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Machine Learning Models for Predicting and Controlling the Pressure Difference of Blast Furnace

  • Applications of Machine Learning in Materials Development and Additive Manufacturing
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Abstract

The pressure difference is an important parameter to characterize the stability and smoothness of the blast furnace (BF) ironmaking process. The prediction and control of the pressure difference is the basic step to ensure the high efficiency, stability, low consumption, and smooth operation of a BF. Based on the collected industrial data, eight machine learning methods were used to build a prediction model for the pressure difference after 1 h (PD-1 h) in this article. In addition, a partial dependence plot (PDP) and Shapley additive explanations were used to visualize and quantitatively analyze the impact of features on PD-1 h. The simulation results show that the prediction accuracy of the support vector regression model and the CatBoost model for PD-1 h reaches 94.45% and 94.34%, respectively. PD, BP, and VBG are the major factors that affect PD-1 h. In addition, the method of controlling PD-1 h by adjusting operating parameters through 2D-PDP is given.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Project No.: 51904026] and the China Postdoctoral Science Foundation [Project No.: BX20200045].

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Authors and Affiliations

  1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Dewen Jiang, Zhenyang Wang, Kejiang Li, Jianliang Zhang & Song Zhang

  2. School of Chemical Engineering, The University of Queensland, St Lucia, QLD, 4072, Australia

    Jianliang Zhang

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  1. Dewen Jiang
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Correspondence to Zhenyang Wang.

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Jiang, D., Wang, Z., Li, K. et al. Machine Learning Models for Predicting and Controlling the Pressure Difference of Blast Furnace. JOM 75, 4550–4561 (2023). https://doi.org/10.1007/s11837-023-06094-w

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