(62g) Towards the CFD Modelling of Bimodal Gas Fluidized Beds
The many advantageous properties
of gas-fluidized beds have enabled its widespread use across many industries.
In most industrial applications of fluidization the suspension consists of
non-spherical particles of different diameters and sometimes-different
densities. The materials of interest in the present study, sponsored by
Huntsman-Tioxide, are industrial raw materials used in the manufacture of
titanium dioxide pigment, the two materials belong to the Geldart Group B of
the classification of powders and have the following characteristics; dp1 =
density1 = 4200kg/m3, dp2=156x10-6m,
density2 = 3200kg/m3.
Eulerian-Eulerian models have
become useful in the study of the hydrodynamic behaviour of particulate flows;
this development has been facilitated by quantum leaps in computing power. In
the Eulerian-Eulerian approach, both the fluid and particle phases are assumed
to be interpenetrating continua and are described in terms of conservation of
mass and momentum. In the present study, the Eulerian-Eulerian two fluid
continuum model built in CFX 4.4 has been used to model and simulate the fluid
dynamics of the mono-dispersed system of the two mentioned individual materials
and their bimodal behaviour within the bubbling regime of fluidization.
Granular Kinetic theory, which is
already available from CFX 4.4, has been used for 2D time-dependent simulations
for the mono-dispersed system of the individual materials. Model predictions
have been compared with experimental data obtained on pressure, voidage and bed
height fluctuations. The aim of the project is then to further develop the
modelling work towards the simulations of the bimodal system at different
weight fractions. To rise to this challenge, a new Particle Bed model, which
makes use of volume-averaged mass and momentum balances for the fluid and
particle phases, is proposed for the modelling of the bimodal particle
mixtures. The model is used to investigate the stability and fluidization
quality of the feedstock mixture, with model predictions compared with
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