Model predictive control guided with optimal experimental design for pulse-based parallel cultivation

Kim Jong Woo, Niels Krausch, Judit Aizpuru, Tilman Barz, Sergio Lucia, Ernesto Martinez, Peter Neubauer, Mariano Nicolas Cruz Bournazou

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband ohne PräsentationBegutachtung

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Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With the recent developments in miniaturization and parallelization of cultivation platforms for high-throughput screening of optimal growth conditions massive amounts of informative data can be generated with few experiments. Increasing the quantity of the data means to increase the number of parameters and experimental design variables which might deteriorate the identifiability and hamper the online computation of optimal inputs. To reduce the problem complexity, in this work, we introduce an auxiliary controller at a lower level that tracks the optimal feeding strategy computed by a high-level optimizer in an online fashion. The hierarchical framework is especially interesting for the operation under constraints. The key aspect of this method are discussed together with an in silico study considering parallel glucose limited bacterial fed batch cultivations.
Titel13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022
Redakteure/-innenLuis Ricardez-Sandoval, Jesus Pico, Jay Lee, Min Jong
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Efficiency in Industrial Processes and Systems


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