Antti Koistinen
Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, Finland
Markku Ohenoja
Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, Finland
Jani Tomperi
Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, Finland
Mika Ruusunen
Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, Finland
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https://doi.org/10.3384/ecp20176365Published in: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland
Linköping Electronic Conference Proceedings 176:52, p. 365-372
Published: 2021-03-03
ISBN: 978-91-7929-731-2
ISSN: 1650-3686 (print), 1650-3740 (online)
Digital twins for performance-oriented applications in industrial environments require systematic model maintenance. Model adaptation requires efficient optimization tools and continuous evaluation of measurement quality. The adaptation and model performance evaluation are based on the modeling error, making the adaptation prone also to the measurement errors. In this paper, a framework for combining model adaptation and measurement quality assurance are discussed. Two examples with simulated industrial-scale biopharmaceutical penicillin fermentation are presented to illustrate the usability of the framework.
