A Multiple RBF NN Modeling Approach to BOF Endpoint Estimation in Steelmaking Process (original) (raw)

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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Author information

Authors and Affiliations

  1. Institute of Automation, Shanghai Jiao Tong University, 200030
    Xin Wang & Shaoyuan Li
  2. Depart. of Control Science&Engineering, Tongji University, 200092
    Zhongjie Wang
  3. Baosight Software Corporation, Shanghai, 201900
    Jun Tao
  4. Research center of Automation, Northeastern University, 110004
    Jinxin Liu

Authors

  1. Xin Wang
  2. Shaoyuan Li
  3. Zhongjie Wang
  4. Jun Tao
  5. Jinxin Liu

Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China
    Fu-Liang Yin
  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
    Jun Wang
  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China
    Chengan Guo

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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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