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 ( G E ) 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 conflicting 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 G E -models.

中文翻译：

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用于参数化过量吉布斯能量模型的多准则优化

摘要 热力学模型包含根据实验数据调整的参数。通常，不同数据集的最优描述需要不同的参数。多标准优化 (MCO) 是获得折衷的合适方法。此处针对 Gibbs 过剩能量 ( GE ) 模型演示了这一点。例如，NRTL 模型应用于三个二元系统（包含水、2-丙醇和 1-戊醇）。对于每个系统，都考虑了不同的目标（气液平衡、液液平衡和过量焓的描述）。使用自适应数值算法解决由此产生的 MCO 问题。它产生了帕累托前沿，它全面概述了给定模型对给定冲突数据的描述能力。从帕累托前沿来看，可以以指示的方式选择对给定应用程序特别有利的解决方案。当前工作中的示例展示了 MCO 方法用于参数化 GE 模型的好处。