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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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