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Title: | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
Authors: | del Rio-Chanona, Ehecatl Antonio Wagner, Jonathan L. Ali, Haider Fiorelli, Fabio Zhang, Dongda Hellgardt, Klaus |
Keywords: | Surrogate modelling Convolutional neural network Hybrid stochastic optimization Excreted biofuel Photobioreactor design |
Issue Date: | 2018 |
Publisher: | © 2018 American Institute of Chemical Engineers (AIChE). Published by Wiley |
Citation: | DEL RIO-CHANONA, E.A. ... et al., 2018. Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design. AIChE Journal, Doi: 10.1002/aic.16473 |
Abstract: | Identifying 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. |
Description: | This paper is in closed access until 15th Nov 2019. |
Sponsor: | 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). |
Version: | Accepted for publication |
DOI: | 10.1002/aic.16473 |
URI: | https://dspace.lboro.ac.uk/2134/36305 |
Publisher Link: | https://doi.org/10.1002/aic.16473 |
ISSN: | 0001-1541 |
Appears in Collections: | Closed Access (Chemical Engineering)
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