Interfacial Mass
Transfer in Randomly Packed Towers:
A
Confident Correlation for Environmental Applications
Simon Piché, Bernard
P.A. Grandjean*, Ion Iliuta, Faïçal
Larachi
Department
of
Environmental Science & Technology,
35, 4817-4822 (2001)
Abstract ¾Volumetric
mass transfer coefficients (kLaw,
KLaw, kGaw,
KGaw) required for randomly dumped
packed tower design were gathered from the literature to generate a working
database comprehending 2,675 measurements relevant to water and air pollution
abatement processes. The cross-examination of two important correlations
predicting mass transfer coefficients was achieved through this database (Onda correlation, 1968; Billet and Schultes
correlation, 1993). Some limitations regarding their accuracy level came to
light. Artificial neural network (ANN) modeling is then proposed to improve the
accuracy in predicting all four mass transfer coefficients. A sole and robust
ANN correlation was built to predict the dimensionless gas (or liquid) film
Sherwood number as a function of a combination of
six dimensionless groups, namely the liquid Reynolds , Froude , Eotvös numbers, the gas (or liquid)
Schmidt number , the Lockhart-Martinelli
parameter and a bed-characterizing
number . Using the ANN correlation along
with the two-film theory, a reconciliation procedure was also implemented
resulting in accurate predictions of the gas (or liquid) overall (or film)
volumetric mass transfer coefficients. The correlation yielded an absolute
average relative error of 22.1%; a standard deviation of 21.1% based on whole
database and the ANN predictions remain in accordance with the physical
evidence reported in the literature.
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