Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity

The control valve in process control undergoes wear and aging which causes valve stiction. The valve stiction problem effects the process performance in loops and eventually the quality of the product. Stiction study consists of four main parts, stiction modelling, stiction detection, stiction quant...

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Main Author: A/L Karuppiah, Yuveneshraj
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.17790 /
Published: IRC 2016
Subjects:
TP Chemical technology
Online Access: http://utpedia.utp.edu.my/17790/1/Final%20Hard%20Bound%20YUVENESHRAJ%2015869%20-28-4-2016.pdf
http://utpedia.utp.edu.my/17790/
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spelling utp-utpedia.177902017-11-23T09:44:31Z http://utpedia.utp.edu.my/17790/ Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity A/L Karuppiah, Yuveneshraj TP Chemical technology The control valve in process control undergoes wear and aging which causes valve stiction. The valve stiction problem effects the process performance in loops and eventually the quality of the product. Stiction study consists of four main parts, stiction modelling, stiction detection, stiction quantification and stiction compensation. The quantification technique for the valve stiction is very important aspect in process control. We consider that the presence of stiction in valve can be calculated using few techniques. The objective of this paper is to develop support vector regression (SVR) for the valve stiction nonlinearity. This technique is less explored and very rarely applied in stiction quantification. A proper algorithm has been introduced for the SVR based stiction quantification. The algorithm had been demonstrated through simulation based on multiple cases studies. The results of the experiment has been interpreted and analyzed. It also has been compared with other methods based on the efficiency and accuracy IRC 2016-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/17790/1/Final%20Hard%20Bound%20YUVENESHRAJ%2015869%20-28-4-2016.pdf A/L Karuppiah, Yuveneshraj (2016) Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity. IRC, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
A/L Karuppiah, Yuveneshraj
Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
description The control valve in process control undergoes wear and aging which causes valve stiction. The valve stiction problem effects the process performance in loops and eventually the quality of the product. Stiction study consists of four main parts, stiction modelling, stiction detection, stiction quantification and stiction compensation. The quantification technique for the valve stiction is very important aspect in process control. We consider that the presence of stiction in valve can be calculated using few techniques. The objective of this paper is to develop support vector regression (SVR) for the valve stiction nonlinearity. This technique is less explored and very rarely applied in stiction quantification. A proper algorithm has been introduced for the SVR based stiction quantification. The algorithm had been demonstrated through simulation based on multiple cases studies. The results of the experiment has been interpreted and analyzed. It also has been compared with other methods based on the efficiency and accuracy
format Final Year Project
author A/L Karuppiah, Yuveneshraj
author_sort A/L Karuppiah, Yuveneshraj
title Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
title_short Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
title_full Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
title_fullStr Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
title_full_unstemmed Diagnosis of Poor Control Loop Performance: Investigate Support Vector Regression Method on Stiction Quantification for Control Valve Nonlinearity
title_sort diagnosis of poor control loop performance: investigate support vector regression method on stiction quantification for control valve nonlinearity
publisher IRC
publishDate 2016
url http://utpedia.utp.edu.my/17790/1/Final%20Hard%20Bound%20YUVENESHRAJ%2015869%20-28-4-2016.pdf
http://utpedia.utp.edu.my/17790/
_version_ 1741195248399286272
score 11.62408

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