Authors: Winz, Joschka
Nentwich, Corina
Engell, Sebastian
Title: Surrogate modeling of thermodynamic equilibria: applications, sampling and optimization
Language (ISO): en
Abstract: Models based on first principles are an effective way to model chemical processes. The quality of these depends critically on the accurate description of thermodynamic equilibria. This is provided by modern thermodynamic models, e.g., PC-SAFT, but they come with a high computational cost, which makes process optimization challenging. This can be addressed by using surrogate models to approximate the equilibrium calculations. A high accuracy of the surrogate model can be achieved by carefully choosing the points at which the original function is evaluated to create data for the training of the surrogate models, called sampling. Using a case study, different approaches to sampling are discussed and evaluated with a focus on new approaches to adaptive sampling.
Subject Headings: Machine learning
Process optimization
Surrogate modeling
Thermodynamic equilibria
Issue Date: 2021-09-27
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Appears in Collections:Lehrstuhl Systemdynamik und Prozessfuehrung

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