Evolutionary algorithms for the optimization of Modified UNIFAC parameters

Over the last two decades, the UNIFAC group contribution method has been wi dely used for the prediction of vapor-liquid equilibria. For application to refrigerant mixtures, additional structure groups were introduced and veri fied by fitting the interaction parameters with satisfactory results. Analo gously, the new structural groups should be implemented in the Modified UNI FAC method, which uses temperature-dependent interaction parameters and adj ustable group surface area and volume parameters to achieve a better descri ption of the behaviour of the mixtures. In order to fit these parameters to experimental data, an optimization problem with 386 variables was solved. This was done by applying Evolutionary Algorithms to mathematical optimizat ion, involving the mutation-selection principle known from biology. The opt imum interplay of many well-known strategies as well as the use of parallel computers resulted in levels well below the local extremes found using a c onventional search method. The EVOBOX program package can be used for any m inimization task with multivariable functions. (C) 1998 Elsevier Science Lt d. All rights reserved.