The Kalman Filter for the Supervision of Cultivation Processes.
Bioprocess supervision
Cultivation
Digital twin
Estimation
Kalman filter
Journal
Advances in biochemical engineering/biotechnology
ISSN: 0724-6145
Titre abrégé: Adv Biochem Eng Biotechnol
Pays: Germany
ID NLM: 8307733
Informations de publication
Date de publication:
2021
2021
Historique:
pubmed:
12
11
2020
medline:
28
4
2021
entrez:
11
11
2020
Statut:
ppublish
Résumé
In the era of technology and digitalization, the process industries are undergoing a digital transformation. The available process models, advance sensor technologies, enhanced computational power and a broad set of data analytical techniques enable solid bases for digital transformation in the biopharmaceutical industry.Among various data analytical techniques, the Kalman filter and its non-linear extensions are powerful tools for prediction of reliable process information. The combination of the Kalman filter with a virtual representation of the bioprocess, called digital twin, can provide real-time available process information. Incorporation of such variables in process operation can provide improved control performance with enhanced productivity.In this chapter the linear discrete Kalman filter, the extended Kalman filter and the unscented Kalman filters are described and a brief overview of applications of the Kalman filter and its non-linear extensions to bioreactors are presented. Furthermore, in a case study an example of the digital twin of the backer's yeast batch cultivation process is presented. A digital twin of a bioreactor mirrors the processes of the real bioreactor. It contains the physical parts, the process model and prediction algorithm to predict the bioprocess variables. These values could be used for optimization and control of the process.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
95-125Références
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