Academic Profile : Faculty
Asst Prof Xunyuan Yin
Assistant Professor, School of Chemistry, Chemical Engineering and Biotechnology
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Xunyuan Yin is an Assistant Professor in the School of Chemical and Biomedical Engineering at Nanyang Technological University (NTU), Singapore. He received the Ph.D. degree in process control from the University of Alberta, Edmonton, AB, Canada, in August 2018. He received the B.Sc. and M.Sc. degrees from the Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China, in 2010 and 2012, respectively. He was a visiting student in the Department of Aerospace Engineering, The University of Michigan, Ann Arbor, MI, USA, in 2014.
Between August 2018 and November 2021, he worked as a Postdoctoral Fellow at the University of Alberta. His research interests include state estimation, optimal process control, fault diagnosis, learning-based process monitoring and operation, and their applications to manufacturing processes.
Between August 2018 and November 2021, he worked as a Postdoctoral Fellow at the University of Alberta. His research interests include state estimation, optimal process control, fault diagnosis, learning-based process monitoring and operation, and their applications to manufacturing processes.
Distributed process monitoring and control
Process decomposition
Learning-based process control
Data-driven/hybrid process modeling
Machine-learning in process systems engineering
Process decomposition
Learning-based process control
Data-driven/hybrid process modeling
Machine-learning in process systems engineering
- Learning-based state estimation of large-scale nonlinear complex industrial processes – enabling intelligent process monitoring and process control for digital and smart manufacturing
- Parallel subsystem modeling and autonomous distributed collaborative process sensing, real-time optimization and control
- Robust distributed moving horizon estimation and adaptive economic predictive control of membrane bioreactors for energy-efficient wastewater treatment
- Physics-informed machine learning-based process modeling and resilient operation of continuous protein crystallization
- Optimal process decomposition, distributed Intelligent monitoring and control of large-scale complex industrial process networks