文献类型：期刊文献

英文题名：Computer-aided design and process evaluation of ionic liquids for n-hexane-methylcyclopentane extractive distillation

作者：Chao, He[1];Song, Zhen[1];Cheng, Hongye[1];Chen, Lifang[1];Qi, Zhiwen[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, Max Planck Partner Grp, State Key Lab Chem Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2018

卷号：196

起止页码：157

外文期刊名：SEPARATION AND PURIFICATION TECHNOLOGY

收录：;EI(收录号：20172803903746);WOS:【SCI-EXPANDED(收录号:WOS:000427211200020)】；

基金：This research is supported by the National Natural Science Foundation of China (NSFC U1462123, 21407684 and 21576081), PetroChina Innovation Foundation, Fundamental Research Funds for the Central Universities of China, and 111 Project (B08021).

语种：英文

外文关键词：Ionic liquid design; N-hexane-methylcyclopentane; Extractive distillation; MINLP-CAMD; Process simulation

摘要：Ionic liquids (ILs) are highly attractive entrainers for separating close-boiling alkane-cycloalkane mixtures in extractive distillation processes while studies on this particular topic are still scarce. This work employs computer-aided molecular design (CAMD) and process simulation to identify suitable ILs for the extractive distillation of n-hexane-methylcyclopentane, a representative alkane-cycloalkane mixture. A large number of experimental data on the infinite dilution activity coefficient of methylcyclopentane and vapor-liquid equilibria of relevant systems are collected from literature to validate the reliability of the employed UNIFAC-IL model, which is the basis of CAMD and process simulation. Combining the UNIFAC-IL model and two group contribution models of IL physical properties, a mixed integer nonlinear programming (MINLP) problem is then formulated for the CAMD of ILs. The top IL candidates pre-identified from MINLP-CAMD are further introduced into Aspen Plus for process simulation and evaluation. By comparing their process performances with the benchmark solvent N-methyl-2-pyrrolidinone, 1-nonyl-imidazolium thiocyanate ([C(9)Im][SCN]) is consequently identified as the optimal entrainer. (C) 2017 Elsevier B.V. All rights reserved.