Two-Phase Frictional Pressure Drop in Flooded-Bed Reactors: A State-of-the-art Correlation

Abstract

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.

Advanced Diagnostic Techniques for Three-Phase Slurry Bubble Column Reactors(sbcr)

“…Out of the number of dimensionless groups derived, we used ANN regression to establish the best set of chosen dimensionless groups, which describes overall gas holdup (Bensetiti et al, 1997, Larachi et al, 1998. The following criteria guide the choice of the set of input dimensionless groups:…”

Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines

“…Leib et al (1995) used a neural network model along with the mixed-cell model to predict slurry bubble column performance for the Fischer-Tropsch synthesis. Bensetiti et al (1997), Larachi et al (1998), Piche et al (2001) and Iliuta et al (2002) used an ANN to improve the prediction of various hydrodynamic parameters in packed bed and fluidized bed reactors.…”

Advanced Diagnostic Techniques for Three-Phase Slurry Bubble Column Reactors(sbcr)

“…Out of the number of dimensionless groups derived, we used ANN regression to establish the best set of chosen dimensionless groups, which describes overall gas holdup (Bensetiti et al, 1997, Larachi et al, 1998. The following criteria guide the choice of the set of input dimensionless groups: -The dimensionless groups should be as few as possible, -Each group should be highly cross-correlated to the output parameter, -These input groups should be weakly cross-correlated to each other, -The selected input set should give the best output prediction, which is checked by using statistical analysis [e.g., average absolute relative error (AARE), standard deviation, cross-correlation coefficient].…”