Dr Xiaonan WANG
Smart Systems Engineering
Xiaonan Wang Research Group | Learning Hub
Department of Chemical Engineering, Tsinghua University
Beijing,100084, P.R. China
AI for Science
Check more details of
our SSE research at
Intelligent unmanned laboratory
High performance computers, cloud computing, sensors (Internet-of-things)
(in collaboration with CREATE C4T)
Waste-to-Energy facilities (in collaboration with CREATE E2S2)
Research, Happiness, Exploration
More SSE lab news at https://www.smartsystemsengineering.com/lab-news
DeepKey anti-counterfeiting tech by NUS researchers can perform checks under extreme conditions
December 21, 2020
Read the news on NUS website, Zaobao and CNA.
Original scientific paper published on Matter.
Applied Energy Highly Cited Research Paper Award
August 20, 2020
Our paper Energy Demand Side Management within micro-grid networks enhanced by blockchain has been awarded the Applied Energy Highly Cited Paper Awards 2020. It has been a pioneering work in blockchain for energy systems and received more than 130 citations since publication in 2018. Congratulations team!
Congrats on our graduate of 2019
July 12-19, 2019
During NUS Commencement 2019, our group members have received their degrees: Mr Ho Chi-Hung with M.Eng. Mr Feng Bowen and Mr Tian Daniel with B.Eng. Bowen has won Valedictorian and invited to the commencement Dinner. Congratulations!
Visit by East China University of Science and Technology (ECUST) Professors
July 3, 2019
Prof. Feng Qian, Vice president of ECUST, Prof. Wenli Du, Dean of Information Science and Engineering, Prof. Weimin Zhong, Prof. Yang Tang, Dr. Wei Du, and Dr. Bing Wang visited NUS and our group for further collaborations in research and education.
Smart Factory Tour
June 7, 2018
Part of our team visited the Model Factory@SIMTech to learn how the Industry 4.0 and AI technologies are promoting industry development. We will have continuous collaboration with A*STAR's Singapore Institute of Manufacturing Technology (SIMTech) through research projects and students training.
CV and collaborators
Born in 1990 as the new generation of scientists devoted in this enchanting research field worldwide, Dr Xiaonan Wang (in Chinese: 王笑楠), as the PI of this young and vibrant SSE lab, has been aiming to lead the sustainability and smart systems research to real societal benefits with the best team.
Positions and full fellowship available for PhD students, postdocs, and visiting scholars.
Lead of the Smart Systems Engineering lab
September 2021- Present
National University of Singapore (NUS)
Assistant Professor, Adjunct Associate Professor
July 2017- Present
Imperial College London
Lecturer and Master supervisor for Energy Futures Lab
August 2015 - Auguest 2017
University of California, Davis
MSc, PhD, Lecturer
University of California full fellowship for direct PhD
Finalist for the Best Thesis Award in Engineering
September 2011- July 2015
August 2007- July 2011
TAMU Safety Center
June 2010- Aug 2010
Check our latest papers at https://www.smartsystemsengineering.com/publications
A Nexus Approach for Sustainable Urban Energy-Water-Waste Systems Planning and Operation
Environmental science & technology 52, 5 (2018): 3257–3266.
Energy, water, and waste systems analyzed at a nexus level are important to move towardmore sustainable cities. Waste-to-energy pathways, along with the water and energy sectors are studied, aiming to develop waste treatment capacity and energy recovery with the lowest economic and environmental cost. Read more
A multi-objective optimization approach for selection of energy storage systems
Computers & Chemical Engineering 115 (2018): 213-225.
Get fascinated by the rapid development of energy storage technologies but have no clue what their true impacts are? Check out our recently published work on a decision-making framework for energy storage systems selection on our flagship PSE journal Computers & Chemical Engineering. Thanks for the great collaboration with Tsinghua Energy and Power Engineering and AquaBattery.
AI Applications through the Whole Life Cycle of Material Discovery
Matter 3 (2020): 393–432
How is AI accelerating all stages of Material Discovery? Our review published on #Matter provides a holistic look of what #AI and #machinelearning brings to the table for discovery & design by considering ultimate application & end-use of the material.Almost two years' team efforts to put this holistic review together! Congrats to my PhD students and more exciting outcome to expect!
Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
Applied Energy 269 (2020): 115166.
Conversion of wet organic wastes into renewable energy is a promising way to substitute fossil fuels and avoid environmental deterioration. Machine learning models for multi-task prediction of fuel properties of the chars were developed and optimized based on two datasets for hydrochar and pyrochar. Feature importance and correlation were explored based on optimized ML model, and feature re-examination was conducted for model improvement.
Knowing, Acting, and Being in education
CN5111: Optimization of Chemical Processes
CN3121: Process Dynamics and Control
Please feel free to reach out for any collaboration, research and education interest.
Scopus Author ID: 56166894600ResearcherID: T-1102-2017