离子液体
化学
分离过程
过程(计算)
比例(比率)
分离(统计)
萃取(化学)
工艺工程
放大
离子键合
材料科学
化学工程
计算机科学
有机化学
色谱法
工程类
离子
催化作用
机器学习
量子力学
经典力学
操作系统
物理
作者
Xiaobo Tian,Xiangping Zhang,Lü Wei,Shaojuan Zeng,Lei Huang,Suojiang Zhang
出处
期刊:Green Chemistry
[The Royal Society of Chemistry]
日期:2010-01-01
卷期号:12 (7): 1263-1263
被引量:34
摘要
A multi-scale simulation method is proposed to enable screening of ionic liquids (ILs) as entrainers in extractive distillation. The 1,3-butadiene production process with acetonitrile (ACN) was chosen as a research case to validate the feasibility of the methodology. Ab initio calculations were first carried out to further understand the influence of ionic liquids on the selectivity of ACN and the solubility of C4 fractions in [CnMIM][PF6](n = 2–8), [C2MIM][X] (X = BF4−, Cl−, PF6−, Br−), by investigating the microstructure and intermolecular interaction in the mixture of C4 fractions and several selected ionic liquids. It was found that the selectivity of the ionic liquid is determined by both its polarity and hydrogen-bonding ability. Based on the analysis, a suitable ionic liquid was chosen. With the ab initio calculation, a priori prediction of thermophysical data of the IL-containing system was performed with COSMO-RS. The calculation revealed that the selectivity of the extractive solvent was increased by an average of 3.64% after adding [C2MIM][PF6]. With above calculations, an improved ACN extraction distillation process using ILs as an entrainer was proposed, and a configuration for the new process was constructed. Based on the established thermodynamic models which have considered the properties from the molecular structure of ILs, process simulation was performed to obtain the process parameters which are important for the new process design. The simulation results indicated that the temperatures at the bottom of the extractive distillation column with the ionic liquid as an additive are lowered by an average of 3.1 °C, which is significant for inhibition of polymerization. We show that the ACN consumption using this process can be lowered by 24%, and the energy consumption can likewise be lowered by 6.62%.
科研通智能强力驱动
Strongly Powered by AbleSci AI