Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.
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.
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