Conference item
Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC
- Abstract:
- Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have been proposed to solve this problem, such as robust or stochastic MPC, but they can be computationally expensive when the system is nonlinear. Recent advances in optimal control theory have shown that concepts from convex optimization, tube-based MPC, and difference of convex functions (DC) enable stable and robust online process control. The approach is based on systematic DC decompositions of the dynamics and successive linearizations around feasible trajectories. By convexity, the linearization errors can be bounded tightly and treated as bounded disturbances in a robust tube-based MPC framework. However, finding the DC composition can be a difficult task. To overcome this problem, we used a neural network with special convex structure to learn the dynamics in DC form and express the uncertainty sets using simplices to maximize the product formation rate of a cultivation with uncertain substrate concentration in the feed. The results show that this is a promising approach for computationally tractable data-driven robust MPC of bioprocesses.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 611.5KB, Terms of use)
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- Publisher copy:
- 10.1016/B978-0-443-28824-1.50284-2
Authors
- Publisher:
- Elsevier
- Host title:
- Proceedings of the European Symposium on Computer Aided Process Engineering and International Symposium on Process Systems Engineering (ESCAPE34-PSE 2024)
- Journal:
- Computer Aided Chemical Engineering More from this journal
- Volume:
- 53
- Pages:
- 1699-1704
- Publication date:
- 2024-06-26
- Acceptance date:
- 2023-12-14
- Event title:
- European Symposium on Computer Aided Process Engineering and International Symposium on Process Systems Engineering (ESCAPE34-PSE 2024)
- Event location:
- Florence, Italy
- Event website:
- https://www.aidic.it/escape34-pse24/
- Event start date:
- 2024-06-02
- Event end date:
- 2024-06-06
- DOI:
- ISSN:
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1570-7946
- Language:
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English
- Pubs id:
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1595524
- Local pid:
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pubs:1595524
- Deposit date:
-
2024-01-06
Terms of use
- Copyright holder:
- Elsevier
- Copyright date:
- 2024
- Rights statement:
- © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- This paper was presented at the 34th European Symposium on Computer Aided Process Engineering and International Symposium on Process Systems Engineering (ESCAPE34 - PSE 2024), 2nd - 6th June 2024, Florence, Italy. This is the accepted manuscript version of the article. The final version is available online from Elsevier at https://dx.doi.org/10.1016/B978-0-443-28824-1.50284-2
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