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Statistical Approaches to Robust Identification of Multi-modal Processes

  • Author / Creator
    Lu, Yaojie
  • In this thesis, time-varying behaviour, nonlinearity and switching dynamics are generally treated as multi-modal behaviour. Two multi-model modelling techniques, i.e., the linear parameter varying (LPV) technique and the switched modelling technique, are investigated to model the multi-modal processes. The robustness of pro-posed algorithms is enhanced by modelling the noise as t distributions. Two statistical approaches, i.e., EM algorithm and variational Bayesian algorithm, are used for process identification. The proposed algorithms are verified by simulations and experiments. Finally, soft sensors based on proposed algorithms are designed to effectively estimate the steam-quality for the once-through steam generators used in the oil sands industry.

  • Subjects / Keywords
  • Graduation date
    2014-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3R95P
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Process Control
  • Supervisor / co-supervisor and their department(s)
    • Huang, Biao (Chemical and Materials Engineering)
  • Examining committee members and their departments
    • Huang, Biao (Chemical and Materials Engineering)
    • Sharp, David (Chemical and Materials Engineering)
    • Prasad, Vinay (Chemical and Materials Engineering)
    • Li, Zukui (Chemical and Materials Engineering)