高精度煤质成分快速定量分析LIBS系统研究
Study on LIBS system for high-precision coal components rapid and quantitative analysis
刘树林 1王猛 1李安 2张颖 2刘晓东 2刘瑞斌2
作者信息
- 1. 西安科技大学 机械工程学院,陕西 西安 710054
- 2. 北京理工大学 物理学院,北京 100081
- 折叠
摘要
煤质成分的快速、高精度定量分析是工厂对能源高效利用的重要环节.本文通过利用搭建的激光诱导击穿技术煤质快速分析仪,对所采集煤样工业特性固定碳(FixC),灰分(Ash),干燥无灰基的挥发分(Vdaf),热值(Q)进行了定量测量,并对测得的数据与实验室人工测量数据进行对比分析,检验得出该方法在煤质分析方面准确快速.通过对设备稳定性和动态精密度的测试,结果表明了快速煤质分析仪具有较高的稳定性,满足国标规定值.在预测灰分值分布跨度为5%～60%的煤样时,通过增加样品量,增加模型中对大灰分煤样的预测权重,实现预测均方根误差RMSEP<1%的预测精度,挥发分和全硫的RMSEP<1%,热值的RMSEP<0.18 MJ/kg.预测结果均达到工业分析要求,可以满足工业现场的应用需求,具有应用于煤质在线检测方面的广阔前景.
Abstract
The rapid and high-precision quantitative analysis of coal quality components is an important link for factories to efficiently utilize energy.This article quantitatively measured the industrial characteris-tics of fixed carbon(FixC),ash(Ash),volatile matter(Vdaf),and calorific value(Q)of the collected coal samples using our laser induced breakdown technology coal quality rapid analyzer.The measured data were compared with manual measurement data in the laboratory,and it was verified that this method is ac-curate and fast in coal quality analysis.Through testing the stability and dynamic precision of the equip-ment,the results indicate that the rapid coal quality analyzer has high stability and meets the national stan-dard requirements.When predicting coal samples with a distribution span of 5%～60%ash content,by in-creasing the sample size and increasing the prediction weight of high ash content coal samples in the mod-el,the prediction accuracy of root mean square error RMSEP<1%,volatile matter and total sulfur RM-SEP<1%,and calorific value RMSEP<0.18 MJ/kg can be achieved.The predicted results all meet the requirements of industrial analysis and can meet the application needs of industrial sites,with broad pros-pects for application in online coal quality detection.
关键词
激光诱导击穿光谱/定量分析/快速煤质分析/稳定性Key words
Laser-Induced Breakdown Spectroscopy(LIBS)/quantitative analysis/rapid coal analyzer/stability引用本文复制引用
基金项目
科技部重点研发计划(2018YFC2001100)
出版年
2024
国家科技期刊平台
NSTL
万方数据