论文摘要
Laser-induced breakdown spectroscopy（LIBS） is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve（CC） and support vector regression（SVR） methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error（ARE）,relative standard deviation（RSD） and root mean squared error of prediction（RMSEP） were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
论文目录
文章来源
类型: 期刊论文
作者: 贾军伟,付洪波,侯宗余,王华东,倪志波,董凤忠
来源: Plasma Science and Technology 2019年03期
年度: 2019
分类: 基础科学,工程科技Ⅰ辑,工程科技Ⅱ辑
专业: 无机化工,建筑科学与工程
单位: Anhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences,University of Science and Technology of China,State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University
基金: supported by National Natural Science Foundation of China (Grant Nos. 61505223,41775128),the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. Y03RC21124),the External Cooperation Program of Chinese Academy of Sciences (Grant No. GJHZ1726),the project of China State Key Lab. of Power System (Grant Nos. SKLD18KM11,SKLD18M12)
分类号: TQ172.1
页码: 27-34
总页数: 8
文件大小: 1462K
下载量: 25