Zhang, D;
Del Rio-Chanona, EA;
Petsagkourakis, P;
Wagner, J;
(2019)
Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization.
Biotechnology and Bioengineering
, 116
(11)
pp. 2919-2930.
10.1002/bit.27120.
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Abstract
Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.
| Type: | Article |
|---|---|
| Title: | Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/bit.27120 |
| Publisher version: | https://doi.org/10.1002/bit.27120 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | bioprocess optimization, data recalibration, fed-batch operation, kinetic modeling, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10113532 |
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