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Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Keulen, Daphne, Hagen, Erik van der, Geldhof, Geoffroy, Le Bussy, Olivier, Pabst, Martin, Ottens, Marcel·2024·Biotechnology and Bioengineering
DOI 10.1002/bit.28454
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Abstract

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model‐based approaches enable to screen a broad range of the design‐space, in contrast to traditional statistical or heuristic‐based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model‐based approaches, can be speed‐limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.

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Cited by

Sorted by citations

Authors
Keulen, D., Hagen, E. V. D., Geldhof, G., Le Bussy, O., Pabst, M., & Ottens, M.
Affiliations
  • Department of Biotechnology Delft University of Technology Delft The Netherlands
  • GSK, Technical Research & Development—Microbial Drug Substance Rixensart Belgium
ORCID
Venue
Biotechnology and Bioengineering
Publisher
Wiley
Published
2023-05-31
Volume
121
Issue
8
Pages
2318-2331
ISSN
  • 0006-3592
  • 1097-0290
DOI
10.1002/bit.28454
Cited by
16
References
1
Language
en
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)

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