The paper presents a prediction model for steel temperature in a specified time interval during its overheating over a liquidus point in the tundish of a continuous casting machine (CCM). This depends on the last temperature measurement in a ladle prior to casting with consideration to the ladle’s history and treatment during secondary metallurgical processes in an casting and rolling complex (CRC). The prediction of steel superheat temperature at CRC is based on two modeling stages: the first stage integrates two approaches—a machine-learning algorithm and a probabilistic-graphical model (PGM and Bayesian networks); and the second stage uses a method for evaluating probability distributions. The first approach gives a point estimate of the intermediate and final temperatures of specific heat. The PGM-approach is useful for scenarios with uncertain input data, which is often the case for metallurgical enterprises. The accuracy level in predicting the temperature of the metal in the tundish at the first stage reached 6.0°C. At the second stage, the model integration into the process control system improved prediction accuracy, as well as provided technological parameter control in real time. The model is used as an advisor (master) for the technical team of the ladle furnace and vacuum degasser.
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Laine, J., Holappa, L., and Louhenkilpi, S., Temperature prediction for steel continuous casting, Proc. 2nd Int. Conf. “Advances in Metallurgical Processes & Materials,” Kyiv, 2015, pp. 1–7.
He, F., He, D.-F., Xu, A.-J., et al., Hybrid model of molten steel temperature prediction based on ladle heat status and artificial neutral network, J. Iron Steel Res. Int., 2014, vol. 21, no. 2, pp. 181–190.
Wang, Y., Abraham, S., Bodnar, R., et al., Continuous slab superheat control at SSAB Mobile, Proc. 2014 AISTech Conf., Warrendale, PA: Assoc. Iron Steel Technol., 2014, pp. 1647–1657.
Mandal, K., Miller, E., Pierce, D., et al., Development and implementation of an online process model for the control of steel chemistry and superheat during secondary steelmaking, Proc. 2012 AISTech Conf., Warrendale, PA: Assoc. Iron Steel Technol., 2012, pp. 1045–1053.
Chen, S., D’souza, C., Evans, D., et al., Continuous enhancement of the Evraz superheat model control for slab casting, Proc. 2012 AISTech Conf., Warrendale, PA: Assoc. Iron Steel Technol., 2012, pp. 1303–1315.
Abraham, S. and Chen, S., On-line superheat control model for continuously cast slabs and billets, Iron Steel Technol., 2010, vol. 7, no. 7, pp. 89–96.
Aranda, V., Lourenco, F., Demuner, L., et al., Mold flow evaluation during production of IF steel grade with an advanced multiple measurement mold audit tool “XMAT”, Proc. 2018 AISTech Conf., Warrendale, PA: Assoc. Iron Steel Technol., 2018. http://digital.library.aist.org/pages/PR-374-251.htm. Accessed April 2, 2019.
Soete, B., Warmers, C., Bikkembergs, E., et al., Tundish flow optimization in Aperam GENK quality improvement, Proc. European Steel Technology and Application Days 2017 (ESTAD 2017), Vienna: Aust. Soc. Metall. Mater., 2017, pp. 486–496.
Lehut, T. and Dörsel, A., ACCUOPTIX™. Continuous temperature measurements system in the tundish, METEC and 2nd European Steel Technology and Application Days, Düsseldorf, 2015, pp. 1–9.
Translated by A. Simakova
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Botnikov, S.A., Khlybov, O.S. & Kostychev, A.N. Development of a Steel Temperature Prediction Model in a Steel Ladle and Tundish in a Casting and Rolling Complex. Steel Transl. 49, 688–694 (2019). https://doi.org/10.3103/S096709121910005X
- superheat control in a tundish
- statistical model
- metal temperature in the steel ladle before casting
- neural networks
- temperature prediction
- optimization of thermal balance
- basicity of slag