USE OF EVOLUTIONARY ALGORITHMS FOR THE CALCULATION OF GROUP-CONTRIBUTION PARAMETERS IN ORDER TO PREDICT THERMODYNAMIC PROPERTIES - PART 1 -GENETIC ALGORITHMS

The computation of parameters for group contribution models in order t o predict thermodynamic properties usually leads to a multiparameter o ptimization problem. The model parameters are calculated using a regre ssion method and applying certain error criteria. A complex objective function occurs for which an optimization algorithm has to find the gl obal minimum. For simple increment or group contribution models it is often sufficient to use simplex or gradient algorithms. However, if th e model contains complex terms such as sums of exponential expressions , the search of the global or even of an fairly good optimum becomes r ather difficult. Evolutionary Algorithms represent a possibility for s olving such problems. In most cases, the use of biological principles for optimization problems yields satisfactory results. A genetic algor ithm and an optimization method using an evolutionary strategy were pr ogrammed at the Institute for Thermodynamics at the University of Dort mund and were tested with an Enthalpy Based Group Contribution Model ( EBGCM). The results obtained with these procedures were compared with the results obtained using a simplex algorithm. A test system was crea ted and the corresponding objective function was examined in detail. F or this purpose, 3D-plots were produced by varying two out of six mode l parameters. In this paper, the development of a genetic algorithm is presented and the fitting procedure of the model parameters is discus sed. Part 2 of this article series will deal with the efficiency of ev olutionary strategies applied to such a prototype of non-linear regres sion problems. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.