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With Wee Chin WONG

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Assistant Professor

Wee Chin WONG

Assistant Professor
PhD (Chemical Engineering), Georgia Institute of Technology, United States
Cluster:
Food, Chemical and Biotechnology
Email

SIT Appointments

  • Assistant Professor
    – Present

Education

  • PhD (Chemical Engineering)
    Georgia Institute of Technology , United States
  • Master of Science (Chemical Engineering)
    Georgia Institute of Technology , United States
  • Bachelor of Engineering (Chemical Engineering)
    National University of Singapore , Singapore

Professional Certification

  • Singapore Certified Energy Manager (SCEM)
    The Institution of Engineers, Singapore
    –

Professional Memberships

  • Society of Petroleum Engineers (Officer)
    Present

Corporate Experience

  • Systems Engineer - Senior, Schlumberger
    –
  • Research Engineer, Subsea Systems, Shell International Exploration & Production, Inc.
    –
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Research Interests

  • System & Equipment - Operations, Production & Troubleshooting
    • System / equipment integrated control strategy development
    • Surveillance, trouble-shooting & diagnostics
    • Prognostics Health Management (PHM) & Predictive Maintenance 
    • Validation & verification

     

  • Process Control
    • Algorithm application & development (machine-learning enhanced): state estimation, system identification, control

     

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Journal Papers

  • An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system.
    Ewan Chee, Wee Chin Wong & Xiaonan Wang 
    Frontiers of Chemical Science and Engineering , 2021
    https://doi.org/10.1007/s11705-021-2058-6

  • Hydrogen value chain and fuel cells within hybrid renewable energy systems: Advanced operation and control strategies
    S Chen, A Kumar, WC Wong, MS Chiu, X Wang
    Applied Energy, 2019
    https://doi.org/10.1016/j.apenergy.2018.10.003

  • Recurrent neural network-based model predictive control for continuous pharmaceutical manufacturing
    WC Wong, E Chee, J Li, X Wang
    Mathematics, 2018
    https://doi.org/10.3390/math6110242

  • Postdecision-state-based approximate dynamic programming for robust predictive control of constrained stochastic processes
    WC Wong, JH Lee
    Industrial & Engineering Chemistry Research, 2011
    https://doi.org/10.1021/ie1007882

  • Approximate dynamic programming approach for process control
    JH Lee, WC Wong
    Journal of Process Control, 2010
    https://doi.org/10.1016/j.jprocont.2010.06.007

  • A reinforcement learning‐based scheme for direct adaptive optimal control of linear stochastic systems
    WC Wong, JH Lee
    Optimal Control Applications and Methods 31, 2010
    https://doi.org/10.1002/oca.915

  • Fault detection and diagnosis using hidden markov disturbance models
    WC Wong, JH Lee
    Industrial & Engineering Chemistry Research, 2010
    https://doi.org/10.1021/ie9020655

  • Hybrid cybernetic model-based simulation of continuous production of lignocellulosic ethanol: Rejecting abruptly changing feed conditions
    WC Wong, HS Song, JH Lee, D Ramkrishna
    Control engineering practice, 2010
    https://doi.org/10.1016/j.conengprac.2009.09.002

  • Realistic disturbance modeling using hidden Markov models: applications in model-based process control
    WC Wong, JH Lee
    Journal of Process Control, 2009
    https://doi.org/10.1016/j.jprocont.2009.04.014

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