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

  • Process Analysis

• Graduation date

  Spring 2014

• 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.