(732e) Cluster Formation Mechanism in Boundless Down-Flow Gas-Particle Systems | AIChE

(732e) Cluster Formation Mechanism in Boundless Down-Flow Gas-Particle Systems

Authors 

Gómez, N. - Presenter, Universidad Nacional de Colombia - Sede Medellín, Facultad de Minas, Bioprocesos y Flujos Reactivos
Molina, A., Universidad Nacional de Colombia
Marin, G. B., Ghent University

Abstract submitted to the Particle Technology Forum in the AIChE Annual
Meeting 2019. Orlando, USA.

Cluster
formation mechanism in boundless down-flow gas-particle systems

Noel Gomeza,b, Alejandro Molinab,
Guy B. Marina, and Kevin M. Van Geema

a Laboratory for Chemical Technology. Ghent
University. Technologiepark
125-9052 Ghent, Belgium.

b Universidad Nacional de
Colombia – Sede Medellín. Facultad de Minas, Calle 80 No 65-223, Medellín,
Colombia

Key-words: Particle clustering, downer, computational fluid dynamics,
particle technology.

Several Large-Eddy simulations (LES) coupled with the discrete
element method (DEM) were performed in 3D boxes with periodic boundary
conditions to study the dynamics of particle clusters. The investigation is
based on a parametric analysis varying the Stokes number (Stk) and Reynolds
number (Re). These dimensionless groups characterize the hydrodynamic
conditions of the system.

Particle
clusters do not allow an ideal contact between phases in the reactor and,
therefore, the limitations for heat and mass transfer increase, and as such,
may reduce the efficiency of the reactions (low-conversion zones). In reactors
with downward flowing fluid, so-called downers, the positive effect of gravity
in the stream-wise direction prevents particle back-mixing; however, clustering
is not entirely prevented. It has been proven the strong effect of walls on the
formation of clusters in downers (Gomez et al.), boosted by the low turbulence
and velocity in this zone, and by the fact that particles lose energy when they
hit the walls. It is also possible to find agglomerations in regions far from
the wall-influenced zones of the reactors. This fact suggests that there are
other mechanisms of cluster formation that allow the presence of such
structures in the reactor.

One way to study these phenomena is to use
computational fluid dynamics (CFD) under a Euler-Lagrange approach, which
allows the assessment of the gas and particle dynamics independently, the local
interactions between the two phases, and collisions between particles. For that
aim, 3D boxes with periodic boundary conditions were used to analyze the
dynamics of particles in down-flow systems. The simulations covered a range of solid/gas
mass ratio (CTO) from 5 to 30, which corresponds to a mean particle volume
fractions from 0.001 to 0.03 over the simulated volume, typical values for
gas-solid reactive flows in downers. The Smagorinsky LES model was implemented
to calculate the subgrid-scale turbulent values over the computational cells in
which the volume was divided. The LES model takes into account the main
fluctuations in the velocity fields of the gas phase and the influence of these
fluctuations on the particle motion.

It was found that clusters are formed even at Stk
lower than one, value up-to which it is presumed that particles follow the gas
streamlines and do not form clusters. Once the cluster is formed, it flows as
one single entity for a fraction of a second, during which its ‘cluster Stokes
number’ is normally above one, mainly because of its larger characteristic
length (3 to 6 times the particle diameter) than that of a single particle.
This behavior promotes the formation of groups of clusters that flow
independently and are not regularly seen in wall-influenced regions (see Figure
1).

The analysis was also carried out under reactive
conditions, using the catalytic cracking of gasoil as an example. Stk increased
with conversion because the gasoil cracked into lighter molecules, leading to a
decrease in the gas density and viscosity. In consequence, bigger and
long-lasting clusters were formed.



 Figure

1

. Gas
velocity: 1 m/s. CTO: 15. Stk: 0.5.

Bibliography

Gomez and Molina, 2019, Chem. Eng. Technol. 10.1002/ceat.201800463