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Multivariable robust tube-based nonlinear model predictive control of mammalian cell cultures
Systems, Estimation, Control and Optimization (SECO) Group, Faculty of Engineering, University of Mons, 31, Boulevard Dolez, 7000 Mons, Belgium.ORCID iD: 0000-0002-1688-167X
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.ORCID iD: 0000-0003-2753-4152
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO. (Cell Technology group)ORCID iD: 0000-0002-5370-4621
2024 (English)In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 183, article id 108592Article in journal (Refereed) Published
Abstract [en]

In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor defined by lower and upper safety bounds. The latter are assimilated to the well-known tube-based paradigm which is used to formulate the corresponding robust NMPC problem. Both classical and tube-based NMPC performances are assessed in numerical simulations where specific key-species are regulated while dealing with an uncertain plant model. The capability of the tube-based method to reduce the impact of the parameter variations on the state trajectories and the violation of the constraints is highlighted, suggesting the transfer of the method on a real pharmaceutical process.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 183, article id 108592
National Category
Control Engineering Bioprocess Technology
Identifiers
URN: urn:nbn:se:kth:diva-343519DOI: 10.1016/j.compchemeng.2024.108592ISI: 001170759300001Scopus ID: 2-s2.0-85183112280OAI: oai:DiVA.org:kth-343519DiVA, id: diva2:1838173
Funder
EU, Horizon 2020, 777397
Note

QC 20240326

Available from: 2024-02-15 Created: 2024-02-15 Last updated: 2024-03-26Bibliographically approved

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Mäkinen, MeeriChotteau, Véronique

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Dewasme, LaurentMäkinen, MeeriChotteau, Véronique
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