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

Document Type : Research Paper

Authors

1 Department of Chemistry, Faculty of Science, University of Jiroft, Iran

2 Department of Industrial Engineering, Engineering Faculty, Yazd University, Iran

3 R & D (Research and Development) Unit, Petrochemical Sepahan Company, Mobarekeh Industrial City, Isfahan, Iran

4 Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran

5 Department of Chemistry, Payame Noor University, Tehran, Iran

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.
 

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References

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Journal of Petroleum Research
Volume 28, 97-5 - Serial Number 102
January and February 2018
Pages 147-157

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