Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses.pdf (1.25 MB)

Comparison of physics‐based and data‐driven modelling techniques for dynamic optimisation of fed‐batch bioprocesses

Download (1.25 MB)
journal contribution
posted on 13.08.2019 by Ehecatl Antonio Del Rio‐Chanona, Nur Rashid Ahmed, Jonathan Wagner, Yinghua Lu, Dongda Zhang, Keju Jing
The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics‐based and data‐driven models for the dynamic optimisation of long‐term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting‐edge data‐driven model to compute open‐loop optimisation strategies for the production of microalgal lutein during a fed‐batch operation. Light intensity and nitrate inflow rate are used as control variables given their key impact on biomass growth and lutein synthesis. By employing different optimisation algorithms, several optimal control sequences were computed. Due to the distinct model construction principles and sophisticated process mechanisms, the physics‐based and the data‐driven models yielded contradictory optimisation strategies. The experimental verification confirms that the data‐driven model predicted a closer result to the experiments than the physics‐based model. Both models succeeded in improving lutein intracellular content by over 40% compared to the highest previous record; however, the data‐driven model outperformed the kinetic model when optimising total lutein production and achieved an increase of 40–50%. This indicates the possible advantages of using data‐driven modelling for optimisation and prediction of complex dynamic bioprocesses, and its potential in industrial bio‐manufacturing systems.

Funding

National Natural Science Foundation of China No. 21776232 and No. 21736009

EPSRC project (EP/P016650/1)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Biotechnology and Bioengineering

Volume

116

Issue

11

Pages

2971 - 2982

Publisher

Wiley

Version

AM (Accepted Manuscript)

Rights holder

© Wiley Periodicals, Inc.

Publisher statement

This is the peer reviewed version of the following article: Del Rio‐Chanona EA, Ahmed NR, Wagner J, Lu Y, Zhang D, Jing K. Comparison of physics‐based and data‐driven modelling techniques for dynamic optimisation of fed‐batch bioprocesses. Biotechnology and Bioengineering. 2019;116:2971–2982, which has been published in final form at https://doi.org/10.1002/bit.27131. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Acceptance date

22/07/2019

Publication date

2019-07-30

Copyright date

2019

ISSN

0006-3592

eISSN

1097-0290

Language

en

Depositor

Dr Jonathan Wagner

Exports

Logo branding

Categories

Exports