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Improving the prediction of liquid back‐mixing in trickle‐bed reactors using a neural network approach
Author(s) -
Piché Simon,
Larachi Faïçal,
Iliuta Ion,
Grandjean Bernard P A
Publication year - 2002
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.666
Subject(s) - reynolds number , dispersion (optics) , dimensionless quantity , standard deviation , mechanics , piston (optics) , mixing (physics) , artificial neural network , flow (mathematics) , parametric statistics , mathematics , materials science , statistics , thermodynamics , physics , computer science , machine learning , turbulence , optics , wavefront , quantum mechanics
Current correlations aimed at estimating the extent of liquid back‐mixing, via an axial dispersion coefficient, in trickle‐bed reactors continue to draw doubts on their ability to conveniently represent this important macroscopic parameter. A comprehensive database containing 973 liquid axial dispersion coefficient measurements ( D AX ) for trickle‐bed operation reported in 22 publications between 1958 and 2001 was thus used to assess the convenience of the few available correlations. It was shown that none of the literature correlations was efficient at providing satisfactory predictions of the liquid axial dispersion coefficients. In response, artificial neural network modeling is proposed to improve the broadness and accuracy in predicting the D AX , whether the Piston–Dispersion (PD), Piston–Dispersion–Exchange (PDE) or PDE with intra‐particle diffusion model is employed to extract the D AX . A combination of six dimensionless groups and a discrimination code input representing the residence‐time distribution models are used to predict the Bodenstein number. The inputs are the liquid Reynolds, Galileo and Eötvos numbers, the gas Galileo number, a wall factor and a mixed Reynolds number involving the gas flow rate effect. The correlation yields an absolute average error ( AARE ) of 22% for the whole database with a standard deviation on the AARE of 24% and remains in accordance with parametric influences reported in the literature. © 2002 Society of Chemical Industry

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