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Parameter Estimation Strategies in Thermodynamics

1
Fraunhofer Center for Machine Learning and ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany
2
Lonza AG, Lonzastrasse, 3930 Visp, Switzerland
3
BASF SE, Carl-Bosch-Straße 38, 67056 Ludwigshafen, Germany
*
Author to whom correspondence should be addressed.
ChemEngineering 2019, 3(2), 56; https://doi.org/10.3390/chemengineering3020056
Received: 26 February 2019 / Revised: 23 May 2019 / Accepted: 26 May 2019 / Published: 1 June 2019
(This article belongs to the Special Issue Progress in Thermal Process Engineering)
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Abstract

Many thermodynamic models used in practice are at least partially empirical and thus require the determination of certain parameters using experimental data. However, due to the complexity of the models involved as well as the inhomogeneity of available data, a straightforward application of basic methods often does not yield a satisfactory result. This work compares three different strategies for the numerical solution of parameter estimation problems, including errors both in the input and in the output variables. Additionally, the new idea to apply multi-criteria optimization techniques to parameter estimation problems is presented. Finally, strategies for the estimation and propagation of the model errors are discussed. View Full-Text
Keywords: regression; model adjustment; optimization regression; model adjustment; optimization
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Höller, J.; Bickert, P.; Schwartz, P.; von Kurnatowski, M.; Kerber, J.; Künzle, N.; Lorenz, H.-M.; Asprion, N.; Blagov, S.; Bortz, M. Parameter Estimation Strategies in Thermodynamics. ChemEngineering 2019, 3, 56.

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