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NEURAL NETWORK-BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS.

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Title: NEURAL NETWORK-BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS.
Authors: Mjalli, FarouqS.1 (AUTHOR) farouqsm@yahoo.com, Al-Mfargi, A.2 (AUTHOR)
Source: Chemical Engineering Communications. Mar2010, Vol. 197 Issue 3, p318-342. 25p. 3 Diagrams, 4 Charts, 5 Graphs.
Subjects: Fluidized reactors, Fluidized-bed furnaces, Heat transfer, Statistical correlation, Mass transfer
Abstract: The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the mass and heat transfer coefficients. Unfortunately, published empirical-based correlations have the common shortcoming of low prediction efficiency compared with experimental data. 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. [ABSTRACT FROM AUTHOR]
Copyright of Chemical Engineering Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: NEURAL NETWORK-BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS.
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  Data: <searchLink fieldCode="AR" term="%22Mjalli%2C+FarouqS%2E%22">Mjalli, FarouqS.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> farouqsm@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Al-Mfargi%2C+A%2E%22">Al-Mfargi, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Chemical+Engineering+Communications%22">Chemical Engineering Communications</searchLink>. Mar2010, Vol. 197 Issue 3, p318-342. 25p. 3 Diagrams, 4 Charts, 5 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Fluidized+reactors%22">Fluidized reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Fluidized-bed+furnaces%22">Fluidized-bed furnaces</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+transfer%22">Heat transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Mass+transfer%22">Mass transfer</searchLink>
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  Data: The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the mass and heat transfer coefficients. Unfortunately, published empirical-based correlations have the common shortcoming of low prediction efficiency compared with experimental data. 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Chemical Engineering Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Fluidized reactors
        Type: general
      – SubjectFull: Fluidized-bed furnaces
        Type: general
      – SubjectFull: Heat transfer
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      – SubjectFull: Mass transfer
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      – TitleFull: NEURAL NETWORK-BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS.
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              Text: Mar2010
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