摘要
针对掘进工作面粉尘问题,设计了一种基于机器学习的智能降尘系统。该系统通过部署GKT5LX(A)型矿用设备开停传感器、GLR矿用本安型流量开关传感器、CCZ1000全自动粉尘测量仪传感器网络来监测工作面的各装置流量以及粉尘浓度,并利用机器学习算法分析数据,预测粉尘扩散并优化降尘策略。系统采用随机森林算法自适应调整喷雾装置的工作参数,实现精准降尘。在东滩煤矿的应用试验中,该系统最优状态下粉尘浓度降低了81.24%,极大地改善了工作环境。
Aiming at the dust problem of heading face,an intelligent dust reduction system based on machine learning is designed.The system monitors the flow and dust concentration of each device in the working face by deploying GKT5LX(A)type mine equipment on-off sensor,GLR mine intrinsically safe flow switch sensor,CCZ1000 automatic dust measuring instrument sensor network,and uses machine learning algorithm to analyze the data,predict dust diffusion and optimize dust reduction strategy.The system uses the random forest algorithm to adaptively adjust the working parameters of the spray device to achieve accu⁃rate dust reduction.In the application test of Dongtan Coal Mine,the dust concentration in the optimal state of the system is reduced by 81.24%,which greatly improves the working environment.
作者
刘渠
李臣华
张振国
李群
孙航
LIU Qu;LI Chenhua;ZHANG Zhenguo;LI Qun;SUN Hang(Dongtan Coal Mine of Yankuang Energy Group Co.,Ltd.;Xuzhou Jiangmei Technology Co.,Ltd.;Jining Energy Bureau)
出处
《现代矿业》
CAS
2024年第8期11-14,18,共5页
Modern Mining
关键词
智能降尘
掘进面
粉尘监测
机器学习
intelligent dust reduction
excavation face
dust monitor
machine learning
