Mathematical Modelling for Prediction of Liquid Steel Temperature in Steel Melting Shop of An Integrated Steel Industry

Vikash Kumar¹ , Somnath Kumar² , Antariksh Gupta³ , K. Abhishek⁴ , Snehangshu Roy⁵ , Anjana Deva⁶

1. R&D Centre for Iron and Steel (RDCIS), Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.
2. R&D Centre for Iron and Steel (RDCIS), Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.
3. Digital Transformation, Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.
4. R&D Centre for Iron and Steel (RDCIS), Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.
5. R&D Centre for Iron and Steel (RDCIS), Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.
6. R&D Centre for Iron and Steel (RDCIS), Steel Authority of India Limited (SAIL), Ispat Bhawan, PO Doranda, Ranchi, India 834002.

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.1 , pp.1-6, Mar-2024

Online published on Mar 31, 2024

Copyright © Vikash Kumar, Somnath Kumar, Antariksh Gupta, K. Abhishek, Snehangshu Roy, Anjana Deva . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

23 Views    24 Downloads    5 Downloads

Abstract :
The optimal temperature of liquid steel supplied from the ladle furnace during continuous casting is critical for ensuring high-quality products, as deviations from the ideal temperature range can lead to adverse effects such as centerline segregation and potential breakout in case of high temperature or nozzle freezing and steel contamination with macro inclusions due to low temperature. The objective of this work is to predict temperature as a function of time during casting process. Different factor of temperature gain and loss was taken into consideration during building of this model. A comprehensive model integrating heat transfer calculations and mathematical formulations was developed to accurately regulate casting superheat, enabling precise prediction of target temperatures. Moreover, CFD modelling was also done to predict the temperature during transportation and holding period of ladle. The outcome of this model has also been validated using plant data and found satisfactorily with deviation of ~ ± 0.65 % with actual for 90% cases. The validated thermal model with plant data shows that prediction of temperature can be done using models with good accuracy. Finally a GUI was prepared for temperature tracking of liquid steel in ladle at each processing station of steel melting shop.

Key-Words / Index Term :
Casting, Temperature, CFD Modelling, Ladle, Steel Melting Shop, Prediction Model

References :
[1] Mingyi Liang,Seong-Mook Cho,Xiaoming Ruan and Brian G. Thomas, “Modeling of Multiphase Flow, Superheat Dissipation, Particle Transport, and Capture in a Vertical and Bending Continuous Caster”, Processes, Vol.10, Issue.7, pp.1-34, 2022.
[2] Huining Zhang, Peiling Zhou, Fei Yuan, “Effects of ladle lid or online preheating on heat preservation of ladle linings and temperature drop of molten steel”, Energy, Vol. 214, 2021.
[3] T.M. Cunha, M.H. Moreira, M.F. Santos, A.P. Luz, V.C. Pandolfelli, “Drying behavior of steel-ladle lining refractory castables under continuous heating rate”, Ceramics International, Vol. 48, Issue 1, Pages 1142-1151, 2022.
[4] Mahmoud Ali,Thomas Sayet, Alain Gasser and Eric Blond,“Transient Thermo-Mechanical Analysis of Steel Ladle Refractory Linings Using Mechanical Homogenization Approach”, Ceramics, Vol. 3, issue 2, pp 171-189, 2020.
[5] K. Abhishek, A. Ganvir, A. Rouf, A. Das and K. Rama Krishna, “Exploring the Possibility of Big Data and Machine Learning in Steel Melting Shop Logistics Planning”, International Journal of Scientific Research in Multidisciplinary Studies, Vol. 5, Issue 5, pp.01-05, 2019.
[6] Noboru Murata, Hideitsu Hino, Hiroshi Kitada and Manabu Kano, “A Statistical Model for Predicting the Liquid Steel Temperature in Ladle and Tundish by Bootstrap Filter”, ISIJ International, Vol.52, Issue 6, pp 1086-1091, 2012.
[7] ?ukasz Sztangret, Krzysztof Regulski ,Monika Pernach and ?ukasz Rauch, “Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques”, Coatings, Vol 13, issue 9, 2023.
[8] N. K. Nath, K. Mandal, A. K. Singh, B. Basu, C. Bhanu, S. Kumar & A. Ghosh, “Ladle furnace on-line reckoner for prediction and control of steel temperature and composition”, Ironmaking & Steelmaking, Vol. 33, issue 2, pp 140-150.
[9] André Zimmer, Álvaro Niedersberg Correia Lima, Rafael Mello Trommer, Saulo Roca Bragança, and Carlos Pérez Bergmann, “Heat Transfer in Steelmaking Ladle”, Journal of Iron and Steel Research, International, Vol 15, Issue 3, pp 11–14, 2008 .
[10] Sanjib Chakraborty and Yogeshwar Sahai, “Effect of slag cover on heat loss and liquid steel flow in ladles before and during teeming to a continuous casting tundish”, Metallurgical Transactions B, Vol 23, pp. 135-151, 1992.
[11] P. Egerton, J. A. Howarth, G. Poots and S. Taylor-Reed, “ A theoretical investigation of heat transfer in a ladle of molten steel during pouring”, International Journal of Heat and Mass Transfer, Vol 22, Issue 11, pp 1525-1532, 1979.
[12] Senhui Wang, Haifeng Li, Yongjie Zhang, Cheng Wang, Xiang He, Denghong Chen and Ke Yang , “Studies on Parameters Affecting Temperature of Liquid Steel and Prediction Using Modified AdaBoost.RT Algorithm Ensemble Extreme Learning Machine”, Metals, Vol 12,issue 12, 2022.
[13] Natasha Gupta and Sanjay Chandra, “Temperature Prediction Model for Controlling Casting Superheat Temperature”, ISIJ International, Vol. 44, Issue 9, Pages 1517-1526, 2004.
[14] Xiaojun Wang, “Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest”, IEEE/CAA Journal Of Automatica Sinica, Vol.4, issue 4, pp 770-774, 2017.
[15] Huixin Tian, Zhizhong Mao, Yan Wang, “Hybrid Modeling of Molten Steel Temperature Prediction in LF”, ISIJ International, Vol.48, Issue 1, Pages 58-62, 2008.
[16] Anurag Tripathi, J. K. Saha, J. B. Singh and S. K. Ajmani, “CFD Modeling and Simulation in Materials Processing”, John Wiley & Sons Publishers, USA, pp. 295-302, 2012.
[17] Anirudh Deodhar, Umesh Singh, Rishabh Shukla, B. P. Gautham and Amarendra K. Singh, “Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle”, Metallurgical and Materials Transactions B, Vol. 48, pp 1217–1229, 2017.
[18] M. A. Omotani, L. J. Heaslip and A. Maclean, “Ladle temperature control during continuous casting”, Iron & Steelmaker, Vol 10, Issue10, pp 29-35, 1983.
[19] J. M. Camp and C. B. Francis, “The Making, Shaping and Treating of Steel, 10th Ed”, United States Steel Corporation publishers , USA, pp.344-348, 1985.