Wednesday, 26 April 2006 - 4:30 PM

Property Predictions for Packed Columns Using Random and Distinct Element Digital Packing Algorithms

Chaoshui Xu1, Xiaodong Jia2, R. A. Williams3, Hugh Stitt4, S. El-Bachir5, Andrew J. Sederman5, Lynn F. Gladden5, and Michiel Nijemeisland4. (1) Structure Vision Ltd., Leeds Innovation Centre, Clarendon Road, LS2 9DF, UK, Leeds, United Kingdom, (2) School of Process, Environmental and Materials Engineering, University of Leeds, Houldsworth Building, Clarendon Road, Leeds, LS2 9JT, United Kingdom, (3) Institute of Particle Science and Engineering, University of Leeds, Houldsworth Building, Clarendon Road, Leeds, LS2 9JT, United Kingdom, (4) Johnson Matthey Catalysts, PO Box 1, Billingham, Cleveland, TS23 1LB, United Kingdom, (5) Department of Chemical Engineering, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK, Cambridge, United Kingdom

Existing theories and computer models for packed columns are either not capable of handling complex pellet shapes or based on over-simplified packing geometry. A digital packing algorithm, namely DigiPac, has recently been developed to fill the gap. It is capable of packing of particles of any shapes and sizes in a container of arbitrary geometry, and is a first step towards a practical computational tool for reliable predictions of packed column properties based on the actual pellet shapes.

DigiPac can operate in two modes: a Monte Carlo mode in which particles undergo directional diffusive motions; and a Discrete Element mode where translations and rotations of particles are governed by physical laws. The former is faster but in certain cases less accurate, whereas the latter is slower but produces significantly more accurate predictions.

Both modes have been used in simulating packed columns of real pellet shapes. Results for spheres and cylinders - the most commonly used for packed columns are reported. Comparisons are made between DigiPac predictions under different modes and experimental data obtained using nuclear magnetic resonance (NMR) imaging technique, in terms of mean packing density, voids distribution, and pellets orientation distribution. Good agreements have been observed.

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