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Application of Chemometrics for Coal Pyrolysis Products by Online py-GC×GC–MS
[Image: see text] Investigations on the molecular composition of coal pyrolysis products can help us to improve nonfuel utilization of coal. Meanwhile, the molecular composition of coal pyrolysis products is also influenced by the characteristics and depositional environment of coal. However, due to...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876837/ https://www.ncbi.nlm.nih.gov/pubmed/33585755 http://dx.doi.org/10.1021/acsomega.0c05359 |
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author | Yin, Hao Lu, Jie Liu, Guijian Niu, Zhiyuan Zha, Xiangping Wu, Dun Feng, Airong Hu, Yanyun |
author_facet | Yin, Hao Lu, Jie Liu, Guijian Niu, Zhiyuan Zha, Xiangping Wu, Dun Feng, Airong Hu, Yanyun |
author_sort | Yin, Hao |
collection | PubMed |
description | [Image: see text] Investigations on the molecular composition of coal pyrolysis products can help us to improve nonfuel utilization of coal. Meanwhile, the molecular composition of coal pyrolysis products is also influenced by the characteristics and depositional environment of coal. However, due to the extremely complex nature of coal, direct investigation of the molecular composition of coal pyrolysis products is still a challenge. In the present work, the data of the molecular composition of bituminous coal pyrolysis products are obtained by online pyrolysis coupled to comprehensive two-dimensional gas chromatography and mass spectrometry (online py-GC×GC–MS) and are divided into nine molecular groups depending on the aromaticity of the pyrolysis products and separating power of the GC×GC–MS. Chemometric tools, hierarchical cluster analysis, and principal component analysis are employed to reveal the correlations among the molecular composition of coal pyrolysis products and coal characteristics. The results show that the nine molecular groups of bituminous coal pyrolysis products can be divided into two clusters, the “aromatic group” and the “aliphatic group”, and that the eight coals are divided into three clusters, all of which can be interpreted by the depositional environments and δ(13)C(VPDB) values of coals. Moreover, a simple and empirical equation for estimation of coal tar from hydropyrolysis can be obtained depending on the chemometric results of the molecular composition of the coal pyrolysis products. By application of chemometrics, the molecular composition of coal pyrolysis products can provide preference to industrial utilization of coal. |
format | Online Article Text |
id | pubmed-7876837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-78768372021-02-12 Application of Chemometrics for Coal Pyrolysis Products by Online py-GC×GC–MS Yin, Hao Lu, Jie Liu, Guijian Niu, Zhiyuan Zha, Xiangping Wu, Dun Feng, Airong Hu, Yanyun ACS Omega [Image: see text] Investigations on the molecular composition of coal pyrolysis products can help us to improve nonfuel utilization of coal. Meanwhile, the molecular composition of coal pyrolysis products is also influenced by the characteristics and depositional environment of coal. However, due to the extremely complex nature of coal, direct investigation of the molecular composition of coal pyrolysis products is still a challenge. In the present work, the data of the molecular composition of bituminous coal pyrolysis products are obtained by online pyrolysis coupled to comprehensive two-dimensional gas chromatography and mass spectrometry (online py-GC×GC–MS) and are divided into nine molecular groups depending on the aromaticity of the pyrolysis products and separating power of the GC×GC–MS. Chemometric tools, hierarchical cluster analysis, and principal component analysis are employed to reveal the correlations among the molecular composition of coal pyrolysis products and coal characteristics. The results show that the nine molecular groups of bituminous coal pyrolysis products can be divided into two clusters, the “aromatic group” and the “aliphatic group”, and that the eight coals are divided into three clusters, all of which can be interpreted by the depositional environments and δ(13)C(VPDB) values of coals. Moreover, a simple and empirical equation for estimation of coal tar from hydropyrolysis can be obtained depending on the chemometric results of the molecular composition of the coal pyrolysis products. By application of chemometrics, the molecular composition of coal pyrolysis products can provide preference to industrial utilization of coal. American Chemical Society 2021-01-29 /pmc/articles/PMC7876837/ /pubmed/33585755 http://dx.doi.org/10.1021/acsomega.0c05359 Text en © 2021 The Authors. Published by American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. |
spellingShingle | Yin, Hao Lu, Jie Liu, Guijian Niu, Zhiyuan Zha, Xiangping Wu, Dun Feng, Airong Hu, Yanyun Application of Chemometrics for Coal Pyrolysis Products by Online py-GC×GC–MS |
title | Application of Chemometrics for Coal
Pyrolysis Products by Online py-GC×GC–MS |
title_full | Application of Chemometrics for Coal
Pyrolysis Products by Online py-GC×GC–MS |
title_fullStr | Application of Chemometrics for Coal
Pyrolysis Products by Online py-GC×GC–MS |
title_full_unstemmed | Application of Chemometrics for Coal
Pyrolysis Products by Online py-GC×GC–MS |
title_short | Application of Chemometrics for Coal
Pyrolysis Products by Online py-GC×GC–MS |
title_sort | application of chemometrics for coal
pyrolysis products by online py-gc×gc–ms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876837/ https://www.ncbi.nlm.nih.gov/pubmed/33585755 http://dx.doi.org/10.1021/acsomega.0c05359 |
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