Supplementary Files
Keywords
Ionic liquids
Aromatic nitrogen compounds
Diesel
COSMO-RS
How to Cite
Abstract
The removal of nitrogen-containing aromatic compounds from diesel is critical for meeting stringent fuel quality and emissions standards. Conventional hydrodenitrogenation (HDN) has limitations in removing these contaminants, which prompted the exploration of alternative separation methods. This study focuses on the extractive denitrogenation of model diesel using ionic liquids (ILs), screened computationally via the Conductor-like Screening Model for Real Solvents (COSMO-RS). Eight ILs were initially evaluated for their ability to extract the non-basic aromatic nitrogen compound, pyrrole from a model diesel system consisting of hexadecane. Screening results identified 1-ethyl-3-methyl-imidazolium dicyanamide and 1-ethyl-3-methyl-pyridinium bromide as the most effective ILs based on selectivity and capacity respectively. These ILs were subsequently evaluated for the extraction of the basic nitrogen compound, pyridine. The performance of the selected ILs in extracting pyrrole and pyridine was compared with that of the conventional organic solvent dimethylformamide (DMF) using selectivity and capacity as key metrics. These results demonstrate that both ILs outperform DMF with its respective metrics. A final evaluation was conducted by analyzing the ternary liquid–liquid equilibrium (LLE) system. Ternary LLE analysis revealed negligible hexadecane content in the IL extract phase which implied minimal cross-contamination. In contrast, the DMF system showed significant solvent presence in the hexadecane-rich phase which confirmed notable cross-contamination. Overall, this work demonstrates the superiority of ILs over organic solvents such as DMF for extractive denitrogenation which supports a promising alternative for cleaner fuel production. The present study completes the computational screening of promising ILs using a highly reliable tool which could guide subsequent experimental works for users with limited computational resources.
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