Imperial College London

DrAntonioDel Rio Chanona

Faculty of EngineeringDepartment of Chemical Engineering

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

 

Summary

I am head of the Optimisation and Machine Learning for Process Systems Engineering group at the Department of Chemical Engineering, and the Centre for Process Systems Engineering, Imperial College London.

My research focuses on developing and applying computer algorithms from the area of optimizationmachine learning and reinforcement learning to engineering systems. The applied branch of my research looks at bioprocess control, optimization and scale-up.

I hold a PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where I received the Danckwerts-Pergamon award for the best PhD dissertation of 2017, and received my undergraduate degree from the National Autonomous University of Mexico (UNAM).

Publications

Journals

Del Rio-Chanona EA, Ahmed NR, Wagner J, et al., 2019, Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses, Biotechnology and Bioengineering, Vol:116, ISSN:0006-3592, Pages:2971-2982

Zhang D, Del Rio-Chanona EA, Petsagkourakis P, et al., 2019, Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization, Biotechnology and Bioengineering, Vol:116, ISSN:0006-3592, Pages:2919-2930

del Rio-Chanona EA, Wagner JL, Ali H, et al., 2019, Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design, Aiche Journal, Vol:65, ISSN:0001-1541, Pages:915-923

Del Rio-Chanona EA, Cong X, Bradford E, et al., 2019, Review of advanced physical and data-driven models for dynamic bioprocess simulation: Case study of algae-bacteria consortium wastewater treatment, Biotechnology and Bioengineering, Vol:116, ISSN:0006-3592, Pages:342-353

Conference

Chanona EADR, Alves Graciano JE, Bradford E, et al., 2019, Modifier-Adaptation Schemes Employing Gaussian Processes and Trust Regions for Real-Time Optimization, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), ELSEVIER, Pages:52-57, ISSN:2405-8963

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