摘要
露天矿粉尘污染会对矿区生态环境和员工身体健康造成严重危害,准确预测其质量浓度对大气污染防治具有重要的指导作用。研究提出一种基于灰狼算法优化随机森林(GWO-RF)的粉尘质量浓度预测模型,并在该模型的特征变量中加入矿区卡车尾气排放因素,考虑计算卡车尾气中的颗粒污染物的含量。研究结果表明,采用移动平均法对粉尘质量浓度进行降噪处理,有效改善了预测效果;与其他传统模型对比,GWO-RF模型的拟合能力和预测的准确率最高。
Dust pollution in open-pit mines has caused serious harm to the ecological environment of mining areas and the health of employees.Accurately predicting its mass concentration plays an important guiding role in the prevention and control of air pollution.A dust mass concentration prediction model was proposed based on grey wolf optimization algorithm optimized random forest(GWO-RF).This model incorporates mining truck exhaust emission factors into the characteristic variables,with consideration of calculating the content of particulate pollutants in truck exhaust.The research results indicate that using the moving average method for noise reduction of dust mass concentration effectively improves the prediction effect.Compared with other traditional models,the GWO-RF model has the highest fitting ability and prediction accuracy.
作者
顾清华
王晨曦
王倩
刘敏
GU Qinghua;WANG Chenxi;WANG Qian;LIU Min(School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an,Shaanxi 710055,China;Xi'an Key Laboratory of Perception,Computing and Decision Making for Intelligent Industry,Xi'an,Shaanxi 710055,China;School of Management,Xi'an University of Architecture and Technology,Xi'an,Shaanxi 710055,China)
出处
《矿业研究与开发》
CAS
北大核心
2024年第4期161-167,共7页
Mining Research and Development
基金
国家自然科学基金项目(51774228,52074205)
陕西省杰出青年基金项目(2020JC-44)。
关键词
露天煤矿
粉尘质量浓度预测
尾气污染
随机森林
灰狼优化算法
Open-pit coal mine
Dust mass concentration prediction
Exhaust pollution
Random forest
Grey wolf optimizationalgorithm
作者简介
顾清华(1981-),男,山东诸城人,博士,教授,主要研究方向为资源系统优化与管理,E-mail:qinghuagu@126.com;通信作者:王晨曦(1997-),女,陕西西安人,硕士研究生,主要从事智慧矿山系统方向的研究,E-mail:601704360@qq.com。
