Rio-Chanona_et_al-2018-AIChE_Journal.pdf (1.54 MB)
Download fileDeep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design
journal contribution
posted on 2018-12-03, 15:37 authored by Ehecatl Antonio del Rio-Chanona, Jonathan WagnerJonathan Wagner, Haider Ali, Fabio Fiorelli, Dongda Zhang, Klaus HellgardtIdentifying optimal photobioreactor configurations and process operating conditions is
critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed
by testing numerous design scenarios from integrated physical models coupling
computational fluid dynamics and kinetic modelling. However, this approach presents
computational intractability and numerical instabilities when simulating large-scale systems,
causing time-intensive computing efforts and infeasibility in mathematical optimization.
Therefore, we propose an innovative data-driven surrogate modelling framework which
considerably reduces computing time from months to days by exploiting state-of-the-art deep
learning technology. The framework built upon a few simulated results from the physical
model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then
adopts a hybrid stochastic optimization algorithm to explore untested processes and find
optimal solutions. Through verification, this framework was demonstrated to have
comparable accuracy to the physical model. Moreover, multi-objective optimization was
incorporated to generate a Pareto-frontier for decision-making, advancing its applications in
complex biosystems modelling and optimization.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640720. This project has also received funding from the EPSRC project (EP/P016650/1, P65332).
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Chemical Engineering
Published in
AIChE JournalCitation
DEL RIO-CHANONA, E.A. ... et al., 2019. Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design. AIChE Journal, 65(3), pp. 915-923.Publisher
© 2018 American Institute of Chemical Engineers (AIChE). Published by WileyVersion
- AM (Accepted Manuscript)
Publisher statement
This is the peer reviewed version of the following article: DEL RIO-CHANONA, E.A. ... et al., 2019. Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design. AIChE Journal, 65(3), pp. 915-923, which has been published in final form at https://doi.org/10.1002/aic.16473. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Acceptance date
2018-11-09Publication date
2018-11-15ISSN
0001-1541Publisher version
Language
- en