Main steam temperature modeling based on levenberg-marquardt learning algorithm

Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often describe as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt l...

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Main Authors: Mazalan, N. A., Malek, A. A., Wahid, M. A., Mailah, M., Saat, A., Sies, M. M.
Format: Conference or Workshop Item
Published: 2013
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Online Access:http://eprints.utm.my/id/eprint/51154/
http://dx.doi.org/10.4028/www.scientific.net/AMM.388.307
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.511542017-09-17T08:00:33Z http://eprints.utm.my/id/eprint/51154/ Main steam temperature modeling based on levenberg-marquardt learning algorithm Mazalan, N. A. Malek, A. A. Wahid, M. A. Mailah, M. Saat, A. Sies, M. M. TJ Mechanical engineering and machinery Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often describe as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt learning algorithm. The result of the simulation showed that the main steam temperature modeling based on neural network with Levenberg-Marqurdt learning algorithm is able to replicate closely the actual plant behavior. Generator output, main steam flow, main steam pressure and total spraywater flow are proven to be the main parameters affected the behavior of main steam temperature in coal fired power plant. 2013 Conference or Workshop Item PeerReviewed Mazalan, N. A. and Malek, A. A. and Wahid, M. A. and Mailah, M. and Saat, A. and Sies, M. M. (2013) Main steam temperature modeling based on levenberg-marquardt learning algorithm. In: Applied Mechanics And Materials. http://dx.doi.org/10.4028/www.scientific.net/AMM.388.307
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mazalan, N. A.
Malek, A. A.
Wahid, M. A.
Mailah, M.
Saat, A.
Sies, M. M.
Main steam temperature modeling based on levenberg-marquardt learning algorithm
description Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often describe as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt learning algorithm. The result of the simulation showed that the main steam temperature modeling based on neural network with Levenberg-Marqurdt learning algorithm is able to replicate closely the actual plant behavior. Generator output, main steam flow, main steam pressure and total spraywater flow are proven to be the main parameters affected the behavior of main steam temperature in coal fired power plant.
format Conference or Workshop Item
author Mazalan, N. A.
Malek, A. A.
Wahid, M. A.
Mailah, M.
Saat, A.
Sies, M. M.
author_facet Mazalan, N. A.
Malek, A. A.
Wahid, M. A.
Mailah, M.
Saat, A.
Sies, M. M.
author_sort Mazalan, N. A.
title Main steam temperature modeling based on levenberg-marquardt learning algorithm
title_short Main steam temperature modeling based on levenberg-marquardt learning algorithm
title_full Main steam temperature modeling based on levenberg-marquardt learning algorithm
title_fullStr Main steam temperature modeling based on levenberg-marquardt learning algorithm
title_full_unstemmed Main steam temperature modeling based on levenberg-marquardt learning algorithm
title_sort main steam temperature modeling based on levenberg-marquardt learning algorithm
publishDate 2013
url http://eprints.utm.my/id/eprint/51154/
http://dx.doi.org/10.4028/www.scientific.net/AMM.388.307
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