Fed-batch bioprocess; Mammalian cells; Model predictive control; Pharmaceutical process; Robust control; Chemical Engineering (all); Computer Science Applications; General Chemical Engineering
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.
F107 - Systèmes, Estimation, Commande et Optimisation
Research institute :
R100 - Institut des Biosciences
European Projects :
H2020 - 777397 - iConsensus - Integrated control and sensing platform for biopharmaceutical cultivation process high-throughput development and production
Funders :
EU - European Union [BE]
Funding text :
Following the recently emerging 5.0 industry era ( European Commission and Directorate-General for Research and Innovation et al., 2021 ), combined to the increasing demand for health care, therapeutic product manufacturing have been intensively supported by Process Analytical Technologies (PAT). The combination of PAT with software solutions indeed allows to optimize the course of bioprocesses and, in turn, the product development chain. The latter statement is supported by the resulting economical impact from time and consumable reductions, and the improving comprehension of the process mechanisms.
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