The Use of Multivariate Statistics in Process Control
Advancements in automation and distributed control systems make possible the collection of large quantities of data. But without the corresponding adequate tools, it is not possible to interpret the data. Every modern industrial site believes that this data bank is a gold mine of information if only...
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Published in | The Control Handbook pp. 439 - 458 |
---|---|
Format | Book Chapter |
Language | English |
Published |
United Kingdom
CRC Press
2011
Taylor & Francis Group |
Subjects | |
Online Access | Get full text |
ISBN | 9781420073607 1420073605 |
DOI | 10.1201/b10382-25 |
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Abstract | Advancements in automation and distributed control systems make possible the collection of large
quantities of data. But without the corresponding adequate tools, it is not possible to interpret the
data. Every modern industrial site believes that this data bank is a gold mine of information if only the
important and relevant information could be extracted painlessly and quickly. Timely interpretation of
data would improve quality and safety, reduce waste, and improve business profits. This interpretation
is possible except for the following dilemmas: undetected sensor failures, uncalibrated and misplaced
sensors, lack of integrity of the data historian and of data compression techniques used to store the data,
and transcription errors. It is no wonder that data analysis methods in the face of these serious problems
may appear to be inadequate. Meanwhile, the data bank continues to grow without appearing to garner
any useful information. Without accurate and timely measurements, feedback control of the process
to some specified objective is very difficult, if not impossible. For example, in the chemical industry,
composition measurements are usually not made online; rather they are sampled and analyzed off-line.
The delay between the samples and the results are usually on the order of hours. Thus, timely information
about the purity of the composition is unavailable to take remedial control action. |
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AbstractList | Advancements in automation and distributed control systems make possible the collection of large
quantities of data. But without the corresponding adequate tools, it is not possible to interpret the
data. Every modern industrial site believes that this data bank is a gold mine of information if only the
important and relevant information could be extracted painlessly and quickly. Timely interpretation of
data would improve quality and safety, reduce waste, and improve business profits. This interpretation
is possible except for the following dilemmas: undetected sensor failures, uncalibrated and misplaced
sensors, lack of integrity of the data historian and of data compression techniques used to store the data,
and transcription errors. It is no wonder that data analysis methods in the face of these serious problems
may appear to be inadequate. Meanwhile, the data bank continues to grow without appearing to garner
any useful information. Without accurate and timely measurements, feedback control of the process
to some specified objective is very difficult, if not impossible. For example, in the chemical industry,
composition measurements are usually not made online; rather they are sampled and analyzed off-line.
The delay between the samples and the results are usually on the order of hours. Thus, timely information
about the purity of the composition is unavailable to take remedial control action. |
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Keywords | PLS Algorithm PCR Solution Mahalanobis Distance Reflux Rate Principal Component Regression PCA Model ICA Eleventh Hour Tray Temperatures PLS Technique PLS Model Score Space tTa Ta PCA Analysis MLR Good Closed Loop Performance TPT Distillation Column Binary Distillation Column PCA Component Linearly Independent Statistically Independent Distillate Composition Mica |
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quantities of data. But without the corresponding adequate... |
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SubjectTerms | Electronics engineering ENVIRONMENTAL ENGINEERING & TECHNOLOGY SCIENCE: GENERAL ISSUES |
Title | The Use of Multivariate Statistics in Process Control |
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