Imperial College London

DrXiaonanWang

Faculty of EngineeringDepartment of Chemical Engineering

Research Associate
 
 
 
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Contact

 

+44 (0)7874 349 693xiaonan.wang Website CV

 
 
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Location

 

C603Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Summary

Xiaonan is currently a research associate at the Centre for Process Systems Engineering (CPSE), Imperial College London, working with Professor Nilay Shah's group. Her research focuses on the system modeling and optimization applied to sustainable city development, agent based modeling, resource technology network, and hybrid renewable energy systems. 

She did her PhD research in Chemical Engineering and Control Science at University of California, Davis, co-supervised by Professor Ahmet Palazoglu and Professor Nael H. El-Farra. She led several projects dealing with how the changing energy generation and distribution profiles can augment residential and industrial consumer behaviors and achieve the best economic and environmental performance. 

She also holds an M.Sc. in Chemical Engineering from UC Davis and a BEng in Chemical and Biological Engineering from Tsinghua University, China.

 


 

Publications

Journals

Wang X, El-Farra NH, Palazoglu A, 2017, Optimal scheduling of demand responsive industrial production with hybrid renewable energy systems, Renewable Energy, Vol:100, ISSN:0960-1481, Pages:53-64

Wang X, El-Farra NH, Palazoglu A, 2015, Proactive Reconfiguration of Heat-Exchanger Supernetworks, Industrial & Engineering Chemistry Research, Vol:54, ISSN:0888-5885, Pages:9178-9190

Wang X, Palazoglu A, El-Farra NH, 2015, Operational optimization and demand response of hybrid renewable energy systems, Applied Energy, Vol:143, ISSN:0306-2619, Pages:324-335

Conference

Wang X, Palazoglu A, El-Farra NH, 2015, Proactive Optimization and Control of Heat-Exchanger Super Networks, 9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015, ELSEVIER SCIENCE BV, Pages:592-597, ISSN:2405-8963

Wang X, Palazoglu A, El-Farra NH, 2014, Operation of Residential Hybrid Renewable Energy Systems: Integrating Forecasting, Optimization and Demand Response, American Control Conference, IEEE, Pages:5043-5048, ISSN:0743-1619

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