Advertisement

Experience with Hybrid Evolutionary/local Optimization for Process Design

  • E. S. Fraga
  • A. Žilinskas
Conference paper

Abstract

Process design requires the solution of mixed integer nonlinear programmes. Optimization procedures must be robust and efficient if they are to be incorporated in automated design systems. For heat integrated separation process design, a natural hybrid decomposition with these properties is possible. The decomposition is based on local search methods for the continuous design parameters for the processing units in the process and an evolutionary optimization procedure for the design of the heat exchanger network. A variety of local search methods are evaluated and it is shown that the Hooke & Jeeves algorithm, combined with a genetic algorithm, provides a robust, efficient solution method.

Keywords

Cost Model Distillation Column Local Search Method Automate Design System Reflux Ratio 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Grossmann I. A., Caballero J. A., Yeomans H. (1999), Mathematical programming approaches to the synthesis of chemical process systems. Korean J Chem Eng 16:407–426.CrossRefGoogle Scholar
  2. 2.
    Douglas J.M. (1988), Conceptual Design of Chemical Processes. McGraw-Hill International Editions.Google Scholar
  3. 3.
    Nishida N., Stephanopolous G., Westerberg A. W. (1981), A review of process synthesis. AIChE J. 27:321–351.CrossRefGoogle Scholar
  4. 4.
    Fraga E. S., Steffens M. A., Bogle I. D. L., Hind A. K. (2000). An object oriented framework for process synthesis and simulation. In: Malone M. F., Trainham J. A., Carnahan B. (eds.), Foundations of Computer-Aided Process Design, (AIChE Symposium Series, 323), 446–449.Google Scholar
  5. 5.
    Fraga E. S., McKinnon K. ?. ?. (1999). A scalable discrete optimization algorithm for heat integration in early design. In: Keil F., Mackens W., Voβ H., Werther J. (eds.), Scientific Computing in Chemical Engineering ?: Simulation, Image Processing, Optimization, and Control, 306–313.Google Scholar
  6. 6.
    Rathore R. N. S., Wormer K. A. van, Powers G. J. (1974), Synthesis of distillation systems with energy integration. AIChE J. 20:940–950.CrossRefGoogle Scholar
  7. 7.
    Novak Z., Kravanja Z., Grossmann I. ?. (1996), Simultaneous synthesis of distillation sequences in overall process schemes using an improved MNLP approach. Computers chem. Engng 20:1425–1440.CrossRefGoogle Scholar
  8. 8.
    Törn A., Žilinskas A. (1989), Global Optimization. Springer.Google Scholar
  9. 9.
    Kelley C. T. (1999), Iterative methods for optimization. SIAM.Google Scholar
  10. 10.
    Kuntzevich A., Kappel F. (1997). SolvOpt. the solver of nonlinear optimization problems. Technical report, Institution of Mathematics, Technical University of Graz.Google Scholar
  11. 11.
    Lewin D. R. (1998), A generalized method for HEN synthesis using stochastic optimization-II. the synthesis of cost-optimal networks. Computers chem. Engng 22:1387–1405.CrossRefGoogle Scholar
  12. 12.
    Fraga E. S., Patel R., Rowe G. W. A. (2001), A visual representation of process heat exchange as a basis for user interaction and stochastic optimization. Chem Eng Res Des 79:765–776.CrossRefGoogle Scholar
  13. 13.
    Parmee I. C. (2001), Evolutionary and adaptive computing in engineering design. Springer, N.Y.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • E. S. Fraga
    • 1
  • A. Žilinskas
    • 2
  1. 1.Centre for Process Systems Engineering, Department of Chemical EngineerinUCL (University College London)Torrington PlaceUK
  2. 2.Institute of Mathematics and InformaticsVytautas Magnus UniversityLithuaniaUSA

Personalised recommendations