Skip to main content

Advertisement

SpringerLink
Log in

Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lukyanov SI, Suspitsyn ES, Krasilnikov SS, Shvidchenko DV (2015) Intelligent system for prediction of liquid metal breakouts under a mold of slab continuous casting machines. Int J Adv Manuf Technol 79(9):1861–1868. https://doi.org/10.1007/s00170-015-6945-1

    Article  Google Scholar 

  2. He F, He DF, Deng ZH, Xu AJ, Tian NY (2015) Development and application of mould breakout prediction system with online thermal map for steel continuous casting. Ironmak Steelmak 42(3):194–208. https://doi.org/10.1179/1743281214Y.0000000220

    Article  Google Scholar 

  3. Blazevic D, Ikonic M, Mikac T (2008) Algorithm for prevention of molten steel sticking onto mold in continuous casting process. Metalurgija 47(1):47–50

    Google Scholar 

  4. Langer M, Arzberger M (2002) New approaches to breakout prediction. Steel Times Int 26(9):23–26

    Google Scholar 

  5. Ansari MO, Ghose J, Kumar R (2017) Neural network based breakout predicting system for all four strands of caster in a continuous casting shop—a case study. Advances in Computational Intelligence. Springer, Singapore, pp 89–99

    Google Scholar 

  6. Liu Y, Wang XD, Du FM, Yao M, Gao YL, Wang FW, Wang JY (2017) Computer vision detection of mold breakout in slab continuous casting using an optimized neural network. Int J Adv Manuf Technol 88(1–4):557–564. https://doi.org/10.1007/s00170-016-8792-0

    Article  Google Scholar 

  7. Ji C, Cai ZZ, Tao NB, Yang JL, Zhu MY (2012) Molten steel breakout prediction based on genetic algorithm and BP neural network in continuous casting process. In: Proceedings of 31st Chinese Control Conference (CCC), 25–27 July 2012, Hefei. IEEE, New York, pp 3402–3406

  8. Tirian GO, Filip I, Proştean G (2014) Adaptive control system for continuous steel casting based on neural networks and fuzzy logic. Neurocomputing 125(3):236–245. https://doi.org/10.1016/j.neucom.2012.11.052

    Article  Google Scholar 

  9. He F, Wu PF, Xu QY, Zhou L (2016) Mould temperature change and propagation behavior of sticking-type breakout during continuous casting. J Iron Steel Res 28(2):27–32

    Google Scholar 

  10. Bouhouche S (2002) Contribution to quality and process optimisation in continuous casting using mathematical modelling. Published by Technischen Universität Bergakademie Freiberg, 125 p

  11. Pat. US 005548520 Int. Cl.7 B22D11/16 Breakout prediction system in a continuous casting process / Nakamura T, Kodaira K, Higuchi K – publ. 20.08.1996

  12. Yang J, Chen HW, Bai J (2005) Application and optimization of mould breakout prevention technology. Steelmak 21(3):17–19

    Google Scholar 

  13. He F, He DF, Zhou L, Tian NY (2015) Research progress on continuous casting crystallizer breakout prediction models. Foundry Technol 36(6):1528–1535

    Google Scholar 

Download references

Funding

The authors are grateful to the financial support for this project from the National Natural Science Foundation of China (Grant No. 51504002).

Author information

Authors and Affiliations

  1. School of Metallurgical Engineering, Anhui University of Technology, Ma’anshan, Anhui, 243002, China

    Fei He & Lingying Zhang

Authors
  1. Fei He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, F., Zhang, L. Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules. Int J Adv Manuf Technol 95, 4081–4089 (2018). https://doi.org/10.1007/s00170-017-1517-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-1517-1

Keywords

Search

Navigation