ERA

Download the full-sized PDF of Data-driven methods for near infrared spectroscopy modelingDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3C53F89F

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Data-driven methods for near infrared spectroscopy modeling Open Access

Descriptions

Other title
Subject/Keyword
modeling
near infrared spectra
wavelength selection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chen, Mulang
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Rajendran, Arvind (Chemical and Materials Engineering)
Tavakoli, Mahdi (Electrical and Computer Engineering)
Huang, Biao (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2013-09-28T13:30:07Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
Time consuming offline laboratory analysis and high cost hardware measurement techniques render difficulties in obtaining the important quality variables in real time application. Near-infrared (NIR) spectroscopy is widely used as a process analytical tool (PAT) in chemical processes, providing online estimation of the target properties which are often obtained by lab analysis. This thesis focuses on the model building, model structure (wavelength) selection and online model update for NIR applications. Time varying issue is solved by applying recursive adaptation methods and a novel recursive wavelength selection algorithm is proposed to adapt the model structure during online phase. The Just-in-time (JIT) modeling approach is adopted to model the nonlinear relationships between spectra and properties. A similarity criterion that utilizes input-output information is developed to search for most relevant samples from the database. Finally, the recursive algorithm and locally weighted algorithm are synthesized into the JIT framework in order to deal with both time varying and non-linearity issues of the process.
Language
English
DOI
doi:10.7939/R3C53F89F
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Chen, Mulang. (2013). Ind. Eng. Chem. Res.

File Details

Date Uploaded
Date Modified
2014-04-29T19:44:53.741+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 7227760
Last modified: 2015:10:12 15:34:38-06:00
Filename: Chen_Mulang_Fall 2013.pdf
Original checksum: 50fed72112185e3ec29d11e75a00a536
Well formed: true
Valid: true
Page count: 122
Activity of users you follow
User Activity Date