Use of Polymer Membranes for Modeling Desulfurization in the Process of Pervaporation through Artificial Neural Network

Kazemimoghadam, Mansoor; Sadeghi, Nastatran

doi:10.22341/jacs.on.00501p383

Cited by 3 publications

(2 citation statements)

References 22 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pervaporation (PV) desulfurization technology has received more and more attention worldwide [6]. Compared with traditional desulfurization technology, it has many potential advantages, including low energy consumption, low pollution, high separation efficiency, and simple equipment [3,[6][7][8][9]. In recent years, there are a large number of investigations on photovoltaic gasoline desulfurization.…”

Section: Introductionmentioning

confidence: 99%

Effect of MoS2 Yolk-Shell Nanostructure on the Thiophene Separation Performance of PEG Membrane

Hou

Peng

Lan

et al. 2022

Advances in Polymer Technology

View full text Add to dashboard Cite

Constructing facilitated transport based on π-complexation has been drawing more and more attention in mixed matrix membranes (MMMs) for pervaporative desulfurization. Herein, a unique molybdenum disulfide (MoS2) yolk-shell nanostructure (MYNS) was prepared and incorporated into the polyethylene glycol (PEG) matrix to fabricate MMMs for model gasoline desulfurization by PV. Moreover, the effects of MYNS content, feed sulfur concentration, and feed temperature on the performance of PEG/MYNS MMMs were evaluated. It was found that there is good interfacial compatibility between the MYNS filler and the PEG matrix, and the resultant MMMs show enhanced swelling resistance against thiophene. The PV results revealed that the as-fabricated MMMs are thiophene-selective, and their desulfurization performance in the pervaporative removal of thiophene from n-octane is remarkably evaluated due to the addition of MYNS. The MMMs display the highest sulfur enrichment factor of 4.02 with an associated permeation flux of 2587 g·m−2·h−1 with the MYNS loading of 3 wt. % when carrying out in an n-octane and thiophene (500 μg·g−1) mixture at 343 K. Furthermore, a consistent increment in the permeation flux accompanied with a continuous reduction in the enrichment factor was observed with increasing the feed sulfur concentration and feed temperature. This work may offer great potential for practical gasoline desulfurization applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of MoS2 Yolk-Shell Nanostructure on the Thiophene Separation Performance of PEG Membrane

Hou

Peng

Lan

et al. 2022

Advances in Polymer Technology

View full text Add to dashboard Cite

show abstract

“…Kaz. and N. Sadeghi[2] Modeling desulfurization of diesel. ANN Bellos et al[3] Simulation of the reactor performance for HDS.…”

mentioning

confidence: 99%

Prediction of Sulfur Content in Desulfurization Process Using a Fuzzy-Logic Based Model

Al-Jamimi¹

2019

SSP

View full text Add to dashboard Cite

In petroleum industry, hydrodesulphurization (HDS) process is considered as one of the crucial catalytic units in which the sulfur is mostly eradicated. The modeling of HDS process is very important for the proper understanding of the process operation to be optimized. The studies conducted, in this area, focused on predicting parameters using analytical, empirical and numerical approaches. However, a typical desulfurization process is constantly faced with an uncertainty, which should be considered in a reasoning way. Therefore, this work aims to explore the use of fuzzy logic (FL) inference system in creating models of the HDS process for the prediction of sulfur reduction from oil. In order to validate the proposed model, we employed experimental data from the HDS setup. The simulated sulfur content results obtained from the proposed model correspond closely to the real experimental values. The outstanding performance of the developed FL-based model suggests its potential in predicting sulfur content for optimization of the HDS process. The model demonstrates promising results in terms of high correlation (R2=0.98) and minimal percentage of error (AARE=0.072).

show abstract

Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil

Alardhi¹,

Jabbar²,

Al-Jadir³

et al. 2022

AIP Conference Proceedings

View full text Add to dashboard Cite

Use of Polymer Membranes for Modeling Desulfurization in the Process of Pervaporation through Artificial Neural Network

Cited by 3 publications

References 22 publications

Effect of MoS2 Yolk-Shell Nanostructure on the Thiophene Separation Performance of PEG Membrane

Effect of MoS2 Yolk-Shell Nanostructure on the Thiophene Separation Performance of PEG Membrane

Prediction of Sulfur Content in Desulfurization Process Using a Fuzzy-Logic Based Model

Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil

Contact Info

Product

Resources

About