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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/347

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contributor.authorSahoo, A-
contributor.authorRoy, G K-
date.accessioned2006-10-16T03:49:45Z-
date.available2006-10-16T03:49:45Z-
date.issued2006-
identifier.citationPowder Technology, (Accepted post-print)en
identifier.urihttp://hdl.handle.net/2080/347-
descriptionCopyright for the published version belongs to Elsevier. This is Author's post print Versionen
description.abstractBinary mixtures of size-different dolomites are fluidized in a bed where co-axial promoters are introduced. The segregation characteristic of jetsam particles has been determined for different mixtures in terms of the segregation distance by empirically correlating the results with the various system parameters viz. initial static bed height, height of a layer of particles above the bottom grid, superficial gas velocity and average particle size of the mixture with dimensional analysis for both the un-promoted and the promoted beds. Correlations have also been developed with the above system parameters from an Artificial Neural Network approach. Segregation distances for the promoted and un-promoted beds have been compared. The results through the correlations thus developed with the above system parameters from ANN approach and the findings with respect to the dimensional analysis approach have been compared.en
format.extent323985 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherElsevieren
subjectBinary mixturesen
subjectSegregation distanceen
subjectCo-axial promotersen
subjectGas-solid fluidizationen
subjectArtificial Neural Networken
titleArtificial Neural Network Approach to Segregation Characteristic of Binary Homogeneous Mixtures in Promoted Gas Solid Fluidized Bedsen
typeArticleen
Appears in Collections:Journal Articles

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