Neural Network Based Breakout Prediction System in Continuous Casting Shop

Abstract:

— Continuous casting of steel is a process in which liquid steel is continuously solidified into semi-finished or finished product (slabs, blooms or billets). There are many problems associated with continuous casting shop which affect the casting process. A major problem is associated in continuous casting shop is breakout of molten steel. Breakout leads to temporary shutdown of caster, damage of machinery due to splash of molten steel, capital loss, safety hazards etc. In Bokaro steel plant a logical based breakout prediction system is used to predict the breakout. This system sometimes generates false alarm and sometimes even fail to generate an alarm before breakout. Also the logical model has lot of dependence on specific equipment, process and calibration. Neural network can be implemented for a better breakout prediction system. So, in this paper a back-propagation neural network model is developed for predicting the existence of primary cracks that might lead to a breakout. The network gets its input temperatures from thermocouples which are attached to the wide and narrow sides of the mould. The output of the neural network is either logic 1 (for presence of crack) or logic 0 (for no crack). Testing the network shows excellent result as evident from the confusion matrix and performance plot. The neural network model is validated by simulating in MatLab/Simulink. The developed network may be used effectively in predicting breakouts during continuous casting. Such effective prediction can go a long way in reducing production losses in steel manufacturing.

• Periodical: Applied Mechanics and Materials (Volumes 813-814)
• Pages: 550-556
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.813-814.550
• Online since: November 2015
• Authors: Md Obaidullah Ansari*, Rajashree Samantray, Joyjeet Ghose
• Keywords: Breakout Prediction, Continuous Casting, LM Algorithm, Neural Network, Pattern Recognition

References

[1] Pan E, Ye L, and Chang T, On-line bleeds detection in continuous casting processes using engineering-driven rule-based algorithm, Journal of manufacturing science and engineering, 2009, 131(6), pp.0610081-9.

DOI: 10.1115/1.4000560

[2] Raja B.V. R, Breakout in continuous casting of steel, Steel world monthly journal report, March 2009: pp.24-30.

[3] Z. Xu, L. Ma , Y. Zhang, et al. Study on neural network breakout prediction system based on temperature unit input, in International Conference on Measuring Technology and Mechatronics Automation, 2010: 612-615.

DOI: 10.1109/icmtma.2010.190

[4] X. Qin, C. Zhu, L. Zheng, Study of the forecasting of molten steel breakouts based on the frictional force between mold and slab shell, International Conference on Mechanic Automation and Control Engineering: 2593-2596, (2010).

DOI: 10.1109/mace.2010.5536840

[5] Qing et al., Research for Breakout Prediction System Based on Support Vector Regression, 2012 IEEE Symposium on Robotics and Applications (ISRA).

DOI: 10.1109/isra.2012.6219155

[6] Mugwagwa et al., Neural Network Breakout Prediction Model for Continuous Casting, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-2, December (2012).

[7] Cheng et al., Molten Steel Breakout Prediction Based on Genetic Algorithm and BP Neural Network in Continuous Casting Process, proceedings of the 31st Chinese Control Conference, July 2012, Hefei, China.

[8] Ben-guo et al., Breakout Prediction Based on BP Neural Network of LM Algorithm in Continuous Casting Process, International Conference on Measuring Technology and Mechatronics Automation, IEEE, (2010).

DOI: 10.1109/icmtma.2010.403

[9] Ren et al., Research on Breakout Prediction System Based on Multilevel Neural Network, International Conference on Electrical and Control Engineering, IEEE, (2010).

[10] Li et al., Study of Mould Breakout Prediction Technique in Continuous Casting Production, 3rd International Conference on Biomedical Engineering and Informatics, IEEE, (2010).

DOI: 10.1109/bmei.2010.5639335

[11] Zening et al., Study on Neural Network Breakout Prediction System Based on Temperature Unit Input, 2010 International Conference on Measuring Technology and Mechatronics Automation.

DOI: 10.1109/icmtma.2010.190

[12] Y. Li, Z. Wang, Z. Ao, et al. Optimization for breakout prediction system of BP neural network, Control and Decision, 25(3): 453-456, (2010).

[13] Z. Xu, L. Ma , Y. Zhang, et al. Study on neural network breakout prediction system based on temperature unit input, in International Conference on Measuring Technology and Mechatronics Automation, 2010: 612-615.

DOI: 10.1109/icmtma.2010.190

[14] W. Li, Y. Li, Y. Zhang, Study of mold breakout prediction technique in continuous casting production, in: 3rd International Conference on Biomedical Engineering and Informatics, 2010: 2966-2970.

DOI: 10.1109/bmei.2010.5639335

[15] P. Bellomo, M. Palchetti, M. E. Santa, et al, Neural network utilization for breakout monitoring, in Steelmaking Conference Proceedings, 1995: 345-348.

[16] A. Tsuneoka, W. Ohashi, S. Ishitobi, et al, Measurement and control system of solidification in continuous casting mould, in Steelmaking Conference Proceedings, 1985: pp.3-10.

[17] Zhigang Hu. The formation mechanism of sticking-type breakout in continuous casting production, Steel, 1998, (6), p.31–36.

[18] Sengupta J, Thomas B. G and Wells M. A, The Use of Water Cooling during the Continuous Casting of Steel and Aluminum Alloys, Metallurgical and Materials Transactions A, 2005, 36 (1), pp.187-204.

DOI: 10.1007/s11661-005-0151-y

[19] Brian G. Thomas and Claudio Ojeda, Ideal Taper Prediction for Slab Casting, 2003 ISSTech Steelmaking Conference, Indianapolis, IN, USA, April 27-30, 2003, ISS-AIME.

[20] M. Wolf, Mold Length in Slab Casting - a Review, Iron and Steelmaker, Vol. 23 (2), 1996, 41-51.

[21] M.M. Wolf, Continuous Casting: Initial Solidification and Strand Surface Quality of Peritectic Steels, Vol. 9, Iron and Steel Society, Warrendale, PA, 1997, 1-111.