The extent of liquid back-mixing in gas-liquid concurrent upflow packed-bub
ble column reactors is quantified in terms of an axial dispersion coefficie
nt or its corresponding dimensionless Peclet number. Effects of reactor ope
rating conditions on the axial dispersion coefficient are not properly acco
unted for by the available literature correlations, wherein most often the
Peclet number is expressed solely in terms of the gas and liquid Reynolds n
umbers or superficial velocities. Based on the broadest experimental databa
nk (1322 measurements, II liquids, four gases, 28 packing materials, 14 col
umns diameters, Newtonian, non-Newtonian, aqueous, organic, coalescing and
non-coalescing liquids, high pressure, bubble and pulsing how regime condit
ions), a state-of-the-art liquid axial dispersion coefficient correlation i
s obtained by combining neural network modeling and dimensional analysis. T
horough qualitative and quantitative analyses of the constructed databank d
emonstrate the robustness of the proposed correlation to restore the variet
y of trend variations of liquid Peclet numbers reported in the literature.
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