Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors
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 fo...
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| Format: | Article |
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Taylor & Francis
2010
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| author | Mjalli, Farouq Sabri Al-Mfargi, A. |
| author_facet | Mjalli, Farouq Sabri Al-Mfargi, A. |
| author_sort | Mjalli, Farouq Sabri |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:38:39Z |
| format | Article |
| id | um.eprints-15355 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:38:39Z |
| publishDate | 2010 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-153552019-05-09T04:44:29Z http://eprints.um.edu.my/15355/ Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors Mjalli, Farouq Sabri Al-Mfargi, A. Q Science (General) 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. Taylor & Francis 2010 Article PeerReviewed Mjalli, Farouq Sabri and Al-Mfargi, A. (2010) Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors. Chemical Engineering Communications, 197 (3). pp. 318-342. ISSN 0098-6445, DOI https://doi.org/10.1080/00986440903088819 <https://doi.org/10.1080/00986440903088819>. https://doi.org/10.1080/00986440903088819 doi:10.1080/00986440903088819 |
| spellingShingle | Q Science (General) Mjalli, Farouq Sabri Al-Mfargi, A. Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title | Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title_full | Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title_fullStr | Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title_full_unstemmed | Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title_short | Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| title_sort | neural network based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors |
| topic | Q Science (General) |
| work_keys_str_mv | AT mjallifarouqsabri neuralnetworkbasedheatandmasstransfercoefficientsforthehybridmodelingoffluidizedreactors AT almfargia neuralnetworkbasedheatandmasstransfercoefficientsforthehybridmodelingoffluidizedreactors |