基于神经网络的煤矿粉尘浓度预测方法及粉尘仪表检定装置研究  

Research on neural network-based prediction method for coal mine dust concentration and dust instrument calibration device

作　　者：葛勇 Ge Yong(CCTEG Beijing Huayu Engineering Co.,Ltd.,Beijing 100000,China)

机构地区：[1]中煤科工集团北京华宇工程有限公司,北京100000

出　　处：《能源与环保》2025年第3期212-218,226,共8页CHINA ENERGY AND ENVIRONMENTAL PROTECTION

基　　金：煤炭科学技术研究院有限公司科技发展基金项目(2024ZDI-16)。

摘　　要：为进一步探究如何提高煤矿粉尘检定的准确性,研究结合当前煤矿粉尘检定工作中存在的精度不足等现状问题加以展开。首先,搭建本次粉尘仪表检定装置的整体架构,而后从软件层面入手,对该粉尘仪表检定装置的粉尘浓度检测方法和粉尘浓度预测方法的相关算法流程进行设计,并通过实验验证了上述方法的准确性;在此基础上,进一步通过数值模拟分析方法,对该粉尘仪表检定装置的运行参数进行优化,确定优化后的运行参数组合为风速2 m/s,检测面距离发射源5 m,同时设置发尘质量流率为0.5 g/s;最后,将优化参数的粉尘浓度检定装置投入现场实验。结果显示,该装置误差较小,具有潜在应用价值。To further explore how to improve the accuracy of coal mine dust calibration,this study combines the current problems of insufficient accuracy in coal mine dust calibration work.Firstly,the overall architecture of the dust instrument calibration device was constructed.Then,from the software level,the relevant algorithm flow of the dust concentration detection method and dust concentration prediction method of the dust instrument calibration device was designed,and the accuracy of the above method was verified through experiments.On this basis,further optimization of the operating parameters of the dust instrument calibration device was carried out through numerical simulation analysis.The optimized operating parameter combination was determined to be a wind speed of 2 m/s,with a detection surface distance of 5 m from the emission source,and a dust mass flow rate of 0.5 g/s.Finally,the dust concentration calibration device with optimized parameters was put into on-site experiments,and the results showed that the device had small errors and potential application value.

关 键 词：神经网络 煤矿 粉尘浓度 仪表检定装置 

分 类 号：TD714.3[矿业工程—矿井通风与安全]