Iranian Chemical Engineering Journal

Iranian Chemical Engineering Journal

Estimating NRTL Parameters via PSO Algorithm for Dibenzothiophene Extraction from Fuel Using Deep Eutectic Solvent

Document Type : Original Article

Authors
H. Kazemi Golbaghi 1
V. Mohebbi 2
1 PhD. Student of Chemical Engineering, Petroleum University of Technology
2 Associate Professor of Chemical Engineering, Petroleum University of Technology
10.22034/ijche.2025.501285.1492
Abstract
Separation of sulfur-containing compounds from fuel remains one of the major challenges in crude oil refining processes. Extractive desulfurization using deep eutectic solvents (DES) has gained the attention of researchers as an effective method for removing these compounds from fuel. In this study, binary interaction parameters of the Non-Random Two-Liquid (NRTL) model were estimated using equilibrium solubility data of dibenzothiophene in the model fuel and the eutectic solvent (choline chloride and diethylene glycol), applying the Particle Swarm Optimization (PSO) algorithm. Subsequently, the developed thermodynamic model was employed to predict the extractive desulfurization performance. Modeling results indicate that the separation efficiency increases at lower temperatures and with a higher solvent-to-fuel ratio. Specifically, at 20 °C, increasing
the solvent ratio from 0.1 to 3 led to an increase in DBT removal from 1.9% to 37%, while at 60 °C, the efficiency increased from 1.7% to 34%.
Keywords
Extractive Desulfurization
Deep Eutectic Solvent
Solid-Liquid Equilibria
Thermodynamic Modeling
Subjects
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Iranian Chemical Engineering Journal
Volume 25, Issue 145 - Serial Number 145
July and August 2026
Pages 41-52