Hybrid Intelligent Control Strategy of the Laminar Cooling Process

  • Minghao Tan
  • Shujiang Li
  • Tianyou Chai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Performance of controlled laminar cooling is usually poor because of the difficulty in continuous online temperature measurement and the complex nature of the laminar cooling process (e.g., highly nonlinear, time varying). This paper developed a hybrid control strategy for the laminar cooling process that integrates Radial Basis Function (RBF) networks and Case-Based Reasoning (CBR). The spraying pattern and the first activated headers are found by a case-based reasoner, while the number of activated headers is calculated in real time by RBF networks. Experimental studies using production data from a hot strip mill show the superior performance of the proposed control strategy.


Radial Basis Function Radial Basis Function Network Propose Control Strategy Coiling Temperature Radial Basis Function Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chai, T.Y., Tan, M.H., Chen, X.Y., Li, H.X.: Intelligent Optimization Control for Laminar Cooling. In: Camacho, B., Puente, D. (eds.) Proc. of the 15th IFAC World Congress, pp. 691–696. Elsevier, Amsterdam (2003)Google Scholar
  2. 2.
    Groch, A.G., Gubemat, R., Birstein, E.R.: Automatic Control of Laminar Flow Cooling in Continuous and Reversing Hot Strip Mills. Iron and Steel Engineer 67(9), 16–20 (1990)Google Scholar
  3. 3.
    Leitholf, M.D., Dahm, J.R.: Model Reference Control of Runout Table Cooling at LTV. Iron and Steel Engineer 66(8), 31–35 (1989)Google Scholar
  4. 4.
    Moffat, R.W.: Computer Control of Hot Strip Coiling Temperature with Variable Flow Laminar Spray. Iron and Steel Engineer 62(11), 21–28 (1985)Google Scholar
  5. 5.
    Ditzhuijzen, V.G.: The Controlled Cooling of Hot Rolled Strip: A Combination of Physical Modeling, Control Problems and Practical Adaptation. IEEE Trans. Aut. Cont. 38(7), 1060–1065 (1993)CrossRefGoogle Scholar
  6. 6.
    Iwamoto, M., Jeyerajan, M., Wada, H.: Process Control Computer System of Fukuyama No.2 Hot Strip Mill. Nippon Kokan Fukuyama Works Tech. Rep. 46 (1986)Google Scholar
  7. 7.
    Miyake, Y., Nishide, T., Moriya, S.: Device and System for Controlled Cooling for Hot Strip Mill. Transactions ISIJ 20(2), 496–503 (1980)Google Scholar
  8. 8.
    Kolodner, J.L.: Case-Based Reasoning, 1st edn. Morgan Kaufmann, New York (1993)Google Scholar
  9. 9.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Comm. 7(1), 39–59 (1994)Google Scholar
  10. 10.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Upper Saddle River (1999)MATHGoogle Scholar
  11. 11.
    Tan, M.H., Chai, T.Y.: Laminar Cooling Process Model Development Using RBF Networks. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 852–857. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Shan, X.: Transformation and Development of the Cooling Control System of the 2050mm Baosteel Hot Strip Mill. In: Ren, D. (ed.) Development of Science and Technology in Metallurgy, pp. 1–4. Metallurgical Industry Press, Hangzhou (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Minghao Tan
    • 1
  • Shujiang Li
    • 1
  • Tianyou Chai
    • 2
  1. 1.School of Information Science and EngineeringShenyang University of TechnologyShenyangChina
  2. 2.Research Center of AutomationNortheastern UniversityShenyangChina

Personalised recommendations