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journal article Nov 08, 2021

A transfer learning approach for predictive modeling of bioprocesses using small data

Alexander W. Rogers Fernando Vega‐Ramon Jiangtao Yan Ehecatl A. del Río‐Chanona Keju Jing ORCID Dongda Zhang ORCID
Biotechnology and Bioengineering Vol. 119 No. 2 pp. 411-422 · Wiley
View at Publisher Save 10.1002/bit.27980
Abstract
AbstractPredictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain‐specific model, provides a promising solution. While transfer learning has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within biochemical engineering has not been systematically explored. In this study, we demonstrated the benefits of transfer learning when applied to predict dynamic behaviors of new biochemical processes. Two different case studies were presented to investigate the accuracy, reliability, and advantage of this innovative modeling approach. We thoroughly discussed the different transfer learning strategies and the effects of topology on transfer learning, comparing the performance of the transfer learning models against benchmark kinetic and data‐driven models. Furthermore, strong connections between the underlying process mechanism and the transfer learning model's optimal structure were highlighted, suggesting the interpretability of transfer learning to enable more accurate prediction than a naive data‐driven modeling approach. Therefore, this study shows a novel approach to effectively combining data from different resources for bioprocess simulation.
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Biochemical Engineering Journal
Metrics
49
Citations
33
References
Details
Published
Nov 08, 2021
Vol/Issue
119(2)
Pages
411-422
License
View
Authors
A
Alexander W. Rogers

Department of Chemical Engineering and Analytical Science The University of Manchester Manchester UK

F
Fernando Vega‐Ramon

Department of Chemical Engineering and Analytical Science The University of Manchester Manchester UK

J
Jiangtao Yan

Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering Xiamen University Xiamen China

E
Ehecatl A. del Río‐Chanona

Department of Chemical Engineering, Imperial College London South Kensington Campus London UK

K
Keju Jing

Department of Chemical Engineering and Analytical Science The University of Manchester Manchester UK

D
Dongda Zhang

Department of Chemical Engineering and Analytical Science The University of Manchester Manchester UK

Funding
National Natural Science Foundation of China Award: 21776232
Engineering and Physical Sciences Research Council Award: EP/T031123/1
Cite This Article
Alexander W. Rogers, Fernando Vega‐Ramon, Jiangtao Yan, et al. (2021). A transfer learning approach for predictive modeling of bioprocesses using small data. Biotechnology and Bioengineering, 119(2), 411-422. https://doi.org/10.1002/bit.27980
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