gchange  > 影响、适应和脆弱性
DOI: 10.1016/j.jcou.2017.06.012
Scopus ID: 2-s2.0-85025091455
Title:
Predicting CO2 capture of ionic liquids using machine learning
Author: Venkatraman V.; Alsberg B.K.
Source Publication: Journal of CO2 Utilization
ISSN: 22129820
Indexed By: SCI-E ; EI ; Scopus
Publishing Year: 2017
Volume: 21
pages begin: 162
pages end: 168
Language: 英语
Keyword: CO2 capture ; Ionic liquids ; Machine learning ; QSPR
Scopus Keyword: Artificial intelligence ; Data mining ; Decision trees ; Education ; Ionic liquids ; Learning systems ; Liquids ; Positive ions ; Quantum chemistry ; Solubility ; CO2 absorption ; Mean absolute error ; Molecular descriptors ; Operating temperature ; QSPR ; Random forests ; Single decision ; Structure property relationships ; Carbon dioxide
Subject in Chinese: ; 吸收 ; 森林 ; 二氧化碳 ; 吸附作用 ; 离子
Subject: CARBON ; ABSORPTION ; FORESTS ; CARBON DIOXIDE ; IONS
English Abstract: Ionic liquid (IL) based CO2 capture is currently seen as a promising alternative to conventional amine-based solvents. While the possible combinations of cations and anions are numerous, it is time consuming and expensive to carry out experimental measurements for CO2 solubilities for each new IL. Therefore, as a means to rapidly screen suitable ILs as potential solvents for CO2 absorption, we investigate the use of machine learning (ML) based models to establish structure-property relationships between molecular structures of cations and anions and their CO2 solubilities. Over 10,000 IL-CO2 solubility data of 185 ILs measured at different operating temperatures and pressures were extracted from the literature. Using semi-empirically derived geometrical and charge-based molecular descriptors, good agreement with the available experimental measurements was obtained for both single decision tree (mean absolute error of 0.10) and ensemble random forest (mean absolute error of 0.04) approaches. The results were found to be more accurate than those obtained with the quantum chemistry based COSMOtherm predictions. © 2017 Elsevier Ltd.
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被引频次[WOS]:9   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.177/globalchange/handle/2HF3EXSE/52666
Appears in Collections:影响、适应和脆弱性

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Venkatraman V.,Alsberg B.K.. Predicting CO2 capture of ionic liquids using machine learning[J]. Journal of CO2 Utilization,2017-01-01,21
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