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激光誘導擊穿光譜技術在提高礦冶分析準確度的研究進展

Research progress on laser-induced breakdown spectroscopy for improving the accuracy of mining and metallurgical analysis

  • 摘要: 激光誘導擊穿光譜法(LIBS)是一種基于原子發射光譜的多元素分析方法,具有快速、準確、無需復雜的樣品制備和遠程分析的優點. 然而,由于礦石、冶金樣品化學成分的復雜性和多樣性,干擾信號多,以及激光光譜的譜線維度較高和自吸收效應嚴重,LIBS技術在礦冶領域定性、定量分析的準確性受到了一定影響. 本文綜述了LIBS在礦冶領域3種信號增強方法,分別是雙脈沖、納米粒子增強和空間約束,以及綜述了降噪、歸一化和自吸收校正3種光譜預處理方法. 此外,為提高定性、定量模型的泛化能力和分析的準確性,人們在模型算法和參數優化做了大量的工作. 簡要概述了主成分分析、偏最小二乘判別分析、支持向量機、隨機森林和人工神經網絡5種LIBS定性分析建模方法在礦石、冶金樣品中的應用,以及概述了多元線性回歸、偏最小二乘法、支持向量機、人工神經網絡和自由定標法5種定量分析建模方法在礦石、冶金樣品中的應用成果,并對LIBS技術未來在礦冶分析領域的發展進行了展望.

     

    Abstract: Laser-induced breakdown spectroscopy (LIBS) is a type of atomic emission spectroscopy for multi-element analysis. This analysis is rapid and accurate, has a simple sample preparation, and realizes remote analysis. However, the accuracy of the qualitative and quantitative LIBS analysis methods in the field of mining and metallurgy has suffered from the complexity and diversity of the chemical composition of ore and metallurgical samples, interference signals, high dimension of the laser spectrum line, and severe self-absorption effect. To enhance the accuracy of LIBS analysis in the mining and metallurgy field, researchers have conducted numerous research on signal enhancement, spectral pretreatment, and modeling methods. In this review, three signal enhancement methods of LIBS in mining and metallurgy are evaluated: double pulse, nanoparticle enhancement, and space constraint. To avoid noise interference, overfitting, and “self-erosion,” three spectral preprocessing methods, including noise reduction, normalization, and self-absorption correction, are also reviewed. Moreover, to improve the generalization ability and analysis accuracy of the qualitative and quantitative modeling methods, extensive research has been conducted on model algorithms and parameter optimization. This paper briefly outlines the application of five typical LIBS qualitative analysis modeling methods in ore and metallurgical samples: principal component analysis method, partial least squares discriminant analysis method, support vector machine, random forest, and artificial neural network, and application results of five quantitative analysis modeling methods in ore and metallurgical samples: multiple linear regression method, partial least square method, support vector machine, artificial neural network, and free calibration method. Currently, light element ores, such as phosphate and lithium ores, rare earth and scattered elements, and the combined use of instruments are rarely investigated using LIBS; thus, future developments in LIBS technology for mineral and metallurgical analysis should mainly focus on the following aspects: (1) Research on LIBS online monitoring technology and suitable instrumentation because the application of online real-time and in situ monitoring analysis in the mining and metallurgical process has not been fully achieved. (2) Application of this method for the rapid analysis of light elements and complex ore and metallurgical samples, especially for online analysis, under special environmental conditions. (3) Improvement in the accuracy of the LIBS analysis and its application range in combination with other analytical techniques, such as Raman spectroscopy and infrared spectroscopy.

     

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