PREDICTION OF MINIMUM
FLUIDIZATION VELOCITY IN
THREE-PHASE FLUIDIZED-BED REACTORS
F. Larachi, I. Iliuta1, , O. Rival, B.P.A. Grandjean
Department of Chemical Engineering & CERPIC, Laval University, Québec, Canada G1K 7P4
Chemical Engineering, Faculty of Industrial Chemistry, University Politehnica of
Industrial & Engineering Chemistry Research 39, 563-572 (2000)
Abstract: Knowledge of the onset of fluidization is of considerable relevance and the key to three-phase fluidized-bed reactors design and safe operation. Accordingly, using a wide historic ULmf database set up from the open literature, all the quantification methods proposed to predict the minimum fluidization liquid velocity in three-phase fluidized beds have been thoroughly revisited and critically evaluated herein. The database, providing access to diversified information related to over 540 measurements, is dedicated to embrace wide-ranging fluids and bed properties. It covers 30 various particles and 19 liquids and includes data such as aspect ratio, wall effect (or column-to-particle diameter) ratio, and ReLmf ranging from 0.8 to 27, 9 to 127, and 10-2 to 800, respectively. Indeed, the ULmf behavior is largely non-linear and thus cannot be accurately described using the existing empirical and physical approaches. As a result, multi-layer perceptron artificial neural networks have been extensively used to generate two highly accurate, a purely dimensional and a dimensionless, empirical correlations describing the ULmf,. Using cross-correlation analyses, two unsuspected effects, namely the wall effect ratio and the liquid surface tension, have been unveiled and then incorporated as correlating variables in the neural network correlations. The resulting mean relative error produced by the dimensional correlation is about 16% while the estimated error associated with the dimensionless-based correlation is 30%. The prediction errors from both correlations are found to be insensitive to column-to-particle diameter ratio. Moreover, the neural network approach has been shown to predict with moderate success the minimum fluidization gas velocity, UGmf, in liquid-buoyed gas-activated three-phase fluidized beds containing coarse particles (dv > 1 mm) at high input gas fractions.
Keywords : Three-phase fluidization, minimum fluidization velocity, neural network correlation, database
You can get the ulmf.zip file to compute (Excel Worksheet) the minimum liquid velocity.
You can get also the simflui3p.zip file (version 28/06/2001) that contains an Excel worksheet simulator to compute phase hold-ups and bubble wake model parameters.
The neural correlations were developped with the software = NNFit