ISSN:
0001-1541
Keywords:
Chemistry
;
Chemical Engineering
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
In this study back-propagation, feed-forward neural networks are applied to estimate mass-transfer parameters in fast fluidized beds of fine solids. These networks are trained to predict mass-transfer rates using measurements of the sublimation rate of coarse naphthalene balls in fast fluidized beds of fine glass beads at several solid-to-gas mass flow rates within the relevant superficial gas-velocity range. When tested to predict the effective diffusivities from a coarse particle to the bulk of the fast bed of fine solids, trained neural networks calculated the Sherwood number with high accuracy. It is demonstrated that back-propagation, feed-forward neural networks provide a more accurate correlation for the mass-transfer coefficient compared to those obtained by the currently used heuristic models.
Additional Material:
5 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/aic.690430705
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