Imperial College London
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DrAntonioDel Rio Chanona

Faculty of Engineering, Department of Chemical Engineering

Senior Lecturer
 
 
 
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Contact

 

a.del-rio-chanona Website

 
 
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Location

 

ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{del:2019:10.1002/aic.16473,
author = {del, Rio Chanona EA and Wagner, JL and Ali, H and Fiorelli, F and Zhang, D and Hellgardt, K},
doi = {10.1002/aic.16473},
journal = {AIChE Journal},
pages = {915--923},
title = {Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design},
url = {http://dx.doi.org/10.1002/aic.16473},
volume = {65},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgaederived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating largescale systems, causing timeintensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative datadriven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting stateoftheart 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, multiobjective optimization was incorporated to generate a Paretofrontier for decisionmaking, advancing its applications in complex biosystems modeling and optimization.
AU  - del,Rio Chanona EA
AU  - Wagner,JL
AU  - Ali,H
AU  - Fiorelli,F
AU  - Zhang,D
AU  - Hellgardt,K
DO  - 10.1002/aic.16473
EP  - 923
PY  - 2019///
SN  - 0001-1541
SP  - 915
TI  - Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design
T2  - AIChE Journal
UR  - http://dx.doi.org/10.1002/aic.16473
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000457747600006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.16473
UR  - http://hdl.handle.net/10044/1/84037
VL  - 65
ER  -
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