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.
In: AIChE Journal, 2022, 68 (4)
doi: 10.26083/tuprints-00021546
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Espinel‐Ríos, Sebastián ; Bettenbrock, Katja ; Klamt, Steffen ; Findeisen, Rolf |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | John Wiley & Sons |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | AIChE Journal |
| Jahrgang/Volume einer Zeitschrift: | 68 |
| (Heft-)Nummer: | 4 |
| Kollation: | 13 Seiten |
| DOI: | 10.26083/tuprints-00021546 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21546 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | dynamic enzyme‐cost flux balance analysis, enforced ATP wasting, fermentation, model predictive control, model‐based control, optimal control |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-215468 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 540 Chemie 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 01 Jul 2022 11:36 |
| Letzte Änderung: | 07 Jul 2022 09:14 |
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- Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover. (deposited 01 Jul 2022 11:36) [Gegenwärtig angezeigt]
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