Adaptive predictive control of bioprocesses with constraint-based modeling and estimation
- Author(s)
- Banafsheh Jabarivelisdeh, Lisa Carius, Rolf Findeisen, Steffen Waldherr
- Abstract
Control of biotechnological processes is currently recipe-based with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, model-based optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of model-based control, it is crucial to use flexible and adaptive control strategies which address biological variability while compensating for uncertainties. In this work, we present an approach for adaptive control of a bioprocess based on dynamic flux balance models. A previously developed bilevel approach for bioprocess optimization is implemented inside a model predictive control (MPC) routine. To account for model uncertainties, a moving horizon estimation algorithm is combined with the MPC in order to estimate uncertain parameters of the underlying model online for different metabolic modes. We apply this method to maximize the productivity of a target metabolite under microaerobic conditions by adapting the degree of oxygen-limitation online.
- Organisation(s)
- Functional and Evolutionary Ecology
- External organisation(s)
- Otto-von-Guericke-Universität Magdeburg, Katholieke Universiteit Leuven
- Journal
- Computers and Chemical Engineering
- Volume
- 135
- ISSN
- 0098-1354
- DOI
- https://doi.org/10.1016/j.compchemeng.2020.106744
- Publication date
- 04-2020
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 209002 Bioprocess technology
- Keywords
- ASJC Scopus subject areas
- General Chemical Engineering, Computer Science Applications
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/cc6f855e-41c7-4dde-a86e-0ab58b27ca54