Dr. Xiaonan Wang
Department of Chemical Engineering, Tsinghua University, Beijing, China.selected
Dr. Xiaonan Wang is currently an associate professor in the Department of Chemical Engineering at Tsinghua University. She received her BEng from Tsinghua University in 2011 and PhD from University of California, Davis in 2015. After working as a postdoctoral research associate at Imperial College London, she joined the National University of Singapore (NUS) as an assistant professor since 2017. Her research focuses on the development of intelligent computational methods including multi-scale modelling, optimization, data analytics and machine learning for applications in advanced materials, energy, environmental and manufacturing systems to support smart and sustainable development. She is leading a Smart Systems Engineering research group at NUS and Tsinghua of more than 20 team members as PI and also the deputy director of the Accelerated Materials Development programme in Singapore (S$25M funding). She has published more than 100 peer-reviewed papers, organized and chaired several international conferences, and delivered more than 50 presentations and invited talks at conferences and universities on five continents. She is an editorial board member of 10 SCI journals e.g. Applied Energy, ACS ES&T Engineering. She was recognized as an AIChE-SLS Outstanding Young Principal Investigator, Young Researcher Award for Engineering Sustainable Development, IChemE Global Awards Young Researcher finalist and for Royal Society International Exchanges Award,as well several best paper awards at IEEE and Applied Energy conferences and journals. She is also a program leader lead of the Association of Pacific Rim Universities (APRU)’s Sustainable Waste Management Program and advisory board member of several international organizations.
Title:Smart energy transitions towards a carbon-neutral future
Abstract:Facing the pressing environmental and climate change challenges, novel approaches are needed for sustainable energy transitions towards a carbon-neutral future. The emergence of big data analytics, internet of things, machine learning (ML), and general artificial intelligence (AI) provide enormous smart tools for processing complex data and information generated from experimental and computational research, as well as industrial applications, which could revolutionize next-generation research, industry and society. The potential contribution of ML combined with big data and cyber-physical systems to energy and environmental is worth of investigation. In this talk, an overview of multi-scale smart systems engineering approaches and their applications in crucial domains of energy and environment management will be first given. The recent developments of ML models and data-driven optimization that can expedite smart systems engineering development will be demonstrated via a series of use cases. The design, operation and management of multi-scale systems with enhanced economic and environmental performance are then presented. Finally, opportunities, challenges, and future directions of smart energy and environment management faced by the pressing carbon-neutrality or net-zero targets are discussed.