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A Prediction Model of Blast Furnace Slag Viscosity Based on Principal Component Analysis and K-Nearest Neighbor Regression

  • Machine Learning Applications in Advanced Manufacturing Processes
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Abstract

Viscosity is considered to be a significant indicator of the metallurgical property of blast furnace (BF) slag. However, a BF is a complicated black box so that the measurement of the viscosity has a large hysteresis. A prediction model for the viscosity based on machine learning, principal component analysis (PCA) and k-nearest neighbor (KNN) regression is presented in this article. First, the main influencing factors of the viscosity are analyzed and selected as the input of the model. Then, the two datasets are preprocessed by data normalization. In addition, the sample characteristics of the data are processed to be statistically irrelevant by PCA. Based on the above, the two datasets are applied to the PCA–KNN model and the support vector regression model, respectively. The results show that the predicted result using the PCA–KNN model is more accurate and reaching 99%.

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References

  1. G. Lei and L. Chaobin, Metall. Mater. Trans. B 45, 875 (2014).

    Article  Google Scholar 

  2. W.C. Chen and W.T. Cheng, Int. Commun. Heat Mass Transf. 71, 208 (2016).

    Article  Google Scholar 

  3. I. Matino, S. Dettori, V. Colla, V. Weber, and S. Salame, Energy Procedia 158, 4037 (2019).

    Article  Google Scholar 

  4. L.X. Li, D.X. Liu, C. Jia, and X.Z. Cheng, Neurocomputing 148, 209 (2015).

    Article  Google Scholar 

  5. H.G. Zhang, S. Zhang, Y.X. Yin, and X.Z. Chen, Int. J. Mach. Learn. Cybern. 9, 1697 (2018).

    Article  Google Scholar 

  6. S.C. Du, J. Bygd’en, and S. Seetharaman, Metall. Mater. Trans. B 25, 519 (1994).

    Article  Google Scholar 

  7. G. Urbain, Steel Res. Int. 58, 111 (1987).

    Article  Google Scholar 

  8. A. Kondratiev and E. Jak, Fuel 80, 1989 (2001).

    Article  Google Scholar 

  9. L. Zhang and S. Jahanshahi, Metall. Mater. Trans. B 29, 177 (1998).

    Article  Google Scholar 

  10. L. Takamichi, S. Hidenori, K. Yoshifumi, and S. Koichi, ISIJ Int. 40, S110 (2000).

    Article  Google Scholar 

  11. K.C. Mills and S. Sridhar, Ironmak. Steelmak. 26, 262 (1999).

    Article  Google Scholar 

  12. H.S. Ray and S. Pal, Ironmak. Steelmak. 31, 125 (2004).

    Article  Google Scholar 

  13. X.J. Hu, Z.S. Ren, G.H. Zhang, W. L.J. Wang and K.C. Chou, Int. J. Miner., Metall. Mater. 19, 1088(2012).

  14. Q.F. Shu, Steel Res. Int. 80, 107 (2010).

    Google Scholar 

  15. J.Q. An, J.L. Zhang, M. Wu, J.H. She, and T.K. Terano, Neurocomputing 315, 405 (2018).

    Article  Google Scholar 

  16. H. Zhou, C.J. Yang, W.H. Liu, and T. Zhuang, IFAC-Pap. Online 50, 14988 (2017).

    Article  Google Scholar 

  17. H.W. Li, J.L. Zhang and H.W. Guo, in 2014 National Iron and Steel Production Technology Conference and Ironmaking Academic Annual Meeting (Zhengzhou, Henan, China, 2014).

  18. A.J. Smola and B. Schölkopf, Stat. Comput. 14, 199 (2004).

    Article  MathSciNet  Google Scholar 

  19. M.J. Wang, (Chongqing University, 2007).

Download references

Funding

This work was supported by China Postdoctoral Science Foundation funded Project [Project No.: 2019M650490].

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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, Jianliang Zhang, Zhenyang Wang, Kexin Jiao & Renze Xu

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

    Jianliang Zhang

  3. School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China

    Chenfan Feng

Authors
  1. Dewen Jiang
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  2. Jianliang Zhang
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  3. Zhenyang Wang
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  4. Chenfan Feng
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  5. Kexin Jiao
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  6. Renze Xu
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Correspondence to Zhenyang Wang.

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Jiang, D., Zhang, J., Wang, Z. et al. A Prediction Model of Blast Furnace Slag Viscosity Based on Principal Component Analysis and K-Nearest Neighbor Regression. JOM 72, 3908–3916 (2020). https://doi.org/10.1007/s11837-020-04360-9

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  • DOI: https://doi.org/10.1007/s11837-020-04360-9

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