Although ionic liquids (ILs) have been widely explored as solvents for extractive desulfurization (EDS) of fuel oils, systematic studying of the optimal design of ILs for this process is still scarce. The UNIFAC‐IL model is extended first to describe the EDS system based on exhaustive experimental data. Then, based on the obtained UNIFAC‐IL model and group contribution models for predicting the melting point and viscosity of ILs, a mixed‐integer nonlinear programming (MINLP) problem is formulated for the purpose of computer‐aided ionic liquid design (CAILD). The MINLP problem is solved to optimize the liquid‐liquid extraction performance of ILs in a given multicomponent model EDS system, under consideration of constraints regarding the IL structure, thermodynamic and physical properties. The top five IL candidates preidentified from CAILD are further evaluated by means of process simulation using ASPEN Plus. Thereby, [C5MPy][C(CN)3] is identified as the most suitable solvent for EDS. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1013–1025, 2018

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For the selection of industrially suitable ionic liquids (ILs) as extraction solvents, a systematic method combining phase equilibrium calculation, physical property prediction, and process simulation is presented. The conductor-like screening model for real solvents is used to predict the liquid–liquid equilibria of the systems composed of the target mixture to be separated and different ILs at the specific global composition of interest, thereby prescreening ILs with higher mass-based distribution coefficient and selectivity as well as lower solvent loss. Group contribution methods are then employed to estimate the key physical properties of the prescreened ILs and further suggest candidates meeting certain physical property constraints. Afterward, the performance of the top IL candidates in a continuous process is analyzed by Aspen Plus to identify finally process-based optimal solvents. The proposed method is illustrated with an extractive desulfurization case study and two most promising ILs for this process are consequently determined.

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Considering that the predictive UNIFAC model is highly valuable for the solvent selection, process design and optimization of separation tasks, a large extension of this model to ionic liquid (IL)-solute systems is presented by combining experimental and COSMO-RS derived databases. The experimental infinite dilution activity coefficient (γ ∞) data of different solutes in ILs are first collected exhaustively to extend UNIFAC-IL to cover all involved IL and conventional functional groups. Afterwards, the experimental and COSMO-RS calculated γ ∞ are compared for different types of solutes to evaluate the potential of using COSMO-RS predictions as quasiexperimental data for further UNIFAC-IL extension. In the cases where COSMO-RS can provide quantitatively accurate predictions after calibration, additional γ ∞ database is specifically generated to regress more group interaction parameters in the UNIFAC-IL model. Finally, a large experimental liquid-liquid and vapor-liquid equilibria database is collected and employed to evaluate the predictive performance of the obtained γ ∞-based UNIFAC-IL model.

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