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Predicting Temperature of Molten Steel in LF-Refining Process Using IF–ZCA–DNN Model

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Abstract

Controlling the temperature of molten steel in ladle furnace (LF)-refining process is one of the main tasks to ensure that the steelmaking-continuous casting process runs smoothly. In this work, a hybrid model based on metallurgical mechanism, isolation forest (IF), zero-phase component analysis whitening (ZCA whitening), and a deep neural network (DNN) was established to predict the temperature of molten steel in LF-refining process. The metallurgical mechanism, Pearson correlation coefficient, ZCA whitening, IF, and t-distributed stochastic neighbor embedding (t-SNE) were used to obtain the main factors affecting the temperature, analyze the correlation between two random variables, eliminate the correlation among the input variables, reduce the abnormal data of the original datasets, and visualize high-dimensional data, respectively. The single-machine-learning (ML) models, ZCA–ML models, and IF–ZCA–DNN model were comparatively examined by evaluating the coefficient of determination (R2), root-mean-square error (RMSE), mean absolute error (MAE), and hit ratio. The optimal structure of IF–ZCA–DNN model had 4 hidden layers, 45 hidden layer neurons, a learning rate of 0.03, regularization coefficient of 2 × 10−4, batch size of 128, leaky-rectified linear unit activation function, and an optimization algorithm of mini-batch stochastic gradient descent with momentum. The R2, RMSE, and MAE of the IF–ZCA–DNN model were 0.916, 2.827, and 2.048, respectively. Meanwhile, the prediction hit ratio for the temperature of IF–ZCA–DNN model in the error ranges of [− 3, 3], [− 5, 5], and [− 10, 10] were 77.9, 92.3, and 99.6 pct, respectively. This study will be beneficial to realize precise control of temperature of molten steel in LF-refining process.

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Acknowledgments

This project is funded by the National Natural Science Foundation of China, under Grant Number 51974023, and the funding of State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, under Grant Number 41621005.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, P.R. China

    Zi-cheng Xin, Jiang-shan Zhang & Qing Liu

  2. Tangshan Branch, Hebei Iron and Steel Co., Ltd., Tangshan, Hebei, P. R. China

    Jun-guo Zhang, Jin Zheng & Yu Jin

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  1. Zi-cheng Xin
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Correspondence to Jiang-shan Zhang or Qing Liu.

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Xin, Zc., Zhang, Js., Zhang, Jg. et al. Predicting Temperature of Molten Steel in LF-Refining Process Using IF–ZCA–DNN Model. Metall Mater Trans B 54, 1181–1194 (2023). https://doi.org/10.1007/s11663-023-02753-0

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