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International Journal of Chemical Engineering
Volume 2012 (2012), Article ID 945314, 15 pages
http://dx.doi.org/10.1155/2012/945314
Research Article

Characterization of Minimum Impeller Speed for Suspension of Solids in Liquid at High Solid Concentration, Using Gamma-Ray Densitometry

Department of Chemical Engineering, École Polytechnique de Montréal, P.O. Box 6079, Station Centre-Ville, Montreal, QC, Canada H3C 3A7

Received 28 February 2012; Accepted 21 April 2012

Academic Editor: See-Jo Kim

Copyright © 2012 Rouzbeh Jafari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The successful design and operation of Liquid-Solid (LS) and Gas-Liquid-Solid (GLS) stirred tank reactors requires an accurate determination of the level of solid suspension needed for the process at hand. A poor design of the stirred tank to achieve optimum conditions and maintain the system under these conditions during operation may cause significant drawbacks concerning product quality (selectivity and yield) and cost. In this paper, the limitations of applying conventional measurement techniques for the accurate characterization of critical impeller speed for just off-bottom suspension () at high solid concentrations are described. Subsequently, the Gamma-Ray Densitometry technique for characterizing is introduced, which can overcome the limitations of previous experimental techniques. The theoretical concept of this method is explained, and experimental validation is presented to confirm the accuracy of the Gamma-Ray Densitometry technique. The effects of clearance, scale, and solid loading on for several impellers are discussed. Experimental values are compared with correlations proposed in the literatures, and modifications are made to improve the prediction. Finally, by utilizing the similarity to the incipient movement of solid particles in other systems, a theoretical model for prediction is presented.