Hybrid physics-based and data-driven modelling for bioprocess online simulation and optimisation.pdf (823.52 kB)
Download file

Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation

Download (823.52 kB)
journal contribution
posted on 13.08.2019, 14:27 by Dongda Zhang, Ehecatl Antonio Del Rio‐Chanona, Panagiotis Petsagkourakis, Jonathan WagnerJonathan Wagner
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.

Funding

EPSRC project. Grant Number: EP/P016650/1

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Biotechnology and Bioengineering

Volume

116

Issue

11

Pages

2919 - 2930

Publisher

Wiley

Version

AM (Accepted Manuscript)

Rights holder

© Wiley Periodicals, Inc.

Publisher statement

This is the peer reviewed version of the following article: Zhang, D., Del Rio‐Chanona, E.A., Petsagkourakis, P., Wagner, J., 2019. Hybrid physics‐based and data‐driven modeling for bioprocess online simulation andoptimization. Biotechnology and Bioengineering, 116 (11), pp.2919-2930, which has been published in final form at https://doi.org/10.1002/bit.27120. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Acceptance date

09/07/2019

Publication date

2019-07-17

Copyright date

2019

ISSN

0006-3592

eISSN

1097-0290

Language

en

Depositor

Dr Jonathan Wagner