Dr Xiaonan WANG
Smart Systems Engineering
Xiaonan Wang Research Group | Learning Hub
王笑楠
清华大学化学工程系,副教授,博士生导师,智慧系统工程实验室负责人
Associate Professor
Department of Chemical Engineering, Tsinghua University
Beijing,100084, P.R. China
Research
智能化、新能源、碳中和
AI for Science
Check more details of
our SSE research at
Expertise
- Energy system modeling
- Data-driven modeling and optimization
- Process design and simulation
- Techno-economic analysis
- LCA (Life Cycle Assessment)
Methodology
- Agent based simulation
- Receding horizon optimization
- Model predictive control
- Machine Learning/Deep Learning
- Multi-objective stochastic programming
Strength
- Data mining and analytics
- Advanced optimization
- Active Learning for materials discovery
- Decision support systems
- Blockchain technologies
Equipment
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)
Lab News
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
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!
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.
Tsinghua University
Associate Professor
Lead of the Smart Systems Engineering lab
September 2021- Present
National University of Singapore (NUS)
Assistant Professor, Adjunct Associate Professor
Lead of the Smart Systems Engineering lab
July 2017- Present
Imperial College London
Research Associate
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
Tsinghua University
BEng, RA
August 2007- July 2011
TAMU Safety Center
Research Associate
June 2010- Aug 2010
Publication Highlights
Check our latest papers at https://www.smartsystemsengineering.com/publications
Environmental science & technology 52, 5 (2018): 3257–3266.
Energy, water, and waste systems analyzed at a nexus level are important to move toward
more 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 moreComputers & 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.
Matter 3 (2020): 393–432
Open Access
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.
Teaching
Knowing, Acting, and Being in education
CN5111: Optimization of Chemical Processes
CN3121: Process Dynamics and Control
National University of Singapore (NUS), Department of Chemical and Biomolecular Engineering+65 6601 6221My blog
http://resilience.io/
April 11, 2020Recent studies suggest that the genetic sequences of viruses isolated from trafficked Malayan...In his In Depth News story “Can China's COVID-19 strategy work elsewhere?” (06 March, Vol. 367,...The United Nations Conference on Sustainable Development, or Rio+ 20 summit committed member...
Please feel free to reach out for any collaboration, research and education interest.