Parallel Evolutionary Algorithms for Optimizing the Unifac Matrix on Workstation Clusters

  • Joachim K. Axmann
  • Michael Kleiber
  • Andreas Kothrade


Over the last two decades, the UNIFAC group contribution method has come to the fore in the prediction of vapor-liquid equilibria. In order to apply it to refrigerant mixtures, it was necessary to implement additional structure groups, whose interaction amongst each other and with the old groups had to be described by fitting the relevant parameters with respect to measurement data. This led to the problem of minimizing an objective function with approx. 200 variables.

This was done by applying evolutionary algorithms to this mathematical optimization problem, involving the mutation and selection processes known from biology. The optimum interplay of classic evolution strategies with new developed extensions as well as the use of parallel computers lead to results well below the local extremes found by using conventional search methods. The EVOBOX program package can be used for any minimization tasks with multivariable functions.


Evolutionary Algorithm Quality Function Step Size Control Refrigerant Mixture Parallel Virtual Machine 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Joachim K. Axmann
    • 1
  • Michael Kleiber
    • 2
  • Andreas Kothrade
    • 1
  1. 1.Institute for Spaceflight and Reactor TechnologyTechnical University of BraunschweigBraunschweigGermany
  2. 2.ProzeßtechnikHoechst AGFrankfurt a. M.Germany

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