Abstract：In the field of nonferrous smelting, element component detection is an important part to ensure the smelting quality. At present, domestic non-ferrous smelting enterprises mostly use X-ray fluorescence method for detection, which requires sample preparation, resulting in no real-time feedback of smelting state, which seriously affects the optimization of smelting process. Unsupervised data mining algorithm assisted laser-induced breakdown spectroscopy was used to analyze the spectral structure of copper smelting. In the experiment, four kinds of copper smelting materials were first selected as experimental samples, and then 18750 spectral data were obtained by LIBS instrument excitation samples. All spectra were analyzed by blind source separation technique, and finally three characteristic spectra were extracted. Further study found that the three characteristic spectra had a one-to-one relationship with the spectra of Cu, Fe and Ca. On this basis, the quantitative evaluation index of LIBS was proposed, and the quantitative results showed that the decomposition model could achieve a high score for all 18750 spectra, indicating that the copper smelting spectrum could be well reconstructed by the three characteristic spectra, that was, the copper smelting spectrum had a significant spectral structure. The above conclusions had important research value in practical applications, and could be used for rapid spectral evaluation, abnormal spectral elimination, spectral signal purification, element spectral line selection, qualitative/semi-quantitative analysis of samples, etc., laying a foundation for the application of LIBS technology in the analysis of copper smelting components on line.
郭杰, 潘从元, 徐勇. 无监督数据挖掘辅助激光诱导击穿光谱用于铜冶炼光谱结构解析[J]. 冶金分析, 2020, 40(12): 59-65.
GUO Jie, PAN Cong-yuan, XU Yong. Unsupervised data mining assisted laser-induced breakdown spectroscopy was used to analyze the spectral structure of copper smelting. , 2020, 40(12): 59-65.
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