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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| Main Authors: | , |
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| Format: | Article |
| Published: |
Taylor & Francis
2010
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| Online Access: | http://eprints.um.edu.my/15355/ https://doi.org/10.1080/00986440903088819 |
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