Multi-criteria Optimization for Parametrizing Excess Gibbs Energy Models

Thermodynamic models contain parameters which are adjusted to experimentaldata. Usually, optimal descriptions of different data sets require differentparameters. Multi-criteria optimization (MCO) is an appropriate wayto obtain a compromise. This is demonstrated here for Gibbs excess energy(GE) models. As an example, the NRTL model is applied to the three binarysystems (containing water, 2-propanol, and 1-pentanol). For each system,different objectives are considered (description of vapor-liquid equilibrium,liquid-liquid equilibrium, and excess enthalpies). The resulting MCO problemsare solved using an adaptive numerical algorithm. It yields the Paretofront, which gives a comprehensive overview of how well the given model candescribe the given conicting data. From the Pareto front, a solution that is particularly favorable for a given application can be selected in an instructedway. The examples from the present work demonstrate the benefits of theMCO approach for parametrizing GE-models.