Fei Yuan, An-jun Xu, and Mao-qiang Gu, Development of an improved CBR model for predicting steel temperature in ladle furnace refining, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1321-1331. https://doi.org/10.1007/s12613-020-2234-6
Cite this article as:
Fei Yuan, An-jun Xu, and Mao-qiang Gu, Development of an improved CBR model for predicting steel temperature in ladle furnace refining, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1321-1331. https://doi.org/10.1007/s12613-020-2234-6
Research Article

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

+ Author Affiliations
  • Corresponding author:

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

  • Received: 31 July 2020Revised: 30 November 2020Accepted: 2 December 2020Available online: 8 December 2020
  • In the prediction of the end-point molten steel temperature of the ladle furnace, 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 based on heat transfer calculation (CBR-HTC) was established through the nonlinear processing of these factors with software Ansys. The results showed that the CBR-HTC model improves the prediction accuracy of end-point molten steel temperature by 5.33% and 7.00% compared with the original CBR model and 6.66% and 5.33% compared with the back propagation neural network (BPNN) model in the ranges of [−3, 3] and [−7, 7], respectively. It was found that the mean absolute error (MAE) and root-mean-square error (RMSE) values of the CBR-HTC model are also lower. It was verified that the prediction accuracy of the data-driven model can be improved by combining the mechanism model with the data-driven model.
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