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Process-wide performance monitoring Jiang, Li
Abstract
Today's process industries have shown a great concern on how to improve their product quality. The product quality, however, can be improved only when the process performance has been improved. Process performance monitoring, using only the routine operating data without interfering with the normal process operation, makes it possible to improve process performance and hence is useful to process industries. In this thesis, a hierarchical performance monitoring system is proposed and tested. The hierarchical monitoring system consists of two levels. The higher-level takes advantage of the advanced statistical regression analysis methods principal component analysis (PCA) and partial least squares (PLS) to assess large amounts of correlated process data. This level is able to provide overall process monitoring and a reliable detection for process abnormality. The lower-level is loop-oriented, and is designed to give detailed performance monitoring and fault diagnosis. It detects loop oscillation and locates the source of the oscillation; it detects high-friction in a valve and evaluates the controller itself. Spectral analysis and time series analysis methods and an adaptive nonlinear modeller (ANM) are used for the purpose of diagnosis at the lower-level. Considering the practical needs, a model-free controller tuning algorithm, iterative feedback tuning (IFT), is also built into the lower-level. Integrated in a complementary manner, the two levels can monitor the process performance with enhanced strength.
Item Metadata
Title |
Process-wide performance monitoring
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1999
|
Description |
Today's process industries have shown a great concern on how to improve their product
quality. The product quality, however, can be improved only when the process performance
has been improved. Process performance monitoring, using only the routine
operating data without interfering with the normal process operation, makes it possible
to improve process performance and hence is useful to process industries.
In this thesis, a hierarchical performance monitoring system is proposed and tested.
The hierarchical monitoring system consists of two levels. The higher-level takes advantage
of the advanced statistical regression analysis methods principal component analysis
(PCA) and partial least squares (PLS) to assess large amounts of correlated process
data. This level is able to provide overall process monitoring and a reliable detection for
process abnormality. The lower-level is loop-oriented, and is designed to give detailed
performance monitoring and fault diagnosis. It detects loop oscillation and locates the
source of the oscillation; it detects high-friction in a valve and evaluates the controller
itself. Spectral analysis and time series analysis methods and an adaptive nonlinear
modeller (ANM) are used for the purpose of diagnosis at the lower-level. Considering
the practical needs, a model-free controller tuning algorithm, iterative feedback tuning
(IFT), is also built into the lower-level. Integrated in a complementary manner, the two
levels can monitor the process performance with enhanced strength.
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Extent |
3559060 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-06-15
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0065153
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1999-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.