Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization

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 arc 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 R-2 of Cat-fish PSO-support vector regression (SVR) is greater than 0.95, the mean value of relative standard deviation R-SD is 3.53%, and the root-mean-square error R-MSE 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.