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Processability Analysis using Principal Component Analysis and Support Vector Machine

  • Author / Creator
    Zhang,Yixin
  • The obtained model developed outperforms the existing linear and logistic prediction methods in terms of content prediction error. As the proof of concept, the methodology is applied to an oil sands processing dataset created using an artificial model with such variables as bitumen content and fines content of ores, along with the processing variables such as pH and temperature.

  • Subjects / Keywords
  • Graduation date
    2014-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R33R0Q25C
  • 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 Electrical and Computer Engineering
  • Specialization
    • Control Systems
  • Supervisor / co-supervisor and their department(s)
    • Zhao,Qing (ECE) Xu,Zhenghe (CME)
  • Examining committee members and their departments
    • Prasad,Vinay(CME)
    • Xu,Zhenghe (CME)
    • Zhao,Qing (ECE)
    • Reformat,Marek(ECE)