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Object-Oriented Neurofuzzy Modeling and Control of a Binary Distillation Column by Using MODELICA

  • Javier Fernandez de Canete
  • Alfonso Garcia-Cerezo
  • Inmaculada Garcia-Moral
  • Pablo del Saz
  • Ernesto Ochoa
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

Neurofuzzy networks offer an alternative approach both for the identification and the control of nonlinear processes in process engineering. The lack of software tools for the design of controllers based on hybrid neural networks and fuzzy models is particularly pronounced in this field. MODELICA is an oriented-object environment widely used which allows system-level developers to perform rapid prototyping and testing. Such programming environment offers an intuitive approach to both adaptive modeling and control in a great variety of engineering disciplines. In this paper we have developed an oriented-object model of binary distillation column with nonlinear dynamics, and an ANFIS (Adaptive-Network-based Fuzzy Inference System) neurofuzzy scheme has been applied to derive both an identification model and a adaptive controller to regulate distillation composition. The results obtained demonstrate the effectiveness of the neurofuzzy control scheme when the plant’s dynamics is given by a set of nonlinear differential algebraic equations (DAE).

Keywords

Object-oriented DAE equations MODELICA Neurofuzzy Modeling Neurofuzzy Control ANFIS structure Distillation Column 

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References

  1. 1.
    Bulsari, A.: Neural Networks for Chemical Engineers. Elsevier, Amsterdam (1995)Google Scholar
  2. 2.
    Hussain, M.A.: Review of the Applications of Neural Networks in Chemical Process Control. Simulation and online Implementations. Artif. Intel. Eng. 13, 55–68 (1999)CrossRefGoogle Scholar
  3. 3.
    Engin, J., Kuvulmaz, J., Omurlu, E.: Fuzzy control of an ANFIS model representing a nonlinear liquid level system. Neural Comput. Appl. 13(3), 202–210 (2004)CrossRefGoogle Scholar
  4. 4.
    Jang, R., Sun, C.: Neuro-fuzzy Modeling and Control. Proceedings of the IEEE 83(3), 378–405 (1995)CrossRefGoogle Scholar
  5. 5.
    Denai, M., Palis, F., Zeghbib, A.: ANFIS Based Modeling and Control of Nonlinear Systems: A Tutorial. In: Proceedings of the IEEE Conference on SMC, pp. 3433–3438 (2004)Google Scholar
  6. 6.
    Leegwater, H.: Industrial Experience with Double Quality Control. In: Luyben, W.L. (ed.) Practical Distillation Control. Van Nostrand Reinhold, New York (1992)Google Scholar
  7. 7.
    Luyben, P.: Process Modeling, Simulation and Control for Chemical Engineers. Chemical Eng. Series. McGraw Hill (1990)Google Scholar
  8. 8.
    Skogestad, S.: Dynamics and Control of Distillation Columns. A Critical Survey. In: IFAC Symposium DYCORD 1992, Maryland (1992)Google Scholar
  9. 9.
    Fruehauf, P., Mahoney, D.: Improve Distillation Control Design. Chem. Eng. Prog. (1994)Google Scholar
  10. 10.
    Burns, A.: ARTIST Survey of Programming Languages. ARTIST Network of Excellence on Embedded Systems Design (2008)Google Scholar
  11. 11.
  12. 12.
    Mattsson, S.E., Elmquist, H., Otter, M.: Physical System Modelling with Modelica. Control Eng. Pract. 6, 501–510 (1998)CrossRefGoogle Scholar
  13. 13.
    Bruck, D., Elmqvist, H., Olsson, H., Matteson, S.E.: Dymola for Multi-engineering Modelling and Simulation. In: Proceedings of the 2nd Int. Modelica Conference, Germany, pp. 551–558 (2002)Google Scholar
  14. 14.
    Tiller, M.: Introduction to Physical Modeling with Modelica. Kluwer Ac. Press (2001)Google Scholar
  15. 15.
    Fritzson, P.: Principles of Object-oriented Modeling and Simulation with Modelica 2.1. Wiley-IEEE Press (2003)Google Scholar
  16. 16.
    Diehl, M., Uslu, I., Findeisen, R.: Real-time optimization for large scale processes: Nonlinear predictive control of a high purity distillation column. In: On Line Optimization of Large Scale Systems: State of the Art. Springer (2001)Google Scholar
  17. 17.
    Zauner, G., Leitner, D., Breitenecker, F.: Modeling Structural Dynamic Systems in MODELICA/Dymola, MODELICA/Mosilab and AnyLogic. In: Proceedings of the International Workshop on Equation-Based Oriented Languages and Tool (2007)Google Scholar
  18. 18.
    Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modeling and Control of Dynamic Systems. Springer (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Javier Fernandez de Canete
    • 1
  • Alfonso Garcia-Cerezo
    • 1
  • Inmaculada Garcia-Moral
    • 1
  • Pablo del Saz
    • 1
  • Ernesto Ochoa
    • 2
  1. 1.System Engineering and AutomationMalagaSpain
  2. 2.Acenture S.L.MalagaSpain

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