Cheminformatics Modeling of Amine Solutions for Assessing their CO2 Absorption Properties

As stricter regulations on CO2 emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO2 is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO2 eff...

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Bibliographic Details
Published inMolecular informatics Vol. 36; no. 7
Main Authors Kuenemann, Melaine A., Fourches, Denis
Format Journal Article
LanguageEnglish
Published Weinheim Wiley Subscription Services, Inc 01.07.2017
Subjects
Absorption
amine scrubbing
Amines
Carbon dioxide
Carbon dioxide emissions
Carbon sequestration
Chemical analysis
Chemical composition
Clustering
CO2 recycling
Informatics
Learning algorithms
Machine learning
Mathematical models
Modelling
molecular modeling
Neural networks
Properties (attributes)
QSPR
Washing
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Summary:As stricter regulations on CO2 emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO2 is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO2 efficiently. Tertiary amines characterized by low heats of reaction are considered good candidates but their absorption properties can significantly differ from one analogue to another despite high structural similarity. Herein, after collecting and curating experimental data from the literature, we have built a modeling set of 41 amine structures with their absorption properties. Then we analyzed their chemical composition using molecular descriptors and non‐supervised clustering. Furthermore, we developed a series of quantitative structure‐property relationships (QSPR) to assess amines’ CO2 absorption properties from their structural characteristics. These models afforded reasonable prediction performances (e. g., Q2LOO=0.63 for CO2 absorption amount) even though they are solely based on 2D chemical descriptors and individual machine learning techniques (random forest and neural network). Overall, we believe the chemical analysis and the series of QSPR models presented in this proof‐of‐concept study represent new knowledge and innovative tools that could be very useful for screening and prioritizing hypothetical amines to be synthesized and tested experimentally for their CO2 absorption properties.
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ISSN:1868-1743
1868-1751
DOI:10.1002/minf.201600143