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Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks

In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under...

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Main Author: Ibrehem, A.S.
Format: Conference or Workshop Item
Language:English
Published: 2009
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/10031/1/04IntEC2009.pdf
http://eprints.um.edu.my/10031/
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id my.um.eprints.10031
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spelling my.um.eprints.100312014-12-26T03:12:28Z http://eprints.um.edu.my/10031/ Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks Ibrehem, A.S. TA Engineering (General). Civil engineering (General) In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods. 2009 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/10031/1/04IntEC2009.pdf Ibrehem, A.S. (2009) Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks. In: International Engineering Convention, 11-14 May 2009, Damascus, Syria.. (Submitted)
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ibrehem, A.S.
Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
description In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.
format Conference or Workshop Item
author Ibrehem, A.S.
author_facet Ibrehem, A.S.
author_sort Ibrehem, A.S.
title Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
title_short Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
title_full Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
title_fullStr Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
title_full_unstemmed Hybrid Modeling Of Well-Mixed Model For Fluidized Bed Reactors Using Artificial Neural Networks
title_sort hybrid modeling of well-mixed model for fluidized bed reactors using artificial neural networks
publishDate 2009
url http://eprints.um.edu.my/10031/1/04IntEC2009.pdf
http://eprints.um.edu.my/10031/
_version_ 1643688689554948096
score 13.211869

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