Abstract
In most cases, in situ online composition detection is highly desirable for the iron and steel industry. High-precision prediction of the iron content of an iron ore is quite difficult due to the complexity of ore composition. Herein, an online composition analysis system based on laser-induced breakdown spectroscopy (LIBS) is built up for real-time Fe concentration determination. Subsequently, using the support vector machine (SVM) combined with multivariate partial least-squares regression (PLSR) to establish a linear relationship between the spectral data and typical element content, the iron content in the standard ore tablet is used as the true value to train the model, and the iron content in the raw ore block is predicted, with an error of 1.6%. By studying the elemental distribution content of the raw ore with repeated laser ablation, it is found that the internal element content of the ore changes at different depths from the surface, and the element content quickly stabilizes. The results demonstrate that the method can accurately and effectively predict iron content online, allowing the application of online detection of industrial ore composition.
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