نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
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%.
کلیدواژهها English
