(233a) Property Predictions for Packed Columns Using Random and Distinct Element Digital Packing Algorithms | AIChE

(233a) Property Predictions for Packed Columns Using Random and Distinct Element Digital Packing Algorithms


Xu, C. - Presenter, Structure Vision Ltd.
Jia, X. - Presenter, University of Leeds
Williams, R. - Presenter, University of Leeds
Stitt, H. - Presenter, Johnson Matthey
El-Bachir, S. - Presenter, University of Cambridge
Sederman, A. J. - Presenter, University of Cambridge
Gladden, L. F. - Presenter, University of Cambridge
Nijemeisland, M. - Presenter, Johnson Matthey Catalysts

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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