Abstract
In order to estimate the bath endpoint phosphorus [P] content and manganese [Mn] content for Basic Oxygen Furnace (BOF) steelmaking process, three BOF endpoint estimation models are given according to the fast speed case, the slow speed case and the middle speed case of analyzing the sublance in-blowing sample. The first one is modeled from metallurgic mechanism, the second is with RBF NN and the last one is modeled using least square method. With theses models, steelmaker can get the estimation of BOF endpoint [P] and [Mn] based on the process information and the sublance measurement. The industrial experiment shows that these models are helpful and powerful.
This work is supported by 863 High Technology Project (2002AA412130, 2003AA412310).
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References
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, X., Li, S., Wang, Z., Tao, J., Liu, J. (2004). A Multiple RBF NN Modeling Approach to BOF Endpoint Estimation in Steelmaking Process. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_135
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DOI: https://doi.org/10.1007/978-3-540-28648-6_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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