激光与光电子学进展, 2020, 57 (5): 053002, 网络出版: 2020-03-05   

改进粒子群算法优化SVR的LIBS钢液元素定量分析 下载: 967次

Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization
作者单位
华北理工大学电气工程学院, 河北 唐山 063210
摘要
通过激光诱导击穿光谱(LIBS)对钢液表面的不同位置进行激发检测,对得到的光谱数据进行归一化预处理。通过主成分分析法筛选出4个代表性因素,将得到的4个因素作为输入信息,针对钢液中Mn、Ni、Cr和Si四种元素,训练并建立定标模型。利用Cat-fish 粒子群(PSO)算法选出最优参数值,最后用测试集来验证模型的预测效果。实验结果表明:Cat-fish PSO-支持向量回归(SVR)的决定系数R2大于0.95,相对标准偏差RSD均值为3.53%,均方根误差RMSE在1.5%以内;所提模型优于普通SVR预测模型,能够快速精确检测出元素含量。该研究为LIBS在线准确定量分析钢液元素提供了借鉴性较高的优化算法。
Abstract
The laser induced breakdown spectrum (LIBS) is used to excite and detect the different positions at liquid steel surface, and normalization pretreatment is performed for the spectral data. The four representative factors are screened out by principal component analysis and used as input information. Aiming at the four elements of Mn, Ni, Cr, and Si in liquid steel, the calibration model is trained and established, and the optimal parameter value is selected by Cat-fish particle swarm optimization (PSO) algorithm. Finally, the test set is used for verifying the prediction effect of the model. The experimental results show that the determination coefficient R2 of Cat-fish PSO-support vector regression (SVR) is greater than 0.95, the mean value of relative standard deviation RSD is 3.53%, and the root-mean-square error RMSE can be controlled within 1.5%. The proposed model is superior to the ordinary SVR prediction model, and it can detect the element content quickly and accurately. This study provides an optimization algorithm for the on-line and accurate quantitative analysis of liquid steel elements by LIBS, which has high reference value.

杨友良, 王禄, 马翠红. 改进粒子群算法优化SVR的LIBS钢液元素定量分析[J]. 激光与光电子学进展, 2020, 57(5): 053002. Youliang Yang, Lu Wang, Cuihong Ma. Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization[J]. Laser & Optoelectronics Progress, 2020, 57(5): 053002.

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