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Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

Espinel‐Ríos, Sebastián ; Bettenbrock, Katja ; Klamt, Steffen ; Findeisen, Rolf (2022):
Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover. (Publisher's Version)
In: AIChE Journal, 68 (4), John Wiley & Sons, e-ISSN 1547-5905,
DOI: 10.26083/tuprints-00021546,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover
Language: English
Abstract:

We present a constrained model‐based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on the core idea of manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting ATP use towards an energetically possibly suboptimal path— allows increasing the metabolic flux towards the product, thereby enhancing product yields and specific productivities. However, this often comes at the expense of lower biomass yields and reduced volumetric productivities. To maximize the overall efficiency, we formulate ATP wasting as a model‐based optimal control problem. This allows for balancing trade‐offs between different objectives such as product yield and volumetric productivity for batch fermentations. Unlike static metabolic control, one obtains a higher degree of flexibility, adaptability, and competitiveness. This can be advantageous towards achieving a sustainable and economically efficient biotechnology industry. To compensate for model‐plant mismatch, disturbances, and uncertainties, we propose not only solving the optimal control problem once. Instead, we exploit the concept of moving horizon model predictive control combined with constraint‐based dynamic modeling to capture the fermentation dynamics. The approach is underlined considering the industrially relevant bioproduction of lactate by Escherichia coli. We discuss practical challenges for the described control strategy and provide an outlook towards future developments.

Journal or Publication Title: AIChE Journal
Volume of the journal: 68
Issue Number: 4
Place of Publication: Darmstadt
Publisher: John Wiley & Sons
Collation: 13 Seiten
Uncontrolled Keywords: dynamic enzyme‐cost flux balance analysis, enforced ATP wasting, fermentation, model predictive control, model‐based control, optimal control
Classification DDC: 500 Naturwissenschaften und Mathematik > 540 Chemie
500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS)
Date Deposited: 01 Jul 2022 11:36
Last Modified: 08 Sep 2022 09:05
DOI: 10.26083/tuprints-00021546
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-215468
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21546
PPN: 498980464
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