Design of Experiments for Large Scale Catalytic Systems

• Author / Creator

  Kumar, Siddhartha

• Parameter estimation for mathematical models is performed based on the data collected by experiments using system identification techniques. However, since performing experiments can be time consuming as well as expensive, experiments must be designed prior to performing, so that the data collected will be optimal for parameter estimation. This thesis aims at performing experimental design while addressing three different design problems: (1) non-identifiability for large scale catalytic systems, (2) uncertainty in parametric values being used for design, and (3) parameter estimation for a specific subset of reactions. Hierarchical clustering, stochastic optimization and computational singular perturbation are the methodologies used in this study. Catalytic systems under investigation are ammonia decomposition and preferential oxidation for hydrogen production for fuel cells.

• Subjects / Keywords

  • Design of experiments
  • hierarchical clustering
  • nonlinear stochastic optimization
  • computational singular perturbation
  • ammonia decomposition
  • preferential oxidation

• Graduation date

  Fall 2011

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3J35R

• 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.