Screening of ionic liquids as green entrainers for ethanol water separation by extractive distillation: COSMO-RS prediction and aspen plus simulation

离子液体 萃取蒸馏 乙二醇 四甲基铵 化学 工艺工程 过程模拟 蒸馏 化学工程 过程(计算) 有机化学 计算机科学 催化作用 离子 操作系统 工程类
作者
Huzaifa Malik,Huma Warsi Khan,Mansoor Ul Hassan Shah,Muhammad Imran Ahmad,Iqra Khan,Abdullah A. Al‐Kahtani,Mika Sillanpää
出处
期刊:Chemosphere [Elsevier]
卷期号:311: 136901-136901 被引量:51
标识
DOI:10.1016/j.chemosphere.2022.136901
摘要

Ionic liquids (ILs) have been demonstrated as promising alternatives to conventional entrainers in separation of azeotropic mixtures mostly investigating phase equilibrium and process design scenarios. However, proper selection of ILs for a specific task always remains challenging. Hence a simulation tool, i.e. conductor like screening model for real solvents (COSMO-RS) was applied to address this challenge. Furthermore, screened ILs were simulated as entrainers for ethanol water separation by extractive distillation. The current study also aims to demonstrate a systematic approach to retrofit existing processes, by employing ILs as green entrainers. Screening of twenty-five (25) ILs was carried out using COSMO-RS to select suitable ILs as green entrainers based on activity coefficient, capacity and selectivity. Results illustrated that tetramethylammonium chloride ([TMAm][Cl]) due to its strong hydrogen bonding ability was found to be the best ILs entrainer. Moreover, in order to reduce the operating costs without compromising desired product purity (ethanol purity ≥99.5% in top product), the selected ILs (8 kg/h) in a mixture with ethylene glycol (72 kg/h) were simulated using Aspen plus v.11. The simulation results revealed that by combining tetramethylammonium chloride (2 kg/h) with ethylene glycol (78 kg/h) reduced 7.26 tons of CO2 emissions/year through heat integration by saving 1.49*108 kJ/year energy besides minimizing operating costs. In conclusion, the systematic selection of ILs as green entrainers in combination with ethylene glycol and then the appropriate simulation of the whole system will ultimately reduce the cost of the separation process and reduce the emission of greenhouse gases as well utilization of toxic conventional entrainers.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Luna_aaa应助B站萧亚轩采纳,获得50
刚刚
刚刚
王赟赟发布了新的文献求助10
1秒前
希望天下0贩的0应助Zxj采纳,获得10
1秒前
orixero应助WeOne采纳,获得10
2秒前
沈子杰发布了新的文献求助10
2秒前
2秒前
六六驳回了乐乐应助
2秒前
完美世界应助辛未采纳,获得10
2秒前
四观人完成签到,获得积分10
2秒前
2秒前
懒洋洋完成签到 ,获得积分10
2秒前
慕青应助克里斯蒂龙采纳,获得10
3秒前
3秒前
朴素的代芹完成签到,获得积分10
3秒前
大个应助聪明水之采纳,获得10
3秒前
乐观的皮卡丘完成签到,获得积分10
4秒前
自信的藏花完成签到,获得积分10
4秒前
六六关注了科研通微信公众号
5秒前
jingwen完成签到,获得积分10
5秒前
5秒前
缓慢的败完成签到,获得积分10
5秒前
量子星尘发布了新的文献求助10
6秒前
nqterysc完成签到,获得积分10
6秒前
彭于晏应助lan采纳,获得10
6秒前
标致香发布了新的文献求助10
7秒前
Udo完成签到,获得积分10
7秒前
SciGPT应助瘦瘦怜阳采纳,获得10
8秒前
8秒前
雅琳完成签到,获得积分10
8秒前
8秒前
9秒前
隐形曼青应助夏夏采纳,获得10
9秒前
美好斓发布了新的文献求助10
10秒前
yyer发布了新的文献求助10
10秒前
赘婿应助wjw采纳,获得10
10秒前
orixero应助moomomomomo采纳,获得20
10秒前
汉堡包应助自由马儿采纳,获得10
10秒前
夜願发布了新的文献求助10
11秒前
早睡早起身体棒完成签到,获得积分10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Binary Alloy Phase Diagrams, 2nd Edition 8000
Encyclopedia of Reproduction Third Edition 3000
Comprehensive Methanol Science Production, Applications, and Emerging Technologies 2000
From Victimization to Aggression 1000
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Exosomes Pipeline Insight, 2025 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5652297
求助须知:如何正确求助?哪些是违规求助? 4787231
关于积分的说明 15059377
捐赠科研通 4810953
什么是DOI,文献DOI怎么找? 2573500
邀请新用户注册赠送积分活动 1529327
关于科研通互助平台的介绍 1488227