BP neural network prediction model of dust concentration based on Hunter-Prey optimization algorithm
In order to better predict the dust concentration after the treatment of foam dust reduction technology in coal mine,taking the 2# hazard treating roadway of 4-2 panel area of Wenjiapo Coal Mine as the engineering background,the dust concentration was predicted based on hunting algorithm-optimized BP neural network.Firstly,using the contact angle test,the concentration of foam foaming agent with better influence of dust reduction was determined as 0.5％,and the dust concentration of driving roadway at different conditions of parameters was measured.Taking the water pressure,wind pres-sure and initial dust concentration as three inputs,and the dust concentration in the roadway under different conditions as the output,we analyzed and compared the prediction accuracy and generalization capability of each algorithm.By comparison of the fitting degree of four kinds of neural networks'prediction models,the 3-9-1 structured HPO-BP neural network pre-diction model with the optimal fitting degree is more suitable for dust concentration prediction in the driving roadway.The study provides basis for adjusting the parameters of foam dust reduction in the future.
dust concentrationcontact angleBP neural networkfoam dust reductionprediction model
国家科技期刊平台
NSTL
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