Multi-criteria Optimization for Parametrizing Excess Gibbs Energy Models

Ester Forte, Aditya Kulkarni, Jakob Burger, Michael Bortz, Karl-Heinz Küfer and Hans Hasse

No 4fp9w, OSF Preprints from Center for Open Science

Abstract: Thermodynamic models contain parameters which are adjusted to experimental data. Usually, optimal descriptions of different data sets require different parameters. Multi-criteria optimization (MCO) is an appropriate way to obtain a compromise. This is demonstrated here for Gibbs excess energy (GE) models. As an example, the NRTL model is applied to the three binary systems (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 problems are solved using an adaptive numerical algorithm. It yields the Pareto front, which gives a comprehensive overview of how well the given model can describe the given conicting data. From the Pareto front, a solution that is particularly favorable for a given application can be selected in an instructed way. The examples from the present work demonstrate the benefits of the MCO approach for parametrizing GE-models.

Date: 2021-10-26
New Economics Papers: this item is included in nep-ene
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:4fp9w

DOI: 10.31219/osf.io/4fp9w

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