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Ensemble Learning for bioprocess dynamic modelling and prediction
  • +3
  • Max Mowbray,
  • Ehecatl del Rio-Chanona,
  • Irina Harun,
  • Wagner Jonathan L.,
  • Klaus Hellgardt,
  • Dongda Zhang
Max Mowbray
University of Manchester
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Ehecatl del Rio-Chanona
Imperial College London
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Irina Harun
Imperial College London
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Wagner Jonathan L.
Loughborough University
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Klaus Hellgardt
Imperial College London
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Dongda Zhang
University of Manchester
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Peer review status:UNDER REVIEW

12 Mar 2020Submitted to Biotechnology and Bioengineering
13 Mar 2020Assigned to Editor
13 Mar 2020Submission Checks Completed
17 Mar 2020Reviewer(s) Assigned
04 May 2020Review(s) Completed, Editorial Evaluation Pending

Abstract

Machine learning techniques have been successfully used to simulate and optimise bioprocesses. This study explores the feasibility to apply Gradient Boosting, an emerging Ensemble Learning algorithm, which combines weak learners to generate better predictions for bioprocess dynamic modelling and prediction. A thorough procedure was presented for Gradient Boosting based data-driven model construction. Different case studies were employed including fermentation and algal photo-production processes. Given that generating a large size of experimental data for model training is time consuming and challenging to many bioprocesses, this work launched a first investigation on the data efficiency of Gradient Boosting by comparing its predictive capability against the predominantly used artificial neural networks. By carrying out a series of experimental verifications over a broad spectrum of process operating conditions, this study concluded that Gradient Boosting may have several advantages in small experimental datasets and can outperform artificial neural networks for bioprocess predictive modelling, indicating its potential for future bioprocess digitalisation and optimisation.