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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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