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Two‐Phase Frictional Pressure Drop in Flooded‐Bed Reactors: A State‐of‐the‐art Correlation
Author(s) -
Larachi Faïçal,
Bensetiti Zouhir,
Grandjean Bernard P. A.,
Wild Gabriel
Publication year - 1998
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/(sici)1521-4125(199811)21:11<887::aid-ceat887>3.0.co;2-b
Subject(s) - dimensionless quantity , pressure drop , generalization , packed bed , correlation , drop (telecommunication) , artificial neural network , phase (matter) , mechanics , thermodynamics , mathematics , engineering , chemistry , chromatography , physics , computer science , artificial intelligence , mechanical engineering , mathematical analysis , geometry , organic chemistry
A state‐of‐the‐art correlation for the prediction of frictional gas‐liquid pressure drop in cocurrent upflow fixed bed reactors was derived based on a wide hydrodynamic data bank of flooded packed‐bed reactors. The data bank, which contains more than 3400 measurements, was constructed using information collected from 22 sources over the past 40 years. The correlation, which relied upon combination of dimensional analysis and artificial feed‐forward neural networks, was expressed as a two‐phase friction factor function of the five most pertinent dimensionless groups (Re LG , Mo, St, Fr L , X L ). The prediction and generalization capabilities of the proposed correlation and the limits of existing literature correlations and models were demonstrated by systematic statistical tests over the constructed data bank.