COSMO-based models

• 作 者: Chengna Dai Zhiwen Qi Zhigang Lei Jose Palomar
• 机 构: Beijing University of Technology,China;East China University of Science and Technology,China;Shihezi
• 出 处: 绿色能源与环境（英文版）
• 发表日期: 2021年3期

In many separation processes (e.g.,extractive distillation,absorption,liquid-liquid extraction,etc.),a third solvent(namely separating agent or entrainer) is commonly needed to add into the mixtures to be separated so as to improve the separation factor.Thus,the selection of a suitable separating agent is the key to ensuring an effective and economical separation processes.However,it is tedious and time-con-suming to choose the best separating agent from thousands of different substances for a given separation task.More spe-cially,ionic liquids (ILs) as innovative fluids have recently received wide attention in separation processes by virtue of their strong salting effect and non-volatility.However,there are still numerous combinations of cations and anions in ILs(up to 1018!).Thus,in this case,the predictive molecular thermodynamic models like COSMO (conductor-like screen-ing model)-based models are indispensable to identify the structure-property relation,rapidly screen out the suitable separating agent,and greatly decrease the amount of exper-imental work.Evidently,the COSMO-based models provide the scientific fundamental of chemical separation processes.