Extractive Distillation with Ionic Liquids To Separate Benzene, Toluene, and Xylene from Pyrolysis Gasoline: Process Design and Techno-Economic Comparison with the Morphylane Process

Aromatic/aliphatic separation stands as a challenge for both industry and academia. More and more efforts are being made to improve energy-demanding technologies based on liquid–liquid extraction or extractive distillation processes. Recently, ionic liquid-based technologies devoted to separating benzene, toluene, and xylene from pyrolysis gasoline have been evaluated, and extractive distillation showed more potential than liquid–liquid extraction in terms of separation performance and global energy requirements. In this work, extractive distillation with ionic liquids is completely evaluated from solvent selection to rate-based process design and compared with the Morphylane benchmark process. The ILUAM database is explored through a validated COSMO/Aspen methodology to understand the impact of the ionic liquid nature on the extractive distillation operation. A parametric study focused on the extractive distillation column (EDC) is conducted for preliminary set initial guesses to design task. The final issue is centered on rigorously designing the ionic liquid-based and Morphylane processes at commercial specifications. Two different ionic liquid-based process configurations are evaluated based on the opportunities that the use of ionic liquids enables. The new process configuration working with [emim]­[TCM] reduces the energy costs and capital expenditures associated with the Morphylane process by 67 and 63%, respectively, along with a reduction in the solvent costs, confirming it as a cleaner alternative. In addition, a parametrization of the Cubic Plus Association equation of state (CPA EoS) obtained from the regression of experimental vapor–liquid–liquid equilibrium data is also used to simulate the EDC in equilibrium and rate-based mode. Both models provide similar results, confirming the ability of the conductor-like screening model–segment activity coefficient model as an a priori tool and the reliability of the CPA EoS as a regressive alternative to describe these kinds of complex multicomponent systems.