| DOI: | 10.1016/j.jcou.2017.06.012
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| Scopus记录号: | 2-s2.0-85025091455
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| 论文题名: | Predicting CO2 capture of ionic liquids using machine learning |
| 作者: | Venkatraman V.; Alsberg B.K.
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| 刊名: | Journal of CO2 Utilization
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| ISSN: | 22129820
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| 出版年: | 2017
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| 卷: | 21 | | 起始页码: | 162
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| 结束页码: | 168
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| 语种: | 英语
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| 英文关键词: | CO2 capture
; Ionic liquids
; Machine learning
; QSPR
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| Scopus关键词: | 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
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| 英文摘要: | 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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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/52666
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| Appears in Collections: | 影响、适应和脆弱性
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| Recommended Citation: | |
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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