摘要
A computer-aided ionic liquid design(CAILD) study is presented for the frequently encountered alkane/cycloalkane separations in petrochemical industry. Exhaustive experimental data are first collected to extend the UNIFAC-IL model for this system, where the proximity effect in alkanes and cycloalkanes is considered specifically by defining distinct groups. The thermodynamic performances of a large number of ILs for 4 different alkane/cycloalkane systems are then compared to select a representative example of such separations. By applying n-heptane/methylcyclohexane extractive distillation as a case study, the CAILD task is cast as a mixed-integer nonlinear programming(MINLP) problem based on the obtained task-specific UNIFAC-IL model and two semi-empirical models for IL physical properties. The top 5 IL candidates determined by solving the MINLP problem are subsequently introduced into Aspen Plus for process simulation and economic analysis, which finally identify 1-hexadecyl-methylpiperidinium tricyanomethane([C_(16)MPip][C(CN)_3]) as the best entrainer for this separation.
A computer-aided ionic liquid design(CAILD) study is presented for the frequently encountered alkane/cycloalkane separations in petrochemical industry. Exhaustive experimental data are first collected to extend the UNIFAC-IL model for this system, where the proximity effect in alkanes and cycloalkanes is considered specifically by defining distinct groups. The thermodynamic performances of a large number of ILs for 4 different alkane/cycloalkane systems are then compared to select a representative example of such separations. By applying n-heptane/methylcyclohexane extractive distillation as a case study, the CAILD task is cast as a mixed-integer nonlinear programming(MINLP) problem based on the obtained task-specific UNIFAC-IL model and two semi-empirical models for IL physical properties. The top 5 IL candidates determined by solving the MINLP problem are subsequently introduced into Aspen Plus for process simulation and economic analysis, which finally identify 1-hexadecyl-methylpiperidinium tricyanomethane([C_(16)MPip][C(CN)_3]) as the best entrainer for this separation.
基金
financial support from National Natural Science Foundation of China(21776074,21576081,and21861132019)
