陕西煤炭2024，Vol.43Issue(8)：52-56,83,6.DOI:10.20120/j.cnki.issn.1671-749x.2024.0810
基于猎人猎物优化算法的粉尘浓度BP神经网络预测模型
BP neural network prediction model of dust concentration based on Hunter-Prey optimization algorithm
徐景果 1张宇轩 2王飞 1史默 1李永永1
作者信息
- 1. 陕西彬长文家坡矿业有限公司,陕西咸阳 713500
- 2. 渭南陕煤启辰科技有限公司,陕西西安 710100
- 折叠
摘要
Abstract
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.关键词
粉尘浓度/接触角/BP神经网络/泡沫降尘/预测模型Key words
dust concentration/contact angle/BP neural network/foam dust reduction/prediction model分类
矿业与冶金引用本文复制引用
徐景果,张宇轩,王飞,史默,李永永..基于猎人猎物优化算法的粉尘浓度BP神经网络预测模型[J].陕西煤炭,2024,43(8):52-56,83,6.
