Advertisement

Encapsulated Evolution strategies for the determination of group contribution model parameters in order to predict thermodynamic properties

  • Hannes Geyer
  • Peter Ulbig
  • Siegfried Schulz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

The computation of parameters for group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem. The model parameters are calculated using a regression method and applying certain error criteria. A complex objective function occurs for which an optimization algorithm has to find the global minimum. For simple increment or group contribution models it is often sufficient to use deterministically working optimization algorithms. However, if the model contains parameters in complex terms such as sums of exponential expressions, the optimization problem will be a non-linear regression problem and the search of the global optimum becomes rather difficult. In this paper we report, that conventional multimembered (Μ,λ)- and (Μ+λ.)-Evolution Strategies could not cope with such non-linear regression problems without further ado, whereas multimembered encapsulated Evolution Strategies with multi-dimensional step length control are better suited for the optimization problem considered here.

Keywords

Step Length Objective Variable Group Contribution Method Strategic Variable Definition Area 
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.
    Bäck, Th., Evolutionary Algorithms in Theory and Practice, Informatik Centrum Dortmund, Oxford University Press, New York/Oxford (1996)Google Scholar
  2. 2.
    Fredenslund, A., Jones, R. L. and Prausnitz, J. M., Group-contribution estimation of activity coefficients in nonideal liquid mixtures. AIChE Journal, 21, (1975) 1086–1099CrossRefGoogle Scholar
  3. 3.
    Friese, T., Ulbig, P., and Schulz, S., Use of Evolutionary Algorithms for the Calculation of Group Contribution Parameters in order to Predict Thermodynamic Properties. Part 1: Genetic Algorithms, Computers & Chemical Engineering (1998) (in press)Google Scholar
  4. 4.
    Geyer, H., Ulbig, P., and Schulz, S., Use of Evolutionary Algorithms for the Calculation of Group Contribution Parameters in order to Predict Thermodynamic Properties. Part 2: Evolution Strategies, Computers & Chemical Engineering (1998) (submitted)Google Scholar
  5. 5.
    Neider, J. A., Mead, R., A simplex method for function minimization, In: Computer Journal, 7(1965)Google Scholar
  6. 6.
    Rechenberg, I., Evolutionsstrategie '94, Werkstatt Bionik und Evolutionstechnik, Band 1, Friedrich Frommann, Stuttgart (1994)Google Scholar
  7. 7.
    Rudolph, G., On correlated mutations in evolution strategies. In R. Männer and B. Manderick, Parallel Problem Solving from Nature, 2, Elsevier, Amsterdam (1992) 105–114Google Scholar
  8. 8.
    Ulbig, P., Entwicklung der Gruppenbeitragsmodelle UNIVAP & EBGCM zur Vorhersage thermodynamischer Größen sowie Bestimmung der Modellparameter unter Verwendung evolutionärer Algorithmen, PhD Thesis, Institute for Thermodynamics, University of Dortmund (1996)Google Scholar
  9. 9.
    Ulbig, P., Friese, T., Geyer, H., Kracht, C., and Schulz, S., Prediction of thermodynamic properties for chemical engineering with the aid of Computational Intelligence. In: Progress in Connectionist-Based Information Systems — Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, Vol. 2, Springer, New York (1997) 1259–1262Google Scholar
  10. 10.
    Schwefel, H.-P., Numerical Optimization of Computer Models, Wiley, Chichester (1981)Google Scholar
  11. 11.
    Schwefel, H.-P., Evolution and Optimum Seeking, Wiley, New York (1995)Google Scholar
  12. 12.
    Weidlich, U., Gmehling, J.: A modified UNIFAC model. Ind. Eng. Chem. Res., Vol. 26. (1987) 1372CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Hannes Geyer
    • 1
    • 2
  • Peter Ulbig
    • 1
    • 2
  • Siegfried Schulz
    • 1
    • 2
  1. 1.Institute for ThermodynamicsDortmundGermany
  2. 2.Department of Chemical EngineeringUniversity of Dortmund Member of the Collaborative Research CenterGermany

Personalised recommendations