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Article Navigation >  International Journal of Minerals, Metallurgy and Materials  > 2020 > Accepted Manuscript

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Research Article

Development of an improved CBR model for predicting steel temperature in LF refining

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  • Received: 31 July 2020; Revised: 30 November 2020; Accepted: 2 December 2020; Available online: 8 December 2020
  • In the prediction of end-point molten steel temperature of LF, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model (CBR_HTC) was established through the nonlinear processing of these factors by calculating the heat transfer of the ladle with software Ansys. The results show that CBR_HTC model improves the prediction accuracy of end-point molten steel temperature by 5.33% and 7.00% compared to original CBR model, and 6.66% and 5.33% compared to BPNN model in the range of [-3,3] and [-7,7]. The MAE and RMSE values of CBR_HTC model are also lower. It is verified that the prediction accuracy of the data-driven model can be improved by coupling the mechanism model with the data-driven model.
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Development of an improved CBR model for predicting steel temperature in LF refining

  • Corresponding author:

    An-jun Xu    E-mail: anjunxu@126.com

  • School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Received: 31 July 2020
    Revised: 30 November 2020
    Accepted: 2 December 2020
    Available online: 8 December 2020

Abstract: In the prediction of end-point molten steel temperature of LF, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model (CBR_HTC) was established through the nonlinear processing of these factors by calculating the heat transfer of the ladle with software Ansys. The results show that CBR_HTC model improves the prediction accuracy of end-point molten steel temperature by 5.33% and 7.00% compared to original CBR model, and 6.66% and 5.33% compared to BPNN model in the range of [-3,3] and [-7,7]. The MAE and RMSE values of CBR_HTC model are also lower. It is verified that the prediction accuracy of the data-driven model can be improved by coupling the mechanism model with the data-driven model.

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