Multi-objective optimization design of feed crushing process parameters
The purpose of the experiment was to optimize the process parameters of feed crushing,so as to improve the working performance of the crusher.Productivity and power consumption per ton of material were studied as optimization objectives.With spindle speed,screen diameter,material moisture content,feed rate,and return pipe diameter as optimization variables,the response surface test principle(BBD)was used to design the test and construct the data set.Based on Back-Propagation Neural Network(BPNN)and Particle Swarm Optimization-Back-Propagation Neural Network(PSO-BPNN),the multi-objective optimization model of feed crushing process parameters was established.The Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ)was used for multi-objective optimization,and the Pareto solution set was obtained.Through CRITIC-TOPSIS evaluation model,Pareto solution sets meeting the actual production requirements were screened.The results show that the error index of the BP neural network algorithm optimized by PSO is smaller and the prediction accuracy was higher,and the average optimization range of the pulverizer's productivity,power consumption per ton of material and particle size reached 61.07％,38.58％,and 54.31％,respectively.After the target optimization,the optimal process parameters of the crusher were combined with spindle speed of 2 697 r/min,screen aperture of 5.8 mm,material moisture content of 10％,feeding capacity of 19.3 kg/min,and diameter of return pipe of 67 mm.The results show that the productivity of pulverizer is increased by 5.92％and the power consumption per ton is reduced by 2.29％.
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