Liquid back-mixing in
packed-bubble column reactors:
A state-of-the-art correlation
Lamia Belfares1), Miryan
Cassanello2), Bernard P.A. Grandjean1) and Faïçal Larachi1)
1) Department of Chemical Engineering and CERPIC
Université Laval, Ste-Foy, Québec, Canada, G1K 7P4
Corresponding author: firstname.lastname@example.org
2)PINMATE, Departmento de Industrias, Facultad de Ciencias Exactas y Naturales,
Universidad de Buenos Aires, Ciudad Universitaria, 1428 Buenos Aires, Argentina
Catalysis Today 64, 321-332 (2001)
The extent of liquid back-mixing in gas-liquid cocurrent upflow packed-bubble column reactors is quantified in terms of an axial dispersion coefficient or its corresponding dimensionless Péclet number. Effects of reactor operating conditions on the axial dispersion coefficient are not properly accounted for by the available literature correlations wherein most often the Péclet number is expressed solely in terms of the gas and liquid Reynolds numbers or superficial velocities. Based on the broadest experimental databank (1322 measurements, 11 liquids, 4 gases, 28 packing materials, 14 columns diameters, Newtonian, non-Newtonian, aqueous, organic, coalescing and non-coalescing liquids, high pressure, bubble and pulsing flow regime conditions), a state-of-the-art liquid axial dispersion coefficient correlation is obtained by combining neural network modeling and dimensional analysis. Thorough qualitative and quantitative analyses of the constructed databank demonstrate the robustness of the proposed correlation to restore the variety of trend variations of liquid Péclet numbers reported in the literature.
You can get
the pecletflb.zip file that contains the
following source codes:
- Fortran (to compute the liquid Peclet number)
- Excel worksheet simulator for flooded-bed reactor ( to compute liquid Peclet number, pressure drop and liquid saturation)
(and the updated version flbsimul-peclet_update.zip ).
You may also download our Excel worksheet Trickle-bed simulator to simulate mass transfer, pressure drop, liquid holdup and flow regime transition.
The neural correlation was developped with the software NNFit