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

다국어 초록 (Multilingual Abstract)

We investigated the performance of a T-type microchannel for mercaptan extraction from light straightrun 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.

참고문헌 (Reference)

1 M. Khalkhali, 171 : 403-, 2019

2 M. R. Mirani, 1422 : 170-, 2015

3 B. Basu, 35 : 571-, 1993

4 R. Barzamini, 130 : 46-, 2014

5 D. Yabroff, 32 : 257-, 1940

6 H. Sahraie, 99 : 81-, 2015

7 M. Rahimi, 98 : 147-, 2015

8 M. Izadi, 356 : 570-, 2019

9 M. Filiz, 81 : 167-, 2006

10 A. S. Afshar, 79 : 2011

11 S. Uslu, 276 : 117990-, 2020

12 M. Asadollahzadeh, 123 : 25-, 2014

13 A. Talebi, 47 : 334-, 2012

14 M. Darekar, 144 : 54-, 2014

15 X. Chen, 106 : 593-, 2017

16 M. R. Ehsani, 32 : 71-, 2013

17 R. Liu, 23 : 711-, 2005

18 A. S. Afshar, 31 : 2364-, 2013

19 S. Ganguly, 31 : 1283-, 2013

20 M. Shahrak, 37 : 791-, 2015

21 A. Parvareh, 14 : 55-, 2017

22 A. Akopyan, 92 : 865-, 2019

23 M. N. Kashid, 158 : 233-, 2010

24 K. K. Singh, 98 : 95-, 2015

25 L. Zhang, 4 : 3-, 2015

26 S. Dai, 56 : 12717-, 2017

27 M. Al-Azzawi, 140 : 43-, 2019

28 X. Chen, 23 : 2649-, 2017

29 C. J. LaFoy, "US Patent, 4,705,620"

30 "UOP163-10, Hydrogen Sulfide and Mercaptan Sulfur in Liquid Hydrocarbons by Potentiometric Titration" TM International

31 Mrinmoy Karmakar, "Separation of tetrahydrofuran using RSM optimized accelerator-sulfur-filler of rubber membranes: Systematic optimization and comprehensive mechanistic study" 한국화학공학회 34 (34): 1416-1434, 2017

32 Ghassan Rokan Daham, "Re-refining of used lubricant oil by solvent extraction using central composite design method" 한국화학공학회 34 (34): 2435-2444, 2017

33 Ken-Ichiro Sotowa, "Mixing and Enzyme Reactions in a Microchannel Packed with Glass Beads" 한국화학공학회 22 (22): 552-555, 2005

34 허윤석, "Microfluidic extraction using two phase laminar flow for chemical and biological applications" 한국화학공학회 28 (28): 633-642, 2011

35 Pouria Amani, "Investigation of hydrodynamic and mass transfer of mercaptan extraction in pulsed and non-pulsed packed columns" 한국화학공학회 34 (34): 1456-1465, 2017

36 Qiang Liu, "High performance removal of methyl mercaptan on metal modified activated carbon" 한국화학공학회 35 (35): 137-146, 2018

37 김보민, "Effect of surfactant on wetting due to fouling in membrane distillation membrane: Application of response surface methodology (RSM) and artificial neural networks (ANN)" 한국화학공학회 37 (37): 1-10, 2020

38 Sayiter Yildiz, "Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process" 한국화학공학회 34 (34): 2423-2434, 2017

39 Alireza Fazlali, "Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate" 한국화학공학회 30 (30): 1681-1686, 2013