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dc.contributor.author
Narayanan, Harini
dc.contributor.author
Behle, Lars
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Luna, Martín Francisco
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Sokolov, Michael
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Guillén Gosálbez, Gonzalo
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Morbidelli, Massimo
dc.contributor.author
Butté, Alessandro
dc.date.available
2021-12-27T13:09:23Z
dc.date.issued
2020-05
dc.identifier.citation
Narayanan, Harini; Behle, Lars; Luna, Martín Francisco; Sokolov, Michael; Guillén Gosálbez, Gonzalo; et al.; Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture; John Wiley & Sons Inc; Bioengineering And Biotechnology; 117; 9; 5-2020; 2703-2714
dc.identifier.issn
0006-3592
dc.identifier.uri
http://hdl.handle.net/11336/149276
dc.description.abstract
In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model-based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data-driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid-EKF framework, that is, hybrid models coupled with an extended Kalman filter for real-time monitoring, control, and automated decision-making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid-EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state-of-the-art soft sensor tools.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Inc
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ADAPTIVE CONTROL
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BIOPROCESSING
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EXTENDED KALMAN FILTER
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HYBRID MODELS
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PROCESS MONITORING
dc.subject.classification
Biotecnología Industrial
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Biotecnología Industrial
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INGENIERÍAS Y TECNOLOGÍAS
dc.title
Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-11-08T17:52:04Z
dc.journal.volume
117
dc.journal.number
9
dc.journal.pagination
2703-2714
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva Jersey
dc.description.fil
Fil: Narayanan, Harini. Institute of Chemical and Bioengineering; Suiza
dc.description.fil
Fil: Behle, Lars. Institute of Chemical and Bioengineering; Suiza
dc.description.fil
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
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Fil: Sokolov, Michael. Institute of Chemical and Bioengineering; Suiza
dc.description.fil
Fil: Guillén Gosálbez, Gonzalo. Institute of Chemical and Bioengineering; Suiza
dc.description.fil
Fil: Morbidelli, Massimo. Politecnico di Milano; Italia
dc.description.fil
Fil: Butté, Alessandro. No especifíca;
dc.journal.title
Bioengineering And Biotechnology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/bit.27437
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/bit.27437
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