PROCESS CONTROL SYSTEM IDENTIFICATION

System identification is a method for generating workable dynamic response models based on an observed dataset from an actual system. It is used to give the input-output relationship of the dynamic response. The objective of this project is to design and implement System Identification for Liquid...

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Main Author: MUSTAFA, NOR FAEIZAH
Format: Final Year Project
Language:English
Published: Universiti Teknologi Petronas 2006
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Online Access:http://utpedia.utp.edu.my/7257/1/2006%20-%20PROCESS%20CONTROL%20SYSTEM%20IDENTIFICATION.pdf
http://utpedia.utp.edu.my/7257/
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spelling my-utp-utpedia.72572017-01-25T09:46:15Z http://utpedia.utp.edu.my/7257/ PROCESS CONTROL SYSTEM IDENTIFICATION MUSTAFA, NOR FAEIZAH TK Electrical engineering. Electronics Nuclear engineering System identification is a method for generating workable dynamic response models based on an observed dataset from an actual system. It is used to give the input-output relationship of the dynamic response. The objective of this project is to design and implement System Identification for Liquid System Pilot Plant. The project will also make comparisons between the conventional and intelligent modeling technique. The project concentrates on the conventional technique known as empirical modeling and intelligent modeling by means of System Identification Toolbox. In empirical model building, models are determines by making small changes in the input variable about a nominal operating condition. The model developed by using this method provides the dynamic relationship between selected input and output variables. Matlab providesthe SystemIdentification Toolboxthat helps to ensure the observedtest data represents the dynamics of the system under investigation. It provides tools for creating mathematical models ofdynamic systems based on the observed input-output data. For the intelligent technique, two model predictors, ARX and ARMAX, are used to obtain the best model. From the analysis, it shows that the ARX models exhibit quite the same characteristics as the models obtained from the empirical technique. By using the System Identification Toolbox, the ARMAX structures are the best models in representing the actual system. After model validation tests, all models from both the conventional and intelligent technique are capable of reproducing observed data with minimum predictive error. Universiti Teknologi Petronas 2006-06 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/7257/1/2006%20-%20PROCESS%20CONTROL%20SYSTEM%20IDENTIFICATION.pdf MUSTAFA, NOR FAEIZAH (2006) PROCESS CONTROL SYSTEM IDENTIFICATION. Universiti Teknologi Petronas. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
MUSTAFA, NOR FAEIZAH
PROCESS CONTROL SYSTEM IDENTIFICATION
description System identification is a method for generating workable dynamic response models based on an observed dataset from an actual system. It is used to give the input-output relationship of the dynamic response. The objective of this project is to design and implement System Identification for Liquid System Pilot Plant. The project will also make comparisons between the conventional and intelligent modeling technique. The project concentrates on the conventional technique known as empirical modeling and intelligent modeling by means of System Identification Toolbox. In empirical model building, models are determines by making small changes in the input variable about a nominal operating condition. The model developed by using this method provides the dynamic relationship between selected input and output variables. Matlab providesthe SystemIdentification Toolboxthat helps to ensure the observedtest data represents the dynamics of the system under investigation. It provides tools for creating mathematical models ofdynamic systems based on the observed input-output data. For the intelligent technique, two model predictors, ARX and ARMAX, are used to obtain the best model. From the analysis, it shows that the ARX models exhibit quite the same characteristics as the models obtained from the empirical technique. By using the System Identification Toolbox, the ARMAX structures are the best models in representing the actual system. After model validation tests, all models from both the conventional and intelligent technique are capable of reproducing observed data with minimum predictive error.
format Final Year Project
author MUSTAFA, NOR FAEIZAH
author_facet MUSTAFA, NOR FAEIZAH
author_sort MUSTAFA, NOR FAEIZAH
title PROCESS CONTROL SYSTEM IDENTIFICATION
title_short PROCESS CONTROL SYSTEM IDENTIFICATION
title_full PROCESS CONTROL SYSTEM IDENTIFICATION
title_fullStr PROCESS CONTROL SYSTEM IDENTIFICATION
title_full_unstemmed PROCESS CONTROL SYSTEM IDENTIFICATION
title_sort process control system identification
publisher Universiti Teknologi Petronas
publishDate 2006
url http://utpedia.utp.edu.my/7257/1/2006%20-%20PROCESS%20CONTROL%20SYSTEM%20IDENTIFICATION.pdf
http://utpedia.utp.edu.my/7257/
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score 13.160551