Abstract
Temperature adjustment is one of the critical tasks affecting the quality of manufactured steel. This is controlled by the Basic Oxygen Furnace’s (BOF) blowing procedures. As many factors influence variations in temperature, it is often difficult to predict the blowing quantity necessary to achieve a required temperature. In this study, we assume the framework used by the intelligent blowing control system uses the Case Based Reasoning (CBR) and Neural Network (NN) to predict the appropriate blowing quantity in the BOF. Our proposed framework consists of three steps. First, we retrieve the similar cases for a new order requirement using CBR. Next, we train the NN engine with the selected case set. Finally, we predict the appropriate blowing quantity using a trained neural network. Experimental results show that the proposed framework performs more effectively than the framework without using CBR process.
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References
Yoo, B.D.: Introduction to the Steel Making. Inha University Press (2004)
Saxen, H., Sillanpaa, M.: A Model for Decision Support in Continuous Steel Casting. Modeling and Simulation in Materials Science and Engineering 2, 79–98 (1994)
Millman, M.S., Thornton, G.: New Technologies in Steelmaking. Rev Metall-Paris 95, 477–486 (1998)
Hatanaka, T., Takenaka, M., Oka, Y.: Development of Blowing Control System in BOF Applying AI Technology, 48–53 (1991)
Yamane, A., Yamane, H., Miyahara, K., Iwamura, T.: Computer Integrated Steelmaking at the No.2 BOF Shop of the Mizushima Works. In: Proceedings of the Sixth International Iron and Steel Congress, pp. 47–54 (1990)
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, J., Sim, E., Jung, S. (2005). An Automated Blowing Control System Using the Hybrid Concept of Case Based Reasoning and Neural Networks in Steel Industry. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_128
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DOI: https://doi.org/10.1007/11427469_128
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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