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  4. Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train
 
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Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Citation Link: https://doi.org/10.15480/882.5128
Publikationstyp
Journal Article
Publikationsdatum
2020-12-29
Sprache
English
Author
Hernández Rodríguez, Tanja 
Posch, Christoph 
Pörtner, Ralf orcid-logo
Frahm, Björn 
Institut
Bioprozess- und Biosystemtechnik V-1 
DOI
10.15480/882.5128
TORE-URI
http://hdl.handle.net/11420/8490
Lizenz
https://creativecommons.org/licenses/by/4.0/
Enthalten in
Bioprocess and biosystems engineering 
Volume
44
Issue
4
Start Page
793
End Page
808
Citation
Bioprocess and Biosystems Engineering 44 (4) : 793-808 (2020-12-29)
Publisher DOI
10.1007/s00449-020-02488-1
Scopus ID
2-s2.0-85098250610
Publisher
Springer Berlin Heidelberg
Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.
Schlagworte
Bioprocess
Cell cultures
Dynamic parameter estimation
Moving horizon estimation
Prior knowledge
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