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End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA

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Abstract

Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Therefore, research on BOF steelmaking modeling is very necessary. In this paper, a novel combination prediction model has been proposed, which consists of a time series prediction model and a compensation prediction model. Both models are established by k-nearest neighbor-based weighted twin support vector regression (KNNWTSVR) algorithm. By introducing Lévy flight algorithm and inertia weight, an improved algorithm of whale optimization algorithm (WOA) called Lévy flight WOA has been initially proposed to solve the optimization problem in the objective function of KNNWTSVR. The simulation results show that the proposed models are effective and feasible. Within different error bounds (0.005% for carbon content model and 10 °C for temperature model), the strike rates of carbon content and temperature both achieve 93%, and a double strike rate of 86% is obtained, which can provide a significant reference for real BOF applications, and the proposed method is also appropriate for the prediction models of other metallurgical applications.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61403177.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China

    Chuang Gao, Lidong Wang & Ming Chen

  2. School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China

    Minggang Shen

  3. Faculty of Engineering, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada

    Xiaoping Liu

Authors
  1. Chuang Gao
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  2. Minggang Shen
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  3. Xiaoping Liu
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  4. Lidong Wang
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  5. Ming Chen
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Corresponding author

Correspondence to Minggang Shen.

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Gao, C., Shen, M., Liu, X. et al. End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA. Trans Indian Inst Met 72, 257–270 (2019). https://doi.org/10.1007/s12666-018-1479-5

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  • DOI: https://doi.org/10.1007/s12666-018-1479-5

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