Advertisement

Metallurgist

, Volume 63, Issue 7–8, pp 792–803 | Cite as

Development of the Metal Temperature Prediction Model for Steel-pouring and Tundish Ladles Used at the Casting and Rolling Complex

  • S. A. BotnikovEmail author
  • O. S. Khlybov
  • A. N. Kostychev
Article
  • 10 Downloads

A model has been developed for predicting the metal temperature in the specified superheat range above the liquidus temperature inside the tundish of a continuous casting machine (CCM) depending on the last temperature measurement in the steel-pouring ladle (prior to casting), as well as ladle history and secondary steelmaking process under the conditions of the Casting and Rolling Complex (CRC) of the JSC “Vyksa Metallurgical Plant” (VMZ). It is suggested to predict the superheat temperature of steel at the CRC by using statistical models considering the specifics of technological process. During the first stage, the model itself has been developed by integrating the following two approaches: a machinelearning algorithm and probabilistic graphical model (PGM and Bayesian networks).

The first approach provides the point estimate of the intermediate and final temperatures of the specific melting, while the second approach allows evaluating the probability distribution. The PGM-approach is very promising in the situations involving missing, incorrect, or undefined input data, which are quite common at the metallurgical plants. During the first stage, the achieved level of accuracy of tundish temperature prediction was 6.0°C.

During the second stage, the model was integrated into the shop automated process control system, which allowed increasing the prediction accuracy and ensuring a real-time control of the process parameters. The model is used as a guidance (master) tool for the process personnel operating the ladlefurnace and vacuum degasser equipment.

Keywords

tundish metal superheat control statistical model ladle metal temperature prior to casting ladle history machine learning neural networks temperature prediction ladle heat balance optimization ladle refractories ladle slag basicity 

References

  1. 1.
    J. Laine, L. Holappa, and S. Louhenkilpi, “Temperature prediction for steel continuous casting,” in: Proc. of 2nd Int. Conf. “Advances in Metallurgical Processes & Materials,” Kyiv, Ukraine (2015), pp. 1–7.Google Scholar
  2. 2.
    F. He, D-F. He, A-J. Xu, et al., “Hybrid model of molten steel temperature prediction based on ladle heat status and artificial neural network,” J. Iron & Steel Res., Int., 2, 21, 181–190 (2014).Google Scholar
  3. 3.
    Y. Wang, S. Abraham, R. Bodnar, et al., “Continuous slab superheat control at SSAB Mobile,” in: Proc. Conf. AISTech (2014), pp. 1647–1657.Google Scholar
  4. 4.
    K. Mandal, E. Miller, D. Pierce, et al., “Development and implementation of an online process model for the control of steel chemistry and superheat during secondary steelmaking,” in: Proc. Conf. AISTech (2012), pp. 1045–1053.Google Scholar
  5. 5.
    S. Chen, C. D’souza, D. Evans, et al., “Continuous enhancement of the Evraz superheat model control for slab casting,” in: Proc. Conf. AISTech (2012), pp. 1303–1315.Google Scholar
  6. 6.
    S. Abraham and S. Chen, “On-line superheat control model for continuously cast slabs and billets,” Iron & Steel Technology, 7, No. 7, 89–96 (2010).Google Scholar
  7. 7.
    V. Aranda, F. Lourenco, L. Demuner, et al., “Mold flow evaluation during production of IF steel grade with an advanced multiple measurement mold audit tool “XMAT,” Proc. Conf. AISTech (2018) [Electronic resourse]. URL: http://digital.library.aist.org/pages/ PR-374–251.htm (Access date: 04.02.2019).
  8. 8.
    B. Soete, C. Warmers, E. Bikkembergs, et al., “Tundish flow optimization in Aperam Genk for quality improvement,” ESTAD, 486–496 (2017).Google Scholar
  9. 9.
    T. Lehut and A. Dörsel, “ACCUOPTIXTM. Continuous temperature measurement system in the tundish,” in: METEC and 2nd ESTAD (2015), pp. 1–9.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • S. A. Botnikov
    • 1
    Email author
  • O. S. Khlybov
    • 1
  • A. N. Kostychev
    • 1
  1. 1.JSC “Vyksa Metallurgical Plant” (VMZ)VyksaRussia

Personalised recommendations