基于循环变量筛选非线性偏最小二乘的LIBS铁矿浆定量分析  

Quantitative Analysis of Laser-Induced Breakdown Spectroscopy Iron Ore Slurry Based on Cyclic Variable Filtering and Nonlinear Partial Least Squares

作　　者：尚栋 孙兰香[1,2,3] 齐立峰[1,2,3] 谢远明 陈彤 Shang Dong;Sun Lanxiang;Qi Lifeng;Xie Yuanming;Chen Tong(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang,Liaoning 110016,China;Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Liaoning 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,,Liaoning 110016,China;University of Chinese Academy of Sciences,Beijing100049,China;Shenyang University of Chemical Technology,Shenyang,Liaoning110412,China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]中国科学院网络化控制系统重点实验室,辽宁沈阳110016 [3]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169 [4]中国科学院大学,北京100049 [5]沈阳化工大学,辽宁沈阳110142

出　　处：《中国激光》2021年第21期165-173,共9页Chinese Journal of Lasers

基　　金：国家重点研发计划(2016YFF0102502);中国科学院前沿科学重点研究计划(QYZDJ-SSW-JSC037);中国科学院青年创新促进会和辽宁省“兴辽英才计划”资助项目(XLYC1807110)。

摘　　要：激光诱导击穿光谱(LIBS)技术因其在线、原位、多元素同时测量等优点,在物质成分检测上得到广泛应用。但是,LIBS技术常受到自吸收及基体效应的干扰,分析的准确度较低,同时,随着光谱仪分辨率的不断提高,数据维度越来越高,其中包括大量对成分分析无用的冗余信息,这就增加了建模的复杂度。为了降低建模的复杂度,减少光谱数据维度以提取最有用的光谱信息,同时减少自吸收及基体效应的非线性干扰对定量分析精度的影响,在传统偏最小二乘(PLS)方法的基础上,提出了利用循环筛选特征变量来校正自吸收及基体效应影响的非线性PLS模型。以铁精矿矿浆样本为分析对象,结果表明,与传统PLS方法相比,所提出的基于循环变量筛选的非线性PLS模型的定量分析精度显著提高,测试样品的均方根误差(RMSE)从1.15%降到0.70%,决定系数R^(2)从0.51提高到0.86。Objective From iron ore to the final steel processing,accurate mineral content data is essential to maximize raw materials and energy accurately control the manufacturing.Mineral flotation is a beneficiation method in which target minerals and impurities are separated based on the physical and chemical properties of target minerals and impurities and then extracted from the original ore slurry.Content of iron ore slurry directly affects the flotation effect and quality and output benefit of the final product.Therefore,conducting an accurate quantitative analysis of the iron ore slurry composition is essential.Laser-induced breakdown spectroscopy(LIBS)has been widely used to detect material composition owing to its advantages such as online,in situ,and simultaneous measurement of multiple elements.However,self-absoprtion and matrix effects in LIBS affect the accuracy of the analysis.Simultaneously,with the continuous improvement of the spectrometer’s resolution,the data dimension is increasing,including a large amount of redundant information that is unnecessary for component analysis.When using PLS and LIBS for quantitative analysis,the existing research uses spectral line feature selection to reduce dimensionality and nonlinear correction to make improvements separately.To simultaneously reduce the data dimension and correct the nonlinear problem of the data itself,we build a nonlinear PLS model to reduce the influence of self-absorption and matrix effects on the accuracy of quantitative analysis.In addition,the characteristic variables are cyclically filtered to reduce the modeling complexity.Methods PLS is widely used in the quantitative analysis of material components,but as a linear processing method,it cannot resolve the nonlinear effects of self-absorption and matrix effects on the spectrum,reducing the accuracy of quantitative analysis.The characteristic spectrum line n-order polynomial form was proposed to be added to the PLS model.Thus,we can reduce the dimensionality of the data to extract the most u

关 键 词：光谱学 激光诱导击穿光谱 非线性偏最小二乘模型 变量筛选 自吸收效应 基体效应 

分 类 号：O657.31[理学—分析化学]