Advertisement

Discrete Optimization using String Encodings for the Synthesis of Complete Chemical Processes

  • Eric S. Fraga
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)

Abstract

The use of discrete programming techniques for the synthesis of process flowsheets in chemical engineering is a well established approach. Recently, improvements in the basic algorithms have been made to deal with the generation of complete processes, including heat exchange networks and processes with reactors, absorbers, flash units, etc. This paper describes a new approach to the use of dynamic programming using string encodings both for subproblem definition and for solution description. These encodings, combined with the use of dynamic hash tables, are used to implement a dynamic programming based optimization algorithm for the synthesis of chemical processes. The implementation shows an increase in both efficiency and usefulness.

Keywords

Global nonlinear optimization discrete programming process synthesis nonconvex optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R. Bañares-Alcántara and H. M. S. Lababidi, Design Support Systems for Process Engineering — II. KBDS: An Experimental Prototype, Computers chem. Engng, 19, 3, pp. 279–301 (1995).CrossRefGoogle Scholar
  2. 2.
    G. H. Ballinger, R. Bañares-Alcántara, D. Costello, E. S. Fraga, J. King, J. Krabbe, D. M. Laing, R. C. McKinnel, J. W. Ponton, N. Skilling, and M. W. Spenceley, Developing an Environment for Creative Process Design, Chemical Engineering Research & Design, 72, A3, pp. 316–324 (1994).Google Scholar
  3. 3.
    A. Aggarwal and C. A. Floudas, Synthesis of Heat Integrated Nonsharp Distillation Sequences, Computers chem. Engng, 16, 2, pp. 89–108 (1992).CrossRefGoogle Scholar
  4. 4.
    M. A. Duran and I. E. Grossman, Simultaneous Optimization and Heat Integration of Chemical Processes, AIChE J 32, p. 123 (1986).CrossRefGoogle Scholar
  5. 5.
    C. A. Floudas and G. E. Paules IV, A Mixed-Integer Nonlinear Programming Formulation for the Synthesis of Heat-integrated Distillation Sequences, Computers chem. Engng, 12, 6, pp. 531–546 (1988).CrossRefGoogle Scholar
  6. 6.
    N. Nishida, G. Stephanopolous, and A. W. Westerberg, A Review of Process Synthesis, AIChE 27, 3, pp. 321–351 (1981).CrossRefGoogle Scholar
  7. 7.
    J. E. Hendry and R. R. Hughes, Generating separation process flowsheets, Chem. Eng. Prog., 68 (6), pp. 71–76 (1972).Google Scholar
  8. 8.
    W. R. Johns and D. Romero, The Automated Generation and Evaluation of Process Flowsheets, Computers chem. Engng, 3, pp. 251–260 (1979).CrossRefGoogle Scholar
  9. 9.
    E. S. Fraga and K. I. M. McKinnon, CHiPS: A Process Synthesis Package, Chemical Engineering Research & Design, 72, A3, pp. 389–394 (1994).Google Scholar
  10. 10.
    C. Stair and E. S. Fraga, Optimization of Unit Operating Conditions for Heat Integrated Processes Using Genetic Algorithms, Proc. 1995 I.Chem.E. Research Event, pp. 95–97, Institution of Chemical Engineering, Rugby, U.K. (1995).Google Scholar
  11. 11.
    N. S. Dhallu and W. R. Johns, Synthesis of Distillation Trains with Heat Integration, I. Chem. E. Symp. Series, 109, pp. 23–42 (1988).Google Scholar
  12. 12.
    E. S. Fraga and K. I. M. McKinnon, The Use of Dynamic Programming with Parallel Computers for Process Synthesis, Computers chem. Engng, 18, 1, pp. 1–13 (1994).CrossRefGoogle Scholar
  13. 13.
    E. S. Fraga and K. I. M. McKinnon, Portable Code for Process Synthesis Using Workstation Clusters and Distributed Memory Multicomputers, Computers chem. Engng, 19, 6 /7, pp. 759–773 (1995).Google Scholar
  14. 14.
    B. W. Kernighan and D. M. Ritchie, The C Programming Language, Prentice-Hall International, Inc., London (1978).Google Scholar
  15. 15.
    P.-A. Larson, Dynamic Hashing, Comm. ACM, pp. 446–457 (1988).Google Scholar
  16. 16.
    E. Bartel, C Code Implementation of a Dynamic Hashing Algorithm, Institut fuer Informatik, TU Muenchen, Muenchen, Germany (1993). Available via Internet, URL ftp://ftp.inria.fr/prog/libraries/dynhash-1.0.shar.gz.Google Scholar
  17. 17.
    M. R. Fenske, Fractionation of Straight-Run Pennsylvania Gasoline, Ind. Eng. Chem., 24, 5, pp. 482–485 (1932).CrossRefGoogle Scholar
  18. 18.
    A. J. V. Underwood, Fractional Distillation of Multicomponent Mixtures, Chem. Eng. Prog., 44, 8, pp. 603–614 (1948).Google Scholar
  19. 19.
    E. R. Gilliland, Multi-component Rectification, Estimation of the Number of Theoretical Plates as a Function of the Reflux Ratio, Ind. Eng. Chem., 32, 9, pp. 1220–1223 (1940).CrossRefGoogle Scholar
  20. 20.
    R. N. S. Rathore, K. A. van Wormer, and G. J. Powers, Synthesis Strategies for Multicomponent Separation Systems with Energy Integration, AIChE J, 20, 3, pp. 491–502 (1974).CrossRefGoogle Scholar
  21. 21.
    National Engineering Laboratory, PPDS. Physical Properties Data Service, The Institution of Chemical Engineers, Rugby, England (1981).Google Scholar
  22. 13.
    J. M. Douglas, Conceptual Design of Chemical Processes, McGraw Hill (1988).Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Eric S. Fraga
    • 1
  1. 1.Department of Chemical EngineeringUniversity of EdinburghEdinburghScotland

Personalised recommendations