变量重要性-反向传播人工神经网络辅助激光诱导击穿光谱测定铁矿石中硅、铝、钙和镁含量

doi:10.3964/j.issn.1000-0593(2023)10-3132-11

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Abstract The rapid and accurate determination of calcium, magnesium, aluminium and silicon content in iron ore plays an important role in iron ore quality assessment. The accurate determination of calcium (CaO), magnesium (MgO), aluminium (Al₂O₃) and silicon (SiO₂) in iron ore using laser-induced breakdown spectroscopy (LIBS) remains a challenge due to the overfitting of multivariate analysis methods and matrix effects between different types of samples. In this paper, variable importance-back propagation artificial neural network (VI-BP-ANN) assisted LIBS was used for the first time to quantify the content of SiO₂, Al₂O₃, CaO and MgO in iron ore. In this study, LIBS spectra of 12 representative samples of 244 batches of iron ore were collected, spectral pre-processing methods were optimised, the importance of LIBS spectral features was measured using random forest (RF), RF model parameters were optimised using out-of-bag (OOB) errors, and variable importance thresholds were used to optimise the input variables for the BP-ANN calibration model. The variable importance thresholds and the number of neurons were optimised by five-fold cross-validation (5-CV) of the coefficient of determination (R²) and root mean square error (RMSE). The results showed root mean square error of prediction (RMSEP) for the SiO₂, Al₂O₃, CaO, MgO content of the test samples were 0.372 3 wt%, 0.129 8 wt%, 0.052 4 wt% and 0.149 0 wt% respectively, with R² of 0.977 1, 0.950 4, 0.987 8 and 0.997 7, respectively. Compared to using the same preprocessing method as input to the three PLS, SVM and RF models, the VI-BP- ANN model showed excellent performance in both the calibration dataset and prediction dataset. The results indicate that the combination of LIBS and VI-BP-ANN has the potential to achieve fast and accurate prediction of calcium, magnesium, aluminium and silicon content of iron ore in practical application.

Key words：Iron ore; Back propagation artificial neural network; Variable importance; Quantitative analysis; Laser-induced breakdown spectroscopy

Received: 2022-06-25 Accepted: 2023-04-23

ZTFLH:

O657.319

Corresponding Authors: WU Xiao-hong E-mail: wuxiaohong_2196@163.com

Cite this article:

LIU Shu,JIN Yue,SU Piao, et al. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)10-3132-11 OR https://www.gpxygpfx.com/EN/Y2023/V43/I10/3132

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