Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column

Adaptation of network weights using Genetic Algorithm (GA) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) inferential estimator. This is particularly useful for cases involving changing operating condition as well as highly nonlinear processes. As a case st...

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Main Author:	Chen, Wah Sit
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/4455/1/ChenWahSitMFChR2005.pdf
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id	my-utm-ep.4455
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spelling	my-utm-ep.44552018-02-25T08:24:17Z Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column 2005-07 Chen, Wah Sit TP Chemical technology Adaptation of network weights using Genetic Algorithm (GA) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) inferential estimator. This is particularly useful for cases involving changing operating condition as well as highly nonlinear processes. As a case study, a fatty acid distillation process was considered. The ANN model trained using GA, employed as inferential estimator was successful in providing on- line estimates to a reasonable accuracy. Comparisons were also made to the feedforward network model trained using Levenberg-Marquardt (LM) training algorithm as well as Elman network. When implemented on-line, GA-based ANN model was proved to be more efficient. The use of on- line retraining further improved the estimator performances. To avoid drastic changes of network weights, a partial network on- line retraining strategy was introduced. In this case, the estimator model did not undergo on-line retraining, but a newly introduced bias model, attached to the main estimator was used for the fine-tuning purposes. Significant improvements were obtained especially when assessing from the perspective of model generalization. The results obtained in this work confirmed the potential of using model update strategy for neural network process estimator. 2005-07 Thesis http://eprints.utm.my/id/eprint/4455/ http://eprints.utm.my/id/eprint/4455/1/ChenWahSitMFChR2005.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 Chen, Wah Sit Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
description	Adaptation of network weights using Genetic Algorithm (GA) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) inferential estimator. This is particularly useful for cases involving changing operating condition as well as highly nonlinear processes. As a case study, a fatty acid distillation process was considered. The ANN model trained using GA, employed as inferential estimator was successful in providing on- line estimates to a reasonable accuracy. Comparisons were also made to the feedforward network model trained using Levenberg-Marquardt (LM) training algorithm as well as Elman network. When implemented on-line, GA-based ANN model was proved to be more efficient. The use of on- line retraining further improved the estimator performances. To avoid drastic changes of network weights, a partial network on- line retraining strategy was introduced. In this case, the estimator model did not undergo on-line retraining, but a newly introduced bias model, attached to the main estimator was used for the fine-tuning purposes. Significant improvements were obtained especially when assessing from the perspective of model generalization. The results obtained in this work confirmed the potential of using model update strategy for neural network process estimator.
format	Thesis
qualification_level	Master's degree
author	Chen, Wah Sit
author_facet	Chen, Wah Sit
author_sort	Chen, Wah Sit
title	Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
title_short	Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
title_full	Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
title_fullStr	Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
title_full_unstemmed	Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
title_sort	application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column
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/4455/1/ChenWahSitMFChR2005.pdf
_version_	1747814532149936128

Application of artificial neural network genetic algorithm in inferential estimation and control of a distillation column

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