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

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Abstract

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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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 51674030) and the Fundamental Research Funds for the Central Universities (Nos. FRF-TP-18-097A1 and FRF-BD-19-022A).

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Authors and Affiliations

  1. School of Metallurgical and Ecological Engineering, University of Science and Technology Bei**g, Bei**g, 100083, China

    Fei Yuan, An-jun Xu & Mao-qiang Gu

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  1. Fei Yuan
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  2. An-jun Xu
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  3. Mao-qiang Gu
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Correspondence to An-jun Xu.

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Yuan, F., Xu, Aj. & Gu, Mq. Development of an improved CBR model for predicting steel temperature in ladle furnace refining. Int J Miner Metall Mater 28, 1321–1331 (2021). https://doi.org/10.1007/s12613-020-2234-6

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  • DOI: https://doi.org/10.1007/s12613-020-2234-6

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