Hydraulics and Mass
Transfer
in
Randomly Packed Towers Revisited
Simon Piché, Ion Iliuta, Bernard P.A. Grandjean, Faïçal Larachi*
Department of
Chemical Engineering and Center for Research on the Properties
of
Interfaces and Catalysis (CERPIC)
2001
GLS'5 conference in
Chem.
Abstract
New robust correlations and
mechanistic model of macroscopic fluid dynamic and gas-liquid mass transfer
characteristics for randomly packed towers were developed based on first
principles, neural network computing and dimensional analysis (ANN-DA). These
tools concerned the loading and flooding capacities, the total liquid holdup,
the irrigated pressure drop, the local volumetric liquid-side, kLa, and gas-side, kGa,
mass transfer coefficients, the overall volumetric, KLa
and KGa, mass transfer coefficients, and
the packing fractional wetted area. Validation of these tools was performed by
interrogating a broad experimental database including over 10750 measurements
published in the literature over the past seven decades. The fully-predictive
mechanistic model proved powerful in forecasting the tower hydraulics below the
loading point without requiring any adjustable parameter. On the other hand,
the ANN-DA correlations proved highly powerful in correlating the tower fluid
dynamics and gas-liquid inter-phase mass transfer regardless of the operating
flow regime. These approaches were also benchmarked with respect to the
comprehensive Billet and Schultes (1999)
phenomenological approach and the classical Onda et
al. (1968) mass transfer correlations.
Keywords: Randomly
packed towers, hydrodynamics, gas-liquid mass transfer, neural network models
You can get the packedbedsimulator.zip
file that contains an Excel worksheet simulator to compute pressure drop,
liquid holdup, loading/flooding capacities, film and overall volumetric mass
transfer coefficients.
You may also download our Excel worksheets simulators for Trickle-bed or Flooded
Bed reactors.
The neural
correlation was developped with the software NNFit