(442p) Biot Number Effects on the Local Heat and Mass Transfer Rate in Fixed and Fluidized Beds

Authors: 
Krainer, F., Graz University of Technology
Radl, S., Graz University of Technology
Puffitsch, T., Graz University of Technology
Kloss, C., DCS Computing GmbH

Predicting the local heat and mass transfer rate in fluidization processes
is an important task for laboratory experiments, as well as for industrial
plant design, operation, and optimization. While predicting the heat (or mass)
transfer rate is already a formidable task on its own, heat and mass transfer
inside the particles even complicate the situation. The Biot number, relating
the external to the intra-particle heat (or mass) flux, is a dimensionless
number that helps in quantifying the relevance of intra-particle transport
phenomena. However, modeling the effect of the Biot number on the local heat
and mass transfer process is still challenging.

In this study we will present two strategies that can be used to model the
effect of the Biot number in fixed and fluidized particle beds: our first
strategy is based on integral heat and mass balances for the gas and particle
phase. These balance equations are spatially discretized using a
one-dimensional finite-difference approach. We use a second-order implicit
formulation for temporal discretization, as well as a robust technique to treat
the exchange terms for an efficient integration of the equations. Our model
considers particle and gas-phase dispersion to account for the effect of
pseudo-turbulence and the agitation by bubbles in case of fluidized beds.

Our second strategy is based on a CFD-DEM simulation approach, which
accounts for intra-particle temperature (and concentration) gradients using the
ParScale package. While the LIGGGHTS®/CFDEM® package is used to integrate the
governing equations for fluid and particle flow, the newly established ParScale
package (https://github.com/CFDEMproject/ParScale-PUBLIC)
is used to resolve intra-particle profiles. The advantage of our second
approach is that we are able to compute particle and (meso-scale) gas
dispersion rates directly, as well as to access intra-particle temperature (and
concentration) profiles of all particles. Finally, we will compare the two
modeling strategies, present a workflow for the calibration of the simple 1D
model, as well as highlight operating regimes in which Biot number effects must
be accounted for.

Figure: Temperature at the
particle surface (hemi-spheres) as well as at the center of each particle
(spheres) in a fluidized bed (fluidization at 2.3 times the minimal
fluidization velocity, mean Biot number 1.7)

Acknowledgement and Disclaimer

SR and TP acknowledge funding through the NanoSim project (http://www.sintef.no/projectweb/nanosim),
as well as the ?NAWI Graz? project by providing access to dcluster.tugraz.at.

LIGGGHTS® and CFDEM® are registered trade marks of DCS Computing GmbH, the
producer of the LIGGGHTS® and CFDEM® software.

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