• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 8, 2332 (2021)
Wen-qiong ZHU*, Mu-chun ZHOU*;, Qi ZHAO, and Jun LIAO
Author Affiliations
  • School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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    DOI: 10.3964/j.issn.1000-0593(2021)08-2332-05 Cite this Article
    Wen-qiong ZHU, Mu-chun ZHOU, Qi ZHAO, Jun LIAO. End-Point Prediction of BOF Steelmaking Based on Flame Spectral Feature Selection Using WCARS-ISPA[J]. Spectroscopy and Spectral Analysis, 2021, 41(8): 2332 Copy Citation Text show less
    Furnace mouth flame spectrum data sets
    Fig. 1. Furnace mouth flame spectrum data sets
    Flowchart of WCARS
    Fig. 2. Flowchart of WCARS
    Variation of RMSE with the number of windows
    Fig. 3. Variation of RMSE with the number of windows
    Rough selection result of WCARS
    Fig. 4. Rough selection result of WCARS
    Variation of RMSE with the number of selected variables(a): ISPA; (b): SPA
    Fig. 5. Variation of RMSE with the number of selected variables
    (a): ISPA; (b): SPA
    Selection result of characteristic wavelengths with WCARS-ISPA
    Fig. 6. Selection result of characteristic wavelengths with WCARS-ISPA
    Training results of the WCARS-ISPA model
    Fig. 7. Training results of the WCARS-ISPA model
    模型特征
    波长数
    平均
    误差/%
    命中率
    /%
    运行
    时间/s
    WCRAS-ISPA101.413 290.630.019 679
    全光谱3 6483.369 177.130.822 247
    WCARS4342.135 082.370.103 025
    SPA101.881 584.020.017 702
    CARS-SPA81.820 986.770.017 809
    Table 1. Prediction results of different models
    Wen-qiong ZHU, Mu-chun ZHOU, Qi ZHAO, Jun LIAO. End-Point Prediction of BOF Steelmaking Based on Flame Spectral Feature Selection Using WCARS-ISPA[J]. Spectroscopy and Spectral Analysis, 2021, 41(8): 2332
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