Imperial College London

DrDongdaZhang

Faculty of EngineeringDepartment of Chemical Engineering

Honorary Research Fellow
 
 
 
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Contact

 

dongda.zhang11

 
 
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Location

 

ACE ExtensionSouth Kensington Campus

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Summary

 

Summary

Dr. Dongda Zhang is a University Lecturer at the School of Chemical Engineering and Analytical Science at the University of Manchester and a Honorary Research Fellow at the Centre for Process Systems Engineering at Imperial College London. He is currently the IChemE Representative of the University of Manchester.

He holds a BSc degree (2011) from Tianjin University and a MSc (Distinction) degree (2013) from Imperial College London. He started his PhD research at the University of Cambridge in 2013, completed his research within 2 years alongside over 10 original publications in leading chemical engineering and biotechnology journals, and graduated at the beginning of the third year after the university approval for thesis early submission. Upon the completion of his PhD in 2016, he was invited by the Chinese Academy of Sciences and several universities for short research visits, and then moved to the Centre for Process Systems Engineering at Imperial College London as a postdoctoral research associate. In 2017, he was awarded the prestigious Leverhulme Early Career Fellowship at the University of Cambridge, followed by his appointment at the University of Manchester as a University Lecturer in the same year.

Dr. Dongda Zhang's Manchester University staff page is available: https://www.research.manchester.ac.uk/portal/dongda.zhang.html


Publications

Journals

Ali H, Solsvik J, Wagner JL, et al., 2019, CFD and kinetic-based modeling to optimize the sparger design of a large-scale photobioreactor for scaling up of biofuel production, Biotechnology and Bioengineering, Vol:116, ISSN:0006-3592, Pages:2200-2211

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, ISSN:0006-3592

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, ISSN:0006-3592

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

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