so as to ensure the safety,energy saving and efficient operation of coal mining.Firstly,the lowest total power consumption of the ventilation network is taken as the optimization goal,and the basic laws of ventilation are taken as the modeling basis.In order to meet the demand of mine wind and the constraint of reasonable fan operating point,the nonlinear constraint problem is transformed by using the method of external penalty function,and the nonlinear unconstrained optimization model of ventilation network is established.On the basis of this model,the optimization method of mine ventilation network air volume based on FWA is compared and analyzed with traditional genetic algorithm and particle swarm optimization algorithm,and verify the feasibility and superiority of FWA in solving complex nonlinear optimization problems of ventilation networks.Secondly,in order to solve the problem that FWA is easy to be precocious and fall into the local optimal solution,the algorithm improvement is studied,and a kind of opposition-based enhanced fireworks algorithm(OBEFWA)is proposed.The algorithm adopts the strategy of uniform opposition-based initialization,which competes evenly distributed random populations with opposition-based populations,and selects the optimal initial population as the starting point of subsequent search.Then,the fireworks explosion radius is finely controlled,so that explosion radius of fireworks populations of different generations shows non-linear decline,and that of the same population generation adjusts adaptively.The minimum dynamic threshold is set to decrease waste of search resources.Finally,selection strategy of elite opposition-based learning is adopted to strengthen search for neighborhood of elite fireworks,so as to improve global exploration ability of the algorithm.Through comparison experiments with FWA,enhanced fireworks algorithm(EFWA),and multi-group adaptive particle swarm optimization algorithm(MA-PSO),it is proved that the method of mine ventilation network air volume optimization based on OBEFWA has better optimization ability and convergence accuracy.Finally,in order to realize the friendly combination of intelligent optimization algorithm and traditional industrial control,the mine intelligent ventilation experiment system is studied and designed.Applying OBEFWA to mine ventilation network optimization,the total ventilation energy consumption after the implementation of the optimization is reduced by 14.54 k W,and the energy saving effect is up to 20.46%.In addition,based on the study of the influence of adjustable branches on the optimization of ventilation network,the number of adjustable branches is gradually reduced,and OBEFWA is used for the optimal control of ventilation air volume.It is proved that the algorithm is effective to achieve the goal of wind distribution on demand,energy saving and consumption reduction under the limited adjustable branch,which can provide economic and reliable application technology guarantee for the construction of intelligent ventilation system in intelligent mines.