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Experimental and modeling studies for intensification of mercaptans extraction from LSRN using a microfluidic system

  • Separation Technology, Thermodynamics
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

We investigated the performance of a T-type microchannel for mercaptan extraction from light straight-run naphtha (LSRN) with sodium hydroxide solution. The aim of this work is to introduce the microfluidic system as a potential tool for mercaptan extraction from light petroleum products. Modeling the extraction process of mercaptan from LSRN has not been carried out previously. In this regard, mercaptan extraction was modeled by response surface methodology (RSM) and artificial neural network (ANN) to analyze the effect of operating parameters on the mercaptan extraction process. The independent variables are considered as temperature, sodium hydroxide concentration, and the volume ratio of sodium hydroxide to LSRN. Two models were compared based on error analysis of the predicted data. Root mean square error, mean relative error, and determination coefficient for the neural network were 0.5650, 0.4341, and 0.9862, respectively. The values of these parameters for the RSM model were 0.6854, 0.7648, and 0.9798. The results showed that the prediction accuracy for both models is appropriate, but the precision of the neural network model is slightly higher than that of the RSM model. The genetic algorithm (GA) technique determined the optimal values of the independent variables with the aim of maximizing the extraction percentage. The mercaptan extraction percentage value of 85.08% was achieved at 303.15 K, the sodium hydroxide concentration of 20 wt%, and the volume ratio of sodium hydroxide to LSRN of 0.128. Furthermore, results showed a higher mercaptan extraction percentage of the microfluidic system compared to a conventional extractor at the same process condition.

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Abbreviations

b:

bias

COS:

carbonyl sulfide

CS2 :

carbon disulfide

E:

extraction percentage

e:

error term

F:

transfer function

K E :

overall extraction constant

M:

mercaptan concentration [ppm]

m:

number of input variables

N:

number of data points

n:

number of neurons

R2 :

coefficient of determination

RS− :

ionized mercaptan

RSH:

mercaptan

W:

weight

X:

independent variables

Y:

predicted response of RSM model

y:

predicted response of ANN model

a:

average

aq:

aqueous phase

H:

hidden layer

in:

inlet

max:

maximum

min:

minimum

norm:

normalized

org:

organic phase

out:

outlet

t:

target data

ANN:

artificial neural network

ANOVA:

analysis of variance

CCD:

central composite design

GA:

genetic algorithm

KORC:

kermanshah oil refinery company

LPG:

liquefied petroleum gas

LSRN:

light straight-run naphtha

MRE:

mean relative error

RMSE:

root mean square error

RSM:

response surface methodology

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Acknowledgements

The authors wish to express thanks to the Kermanshah Oil Refinery Company (KORC) for preparing the LSRN samples.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Eng., Arak University, Arak, Iran

    Mohammad Reza Mirani & Alireza Fazlali

  2. CFD research center, Chemical Engineering Department, Razi University, Kermanshah, Iran

    Masoud Rahimi

Authors
  1. Mohammad Reza Mirani
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  2. Alireza Fazlali
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  3. Masoud Rahimi
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Correspondence to Alireza Fazlali.

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Mirani, M.R., Fazlali, A. & Rahimi, M. Experimental and modeling studies for intensification of mercaptans extraction from LSRN using a microfluidic system. Korean J. Chem. Eng. 38, 1023–1031 (2021). https://doi.org/10.1007/s11814-021-0749-9

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  • DOI: https://doi.org/10.1007/s11814-021-0749-9

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