Optimization of Extractive Desulfurization of Model Oil With a Novel Green Deep Eutectic Solvent Using Genetic Algorithm-Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Shirani Mahboube; AKBARI ALI; NejadKooraki Atefeh; Goli Alireza; Azmoon Behnza; Shirani Nooshin; HABIBOLLAHI SAEED

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: PETROLEUM RESEARCH Year:2018 | Volume:28 | Issue:102 Page(s): 147-157

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,036

Download:

Cites:

Information Journal Paper

Title

Optimization of Extractive Desulfurization of Model Oil With a Novel Green Deep Eutectic Solvent Using Genetic Algorithm-Artificial Neural Network

Author(s)

Keywords

Extractive DesulfurizationQ1

Deep eutectic solventQ1

OptimizationQ2

Genetic Algorithm-artificial Neural NetworkQ1

Green ProcesQ1

Abstract

In this study, Extractive Desulfurization of dibenzothiophene from n-hexane as model fuel using 1, 10-phenantroline 2, 9-dicarboxamide-FeCl3-based choline chloride as a green, novel and efficient Deep eutectic solvent (DES) was considered. FT-IR, 1H NMR, and 13C NMR were used for the characterization of the synthetized DES. The effect of influential parameters of the mass ratio of fuel to DES, temperature, and time was investigated. Moreover, for 10 mL solution containing 500 mg L-1 dibenzothiophene in n-hexane, at obtained optimum conditions of mass ratio of fuel to DES (equal to) 33. 5, temperature (equal to) 25 ° C, and time (equal to) 15 min, the maximum sulfur removal percent of 93. 5 ± 0. 5 was achieved. The obtained experimental results were optimized by Genetic algorithm based on artificial neural network (GA-ANN). By using GA-ANN, the optimum conditions of 34. 4, 27. 33 ° C, and 16. 99 min were acquired for the mass ratio of fuel to DES, temperature, and time which showed high potential and ability of the applied model in the Optimization of the proposed process.

Cites

No record.

References

No record.

Cite

APA: Copy

Shirani, Mahboube, AKBARI, ALI, NejadKooraki, Atefeh, Goli, Alireza, Azmoon, Behnza, Shirani, Nooshin, & HABIBOLLAHI, SAEED. (2018). Optimization of Extractive Desulfurization of Model Oil With a Novel Green Deep Eutectic Solvent Using Genetic Algorithm-Artificial Neural Network. PETROLEUM RESEARCH, 28(102 ), 147-157. SID. https://sid.ir/paper/114723/en

Vancouver: Copy

Shirani Mahboube, AKBARI ALI, NejadKooraki Atefeh, Goli Alireza, Azmoon Behnza, Shirani Nooshin, HABIBOLLAHI SAEED. Optimization of Extractive Desulfurization of Model Oil With a Novel Green Deep Eutectic Solvent Using Genetic Algorithm-Artificial Neural Network. PETROLEUM RESEARCH[Internet]. 2018;28(102 ):147-157. Available from: https://sid.ir/paper/114723/en

IEEE: Copy

Mahboube Shirani, ALI AKBARI, Atefeh NejadKooraki, Alireza Goli, Behnza Azmoon, Nooshin Shirani, and SAEED HABIBOLLAHI, “Optimization of Extractive Desulfurization of Model Oil With a Novel Green Deep Eutectic Solvent Using Genetic Algorithm-Artificial Neural Network,” PETROLEUM RESEARCH, vol. 28, no. 102 , pp. 147–157, 2018, [Online]. Available: https://sid.ir/paper/114723/en

Related Journal Papers