Advertisement

Applied Intelligence

, Volume 20, Issue 2, pp 165–177 | Cite as

An Adaptive, Intelligent Control System for Slag Foaming

  • Eric L. Wilson
  • Charles L. Karr
  • James P. Bennett
Article

Abstract

Slag foaming is a steel-making process that has been shown to improve the efficiency of electric arc furnace plants. Unfortunately, slag foaming is a highly dynamic process that is difficult to control. This paper describes the development of an adaptive, intelligent control system for effectively manipulating the slag foaming process. The level-2 intelligent control system developed is based on three techniques from the field of computational intelligence (CI): (1) fuzzy logic, (2) genetic algorithms, and (3) neural networks. Results indicate that the computer software architecture presented in this paper is suitable for effectively manipulating complex engineering systems characterized by relatively slow process dynamics like those of a slag foaming operation.

intelligent control computational intelligence genetic algorithm neural network fuzzy logic steel manufacturing slag foaming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    L.R. Medsker, Hybrid Intelligent Systems, Kluwer Academic Publishers: Boston, 1995.Google Scholar
  2. 2.
    W.T. Miller, R.S. Sutton, and P.J. Werbos (Eds.), Neural Networks for Control, The MIT Press: Cambridge, MA, 1991.Google Scholar
  3. 3.
    C.L. Karr, “Genetic algorithms for fuzzy controllers,” AI Expert, vol. 6, no. 2, pp. 26-33, 1991a.Google Scholar
  4. 4.
    C.L. Karr, “Fine-tuning a cart-pole balancing fuzzy logic controller using a genetic algorithm,” Proceedings of The Applications of Artificial Intelligence VIII Conference, vol. 1468, pp. 26-36, 1991b.Google Scholar
  5. 5.
    C.L. Karr and E.J. Gentry, “Fuzzy control of pH using genetic algorithms,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 1, pp. 46-53, 1992.Google Scholar
  6. 6.
    C.L. Karr, S.K. Sharma, W.J. Hatcher, and T.R. Harper, “Fuzzy control of an exothermic chemical reaction using genetic algorithms,” Engineering Applications of Artificial Intelligence, vol. 6, no. 6, pp. 575-582, 1993.Google Scholar
  7. 7.
    C. Phillips, C.L. Karr, and G. Walker, “Helicopter flight control with fuzzy logic and genetic algorithms,” Engineering Applications of Artificial Intelligence, vol. 9, no. 2, pp. 175-184, 1996.Google Scholar
  8. 8.
    C.L. Karr, Practical Applications of Computational Intelligence for Adaptive Control, CRC Press: Boca Raton, FL, 1999.Google Scholar
  9. 9.
    K. KrishnaKumar and J. Neidhoefer, “Immunized adaptive critic for an autonomous aircraft control application,” Artificial Immune Systems and their Applications, Springer-Verlag, Inc., 1998.Google Scholar
  10. 10.
    L.A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, pp. 28-44, 1973.Google Scholar
  11. 11.
    A. Kandel and G. Langholz (Eds.), Fuzzy Control Systems, Boca Raton, FL: CRC Press, 1993.Google Scholar
  12. 12.
    E. Wilson, “Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces,” Ph.D. Dissertation, University of Alabama, 2002 (submitted).Google Scholar
  13. 13.
    T. Back, D.B. Fogel, and Z. Michalewicz (Eds.), Handbook of Evolutionary Computation, Oxford University Press: NewYork, 1997.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Eric L. Wilson
  • Charles L. Karr
  • James P. Bennett

There are no affiliations available

Personalised recommendations