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Experimental study and artificial intelligence modeling of liquid-liquid mass transfer in multiple-ring microchannels

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Abstract

This paper reports the results of using multiple-ring microchannels for enhancing liquid-liquid extraction performance. The effects of geometrical parameters including ring and distance characteristics on the extraction efficiency were studied. The mass transfer performance was analyzed using Water + Alizarin Red S+1-octanol system. By change in geometrical parameters, the extraction efficiency of multiple-ring microchannels improved up to 62.9% compared with that of the plain one. The performance ratio is defined based on two contrary effects of friction factor and extraction efficiency for evaluating the extraction performance. A performance ratio of 1.5 was achieved that confirmed the advantage of using this type of microfluidic extraction system. Artificial neural network and adaptive neuro-fuzzy inference system were utilized to evaluate the performance ratio of the multiple-ring microchannels. The mean relative error values of the testing data were 0.397% and 0.888% for the neural network and the neuro-fuzzy system, respectively. The estimation accuracy for both models is appropriate, but the precision of the neural network id higher than that of the neuro-fuzzy system. The genetic algorithm approach was employed to develop a new empirical correlation for predicting the performance ratio with a mean relative error of 1.558%.

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Abbreviations

A:

cross-sectional area of the microchannel [m2]

A1 and A2 :

membership function

B1 and B2 :

membership function

b:

bias

C:

Alizarin Red S concentration [mg/L]

ci :

constant

D:

rings diameter [m]

d:

inner diameter of the microchannels [m]

E:

extraction efficiency

F:

transfer function

f:

friction factor

H:

distance between rings [m]

kLa:

volumetric mass transfer coefficient [1/s]

L:

total length of the microchannels [m]

m:

number of input variables

N:

number of rings

n:

number of experimental data points

AP:

pressure drop [mbar]

Q:

volumetric flow rate [m3/s]

r:

number of neurons

Re:

Reynolds number

ta :

average of the target data

ti :

target data

tm :

fluid residence time [s]

Um :

two-phase mixture superficial velocity [m/s]

V:

volume of the mixing channel [m3]

W:

weight

X:

network input

x:

volume fraction of phases

Y:

final output of the network

ρ :

density [kg/m3]

μ :

dynamic viscosity [Pa s]

η :

performance ratio criterion [-]

δ :

rings characteristic [-]

β :

distance characteristic [-]

A:

average

aq:

aqueous phase

Exp:

experimental results

in:

inlet

m:

two-phase mixture

org:

organic phase

out:

outlet

Pred:

predicted values

*:

equilibrium

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Authors and Affiliations

  1. CFD Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran

    Fardin Hosseini & Masoud Rahimi

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  1. Fardin Hosseini
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  2. Masoud Rahimi
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Correspondence to Masoud Rahimi.

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Hosseini, F., Rahimi, M. Experimental study and artificial intelligence modeling of liquid-liquid mass transfer in multiple-ring microchannels. Korean J. Chem. Eng. 37, 411–422 (2020). https://doi.org/10.1007/s11814-019-0453-1

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  • DOI: https://doi.org/10.1007/s11814-019-0453-1

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