Conference article

Adaptation framework for an industrial digital twin

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

Download articlehttps://doi.org/10.3384/ecp20176365

Published 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

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Published: 2021-03-03

ISBN: 978-91-7929-731-2

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

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.

Keywords

digital twin, adaptation, framework, differential evolution

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