Inferential estimation and control of chemical processes using partial least squares based model

The use of inferential estimation model as a strategy to overcome the lack of efficient on-line measurement for product qualities is proposed. This strategy makes use of easy to measure secondary variables, such as temperature and pressure to infer the value of non-measurable primary variables such...

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Main Author:	Lim, Wan Piang
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/4216/1/LimWanPiangMFKK2005.pdf
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spelling	my-utm-ep.42162018-01-16T04:32:48Z Inferential estimation and control of chemical processes using partial least squares based model 2005-06 Lim, Wan Piang TP Chemical technology The use of inferential estimation model as a strategy to overcome the lack of efficient on-line measurement for product qualities is proposed. This strategy makes use of easy to measure secondary variables, such as temperature and pressure to infer the value of non-measurable primary variables such as chemical composition. As a case study, a fatty acid fractionation column from a local company was considered. The plant that was simulated using HYSYSTM simulator provided all the required process data throughout the study. To provide the necessary process insights, analyses of dynamic behaviour were carried out. Appropriate secondary measurements with significant relationships with the product composition were then identified for the construction of the inferential estimator within MATLABÂ® environment. A number of models were considered but nested neural network partial least squares (NNPLS) model was found most proficient. The model was tested online and reasonable performances were obtained. Further refinements were proposed to improve the accuracy and robustness of the estimator. In particular, the issue of data scaling was elaborately addressed. Following the success implementation of the estimator, inferential control of the product quality was examined. In both regulatory and servo controls, better performances were obtained compared to the indirect strategy of controlling product composition using selected tray temperature. This was further improved by employing cascade control. The results obtained throughout this work have illustrated the potential of inferential control strategy and the capability of the hybrid neural network-PLS model as the process estimator. This should therefore serve as an alternative solution to the lack of measurement in chemical process industry. The model developed from the simulation stage is specified to a particular case and it should be verified against the actual process before practical implementation. 2005-06 Thesis http://eprints.utm.my/id/eprint/4216/ http://eprints.utm.my/id/eprint/4216/1/LimWanPiangMFKK2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering Faculty of Chemical and Natural Resources Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Lim, Wan Piang Inferential estimation and control of chemical processes using partial least squares based model
description	The use of inferential estimation model as a strategy to overcome the lack of efficient on-line measurement for product qualities is proposed. This strategy makes use of easy to measure secondary variables, such as temperature and pressure to infer the value of non-measurable primary variables such as chemical composition. As a case study, a fatty acid fractionation column from a local company was considered. The plant that was simulated using HYSYSTM simulator provided all the required process data throughout the study. To provide the necessary process insights, analyses of dynamic behaviour were carried out. Appropriate secondary measurements with significant relationships with the product composition were then identified for the construction of the inferential estimator within MATLABÂ® environment. A number of models were considered but nested neural network partial least squares (NNPLS) model was found most proficient. The model was tested online and reasonable performances were obtained. Further refinements were proposed to improve the accuracy and robustness of the estimator. In particular, the issue of data scaling was elaborately addressed. Following the success implementation of the estimator, inferential control of the product quality was examined. In both regulatory and servo controls, better performances were obtained compared to the indirect strategy of controlling product composition using selected tray temperature. This was further improved by employing cascade control. The results obtained throughout this work have illustrated the potential of inferential control strategy and the capability of the hybrid neural network-PLS model as the process estimator. This should therefore serve as an alternative solution to the lack of measurement in chemical process industry. The model developed from the simulation stage is specified to a particular case and it should be verified against the actual process before practical implementation.
format	Thesis
qualification_level	Master's degree
author	Lim, Wan Piang
author_facet	Lim, Wan Piang
author_sort	Lim, Wan Piang
title	Inferential estimation and control of chemical processes using partial least squares based model
title_short	Inferential estimation and control of chemical processes using partial least squares based model
title_full	Inferential estimation and control of chemical processes using partial least squares based model
title_fullStr	Inferential estimation and control of chemical processes using partial least squares based model
title_full_unstemmed	Inferential estimation and control of chemical processes using partial least squares based model
title_sort	inferential estimation and control of chemical processes using partial least squares based model
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering
granting_department	Faculty of Chemical and Natural Resources Engineering
publishDate	2005
url	http://eprints.utm.my/id/eprint/4216/1/LimWanPiangMFKK2005.pdf
_version_	1747814504460189696

Inferential estimation and control of chemical processes using partial least squares based model

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