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Development of Soft Sensor Model Using Moving Window Approach

Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advanta...

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Main Author: Rajan, Lavaniya
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2012
Subjects:
TP Chemical technology
Online Access:http://utpedia.utp.edu.my/9664/1/2012%20-%20Development%20of%20Soft%20Sensor%20Model%20using%20Moving%20Window%20Approach.pdf
http://utpedia.utp.edu.my/9664/
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spelling my-utp-utpedia.96642017-01-25T09:40:59Z http://utpedia.utp.edu.my/9664/ Development of Soft Sensor Model Using Moving Window Approach Rajan, Lavaniya TP Chemical technology Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advantages and performances of Moving Window approach over the traditional soft sensor models. Time invariant and stationary process conditions are those assumptions made in developing soft sensors, and these assumptions causes degradations and limitations to the soft sensors in estimating process variables. Degradations of soft sensors are caused by process shift, catalyst performance lost and et cetera. Besides that, the restrictions of sensors in estimating difficult-to-measure variables and the delays during the laboratory tests have becomeone of the factors in developing soft sensor. This paper presents a study regarding the multivariate statistical process control techniques that can be used in developing soft sensors such as Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis. The scope of study for the project includes understanding the concept andwhat are the adaptive schemes available to construct the soft sensors. Besides that further research on Moving Window approach together with MSPC techniques will be carried out which can be adapted into the adaptive models to develop the soft sensors. Systematic approach will be presented through this project in using Moving Window approach to construct the soft sensors and this includes an analysis of an appropriate case study where the approach can be implemented. Keywords: Multivariate Statistical Process Control techniques, Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis Universiti Teknologi PETRONAS 2012-05 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/9664/1/2012%20-%20Development%20of%20Soft%20Sensor%20Model%20using%20Moving%20Window%20Approach.pdf Rajan, Lavaniya (2012) Development of Soft Sensor Model Using Moving Window Approach. 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 TP Chemical technology
spellingShingle TP Chemical technology
Rajan, Lavaniya
Development of Soft Sensor Model Using Moving Window Approach
description Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advantages and performances of Moving Window approach over the traditional soft sensor models. Time invariant and stationary process conditions are those assumptions made in developing soft sensors, and these assumptions causes degradations and limitations to the soft sensors in estimating process variables. Degradations of soft sensors are caused by process shift, catalyst performance lost and et cetera. Besides that, the restrictions of sensors in estimating difficult-to-measure variables and the delays during the laboratory tests have becomeone of the factors in developing soft sensor. This paper presents a study regarding the multivariate statistical process control techniques that can be used in developing soft sensors such as Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis. The scope of study for the project includes understanding the concept andwhat are the adaptive schemes available to construct the soft sensors. Besides that further research on Moving Window approach together with MSPC techniques will be carried out which can be adapted into the adaptive models to develop the soft sensors. Systematic approach will be presented through this project in using Moving Window approach to construct the soft sensors and this includes an analysis of an appropriate case study where the approach can be implemented. Keywords: Multivariate Statistical Process Control techniques, Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis
format Final Year Project
author Rajan, Lavaniya
author_facet Rajan, Lavaniya
author_sort Rajan, Lavaniya
title Development of Soft Sensor Model Using Moving Window Approach
title_short Development of Soft Sensor Model Using Moving Window Approach
title_full Development of Soft Sensor Model Using Moving Window Approach
title_fullStr Development of Soft Sensor Model Using Moving Window Approach
title_full_unstemmed Development of Soft Sensor Model Using Moving Window Approach
title_sort development of soft sensor model using moving window approach
publisher Universiti Teknologi PETRONAS
publishDate 2012
url http://utpedia.utp.edu.my/9664/1/2012%20-%20Development%20of%20Soft%20Sensor%20Model%20using%20Moving%20Window%20Approach.pdf
http://utpedia.utp.edu.my/9664/
_version_ 1739831701787377664
score 13.211869

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