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Fuzzy Controller Design for an FCC Unit

Conference paper

Abstract

This paper examines the procedure for a nonlinear modeling and fuzzy controller design of a Fluidized Catalytic Cracking Unit, also known as FCCU. The case study is an FCCU plant in Abadan Refinery, Iran. FCCU is one of the most important sections in the Petrochemical industry. In 2006 alone, FCCUs refined one-third of the Crude Oil worldwide. FCCUs convert heavy distillates, such as Gasoil (feed) and Crude Oil, to Gasoline, Olefinic gases and other more usable products. Factors including but not limited to FCCU’s high efficiency, and daily price fluctuations in Gas, Oil and Petrochemical products, make the optimization of such units the center of focus for both engineers and investors. Unlike the conventional controllers, Fuzzy Logic is the perfect choice for stochastic, dynamic and nonlinear processes where the mathematical model of the plant cannot be produced, or if realizable, a great deal of approximation is involved. The heuristic approach in Fuzzy Logic controllers is the closest form to the human language, and this virtue will make it a perfect candidate for a wide range of industrial applications. The investigations in this paper are simulated and proven by MATLAB Fuzzy Logic Toolbox R2013a. In this paper, the applicability and promising features of Fuzzy Logic controllers for such a complex and demanding plant will be investigated.

Keywords

FCCU Fuzzy controllers Fuzzy logic Fuzzy surface Membership functions Nonlinear modeling Petrochemical plant 

