Academic Profile

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.
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Asst Prof Xunyuan Yin
Assistant Professor, School of Chemical and Biomedical Engineering

Distributed process monitoring and control
Process decomposition
Learning-based process control
Data-driven/hybrid process modeling
Machine-learning in process systems engineering
 
  • Optimal process decomposition, distributed Intelligent monitoring and control of large-scale complex industrial process networks
 
  • X. Yin, S. Bo, J. Liu and B. Huang, Consensus-based distributed estimation of parameter and state for agro-hydrological systems, AIChE Journal, 67(2):e17096, 2021

  • X. Yin and J. Liu, Event-triggered state estimation of linear systems using moving horizon estimation, IEEE Transactions on Control Systems Technology, 29(2):901-909, 2021

  • X. Yin and J. Liu, Subsystem decomposition of process networks for simultaneous distributed state estimation and control, AIChE Journal, 65(3):904-914, 2019

  • X. Yin, J. Zeng and J. Liu, Forming distributed state estimation network from decentralized estimators, IEEE Transactions on Control Systems Technology, 27(6):2430-2443, 2019

  • X. Yin and J. Liu, State estimation of wastewater treatment plants based on model approximation, Computers & Chemical Engineering, 111:79-91, 2018