In-situ monitoring and characterization of the component analysis of steel at elevated temperature environment of a steel industry using stand-off LIBS

Abstract

Chemical component analysis is an indispensable part of steel analysis. At present, the remote intelligent detection of the component analysis of steel needs to be further developed. In this study, laser‐induced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real‐time detection and steel analysis of steel at elevated temperature environment of a steel industry. Representative steel samples are selected for spectral analysis. The characteristic spectral lines of different elements observed in the spectra. By comparing the spectra of different samples, the differences among them discussed. First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in two dimensional and three-dimensional space and different kinds of the constituent elements of steel are classified successfully. Then, combined with error back propagation training artificial neural network, the steel identification model was established, and the recognition rate can be improved. The results show that LIBS is a powerful tool for remote intelligent real-time detection and classification, and has great application prospects in the chemical component analysis of steel at elevated temperature environment in the future.