References

  1. 1.
    F.Z. Tatrai, P.A. Lant, P.L. Lee, C. Ian T, R.B. Newell, Control relevant model reduction: a reduced order model for model IV fluid catalytic cracking units. J. Process Control 4(1), 3–14 (1994)CrossRefGoogle Scholar
  2. 2.
    H. Tootoonchy and H. Hashemi, in Fuzzy Logic Modeling and Controller Design for a Fluidized Catalytic Cracking Unit. Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS. Lecture Notes in Engineering and Computer Science (San Francisco, USA, 2013), pp 982–987, 23–25 Oct 2013Google Scholar
  3. 3.
    H. Taşkin, C. Kubat, Ö. Uygun, S. Arslankaya, FUZZYFCC: Fuzzy logic control of a fluid catalytic cracking unit (FCCU) to improve dynamic performance. Comput. Chem. Eng. 30(5), 850–863 (2006)CrossRefGoogle Scholar
  4. 4.
    Chun Chien Lee, Fuzzy logic in control systems: fuzzy logic controller. Part II IEEE Trans. Syst. Man Cybern. 20(2), 419–435 (1990)CrossRefMATHGoogle Scholar
  5. 5.
    R. Sadeghbeigi Fluid Catalytic Cracking Handbook: an Expert Guide to the Practical Operation, Design, and Optimization of FCC Units (Elsevier, Oxford, 2012)Google Scholar
  6. 6.
    A. Arbel, Z. Huang, I.H. Rinard, R. Shinnar, No Title. Ind. Eng. Chem. Res. 34, 1228 (1995)CrossRefGoogle Scholar
  7. 7.
    M.F. Azeem, N. Ahmad, M. Hanmandlu, Fuzzy modeling of fluidized catalytic cracking unit. Appl. Soft Comput. 7(1), 298–324 (2007)CrossRefGoogle Scholar
  8. 8.
    A. Arbel, Z. Huang, I.H.I. Rinard, R. Shinnar, A.V. Sapre, Dynamic and control of fluidized catalytic crackers 1 modeling of the current generation of FCC’s. Ind. Eng. Chem. Res. 34(4), 1228–1243 (1995)CrossRefGoogle Scholar
  9. 9.
    V.W. Weekman Jr, Model of catalytic cracking conversion in fixed, moving, and fluid-bed reactors. Ind. Eng. Chem. Process Des. Dev. 7(1), 90–95 (1968)CrossRefGoogle Scholar
  10. 10.
    I. Pitault, D. Nevicato, M. Forissier, J.-R. Bernard, Kinetic model based on a molecular description for catalytic cracking of vacuum gas oil. Chem. Eng. Sci. 49(24), 4249–4262 (1994)CrossRefGoogle Scholar
  11. 11.
    R. Maya-Yescas, F. López-Isunza, Comparison of two dynamic models for FCC units. Catal. Today 38(1), 137–147 (1997)CrossRefGoogle Scholar
  12. 12.
    J. Alvarez-Ramirez, R. Aguilar, F. López-Isunza, Robust regulation of temperature in reactor-regenerator fluid catalytic cracking units. Ind. Eng. Chem. Res. 35(5), 1652–1659 (1996)CrossRefGoogle Scholar
  13. 13.
    M.I. Wan-lin, The application of TRICON ESD in FCCU. Shenyang Chem. Ind. 4, 22 (2005)Google Scholar
  14. 14.
    Y.-Z. Lu, M. He, C.-W. Xu, Fuzzy modeling and expert optimization control for industrial processes. Control Syst. Technol. IEEE Trans. 5(1), 2–12 (1996)Google Scholar
  15. 15.
    M. Delgado, M.A. Vila, J. Kaprzyk, J.L. Verdegay, Fuzzy optimization: recent advances (Springer, New York, 1994)MATHGoogle Scholar
  16. 16.
    J.M. Cadenas, J.L. Verdegay, Towards a new strategy for solving fuzzy optimization problems. Fuzzy Optim. Decis. Mak. 8(3), 231–244 (2009)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    E.E. Ali, S.S.E.H. Elnashaie, Nonlinear model predictive control of industrial type IV fluid catalytic cracking (FCC) units for maximum gasoline yield. Ind. Eng. Chem. Res. 36(2), 389–398 (1997)CrossRefGoogle Scholar
  18. 18.
    T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. Syst. Man Cybern. IEEE Trans. 1, 116–132 (1985)CrossRefGoogle Scholar
  19. 19.
    E.T. Van Donkelaar, P.S.C. Heuberger, P.M.J. Van den Hof, Identification of a fluidized catalytic cracking unit: an orthonormal basis function approach, in Proceedings of the American Control Conference, vol 3 (1998), pp. 1914–1917Google Scholar
  20. 20.
    Y. Huang, S. Dash, G.V Reklaitis, V. Venkatasubramanian, EKF based estimator for FDI in the model IV FCCU, in Proceedings of SAFEPROCESS (2000), pp. 14–16Google Scholar
  21. 21.
    L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes. Syst. Man Cybern. IEEE Trans. 1, 28–44 (1973)CrossRefMathSciNetGoogle Scholar
  22. 22.
    E.H. Mamdani, Application of fuzzy algorithms for control of simple dynamic plant. Electr. Eng. Proc. Inst. 121(12), 1585–1588 (1974)CrossRefGoogle Scholar
  23. 23.
    C.-W. Xu, Y.-Z. Lu, Fuzzy model identification and self-learning for dynamic systems. Syst. Man Cybern. IEEE Trans. 17(4), 683–689 (1987)CrossRefMATHGoogle Scholar
  24. 24.
    M. Sugeno, T. Yasukawa, A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1(1), 7–31 (1993)CrossRefGoogle Scholar
  25. 25.
    Y. Nakamori, M. Ryoke, Identification of fuzzy prediction models through hyperellipsoidal clustering. Syst. Man Cybern. IEEE Trans. 24(8), 1153–1173 (1994)CrossRefGoogle Scholar
  26. 26.
    A. Kandel, G. Langholz, Fuzzy Control Systems. (CRC press, Boca Raton, 1994)Google Scholar
  27. 27.
    A. Lotfi, A.C. Tsoi, Learning fuzzy inference systems using an adaptive membership function scheme. IEEE Trans. Syst Man Cybern. Part B Cybern. 26(2), 326–331 (1996)CrossRefGoogle Scholar
  28. 28.
    K. Nozaki, H. Ishibuchi, H. Tanaka, Adaptive fuzzy rule-based classification systems. Fuzzy Syst. IEEE Trans. 4(3), 238–250 (1996)CrossRefGoogle Scholar
  29. 29.
    Y.-Z. Lu, Industrial Intelligent Control: Fundamentals and Applications. (John Wiley & Sons, Chichester, 1996)Google Scholar
  30. 30.
    S.A. Manesis, D.J. Sapidis, R.E. King, Intelligent control of wastewater treatment plants. Artif. Intell. Eng. 12(3), 275–281 (1998)CrossRefGoogle Scholar
  31. 31.
    S. Passino, K.M. Yurkovich, K.M. Passino, S. Yurkovich, Fuzzy control. Citeseer 42, 498 (1998)Google Scholar
  32. 32.
    S. Sumathi, S. Panneerselvam, Computational Intelligence Paradigms: Theory and Applications Using MATLAB. (CRC Press, New York, 2010)Google Scholar
  33. 33.
    P.B. Venuto, E.T. Habib Jr, Fluid Catalytic Cracking with Zeolite Catalysts (Marcel Dekker, New York, 1979)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineerCalifornia State UniversityFullertonUSA

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