Abstract
Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.
Highlights
Iron ore is the main raw material for blast furnace smelting
The results demonstrate that the ability of principal component analysis (PCA) to identify the Au emission line is highly related to Laser-induced breakdown spectroscopy (LIBS) spectral width
The results show that LIBS coupled with the least squares-support vector regression (LS-SVR) can accurately and quantitatively analyze elements in complex matrices [30]
Summary
Iron ore is the main raw material for blast furnace smelting. The quality and type of iron ore is closely related to the smelting process and economic indicators. The accurate classification of iron is of great significance to the acquisition of raw materials in the metallurgical industry. Laser-induced breakdown spectroscopy (LIBS) is a type of atomic emission spectroscopy, which generates plasma by focusing laser pulse on the surface of sample and collects the plasma through a high-sensitivity spectrometer. LIBS has shown great potential in qualitative and quantitative analysis. It has many advantages: in-situ, no sample preparation, high sensitivity, and almost nondestructive to sample. Mining the acquired spectral data is a hot trend for future LIBS application
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