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Theoretical Derivation of a Prediction Model for CO(2) Adsorption by Coal
[Image: see text] Adsorption characteristics of CO(2) by coal are an important reservoir parameter to determine the CO(2) storage capacity of the coal seam. The Langmuir isotherm adsorption model is commonly used to describe the isothermal adsorption line of coal. However, we cannot predict the CO(2...
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/PMC8158826/ https://www.ncbi.nlm.nih.gov/pubmed/34056475 http://dx.doi.org/10.1021/acsomega.1c00957 |
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author | Hao, Jianchi Wen, Hu Ma, Li Fei, Jinbiao Ren, Lifeng |
author_facet | Hao, Jianchi Wen, Hu Ma, Li Fei, Jinbiao Ren, Lifeng |
author_sort | Hao, Jianchi |
collection | PubMed |
description | [Image: see text] Adsorption characteristics of CO(2) by coal are an important reservoir parameter to determine the CO(2) storage capacity of the coal seam. The Langmuir isotherm adsorption model is commonly used to describe the isothermal adsorption line of coal. However, we cannot predict the CO(2) adsorption capacity at other temperatures by using the Langmuir model based on the experimental data at a fixed temperature. This paper analyzes the ε–V(ad) adsorption characteristic curves of three coal samples over a range of temperatures and pressures. The study demonstrates that the adsorption characteristic curves of CO(2) gas are independent of temperature and depend mainly on the dispersion force between coal and the CO(2) molecules. In addition, the adsorption potential of CO(2) gas has a negative correlation with the volume of the adsorbed phase. Hence, the CO(2) adsorption characteristic curve of coal conforms to the logarithmic function. Based on the adsorption potential theory, the prediction model of CO(2) adsorption by coal is derived. The deviation analysis from measured data shows that the average relative deviation of the three coal samples is ∼5%, and the prediction results are accurate and reliable. Under different temperature and pressure conditions of the three coal samples, the results from the prediction model of CO(2) adsorption by coal and the Langmuir model have a strong correlation with the experimental results. In comparison with the Langmuir model, the prediction model of CO(2) adsorption by coal can predict the adsorption capacity under different temperature and pressure conditions. Hence, it has a wide range of applications when compared to that of the Langmuir model. In practical applications, better results are achieved with a significant reduction in experimental time and labor. |
format | Online Article Text |
id | pubmed-8158826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-81588262021-05-28 Theoretical Derivation of a Prediction Model for CO(2) Adsorption by Coal Hao, Jianchi Wen, Hu Ma, Li Fei, Jinbiao Ren, Lifeng ACS Omega [Image: see text] Adsorption characteristics of CO(2) by coal are an important reservoir parameter to determine the CO(2) storage capacity of the coal seam. The Langmuir isotherm adsorption model is commonly used to describe the isothermal adsorption line of coal. However, we cannot predict the CO(2) adsorption capacity at other temperatures by using the Langmuir model based on the experimental data at a fixed temperature. This paper analyzes the ε–V(ad) adsorption characteristic curves of three coal samples over a range of temperatures and pressures. The study demonstrates that the adsorption characteristic curves of CO(2) gas are independent of temperature and depend mainly on the dispersion force between coal and the CO(2) molecules. In addition, the adsorption potential of CO(2) gas has a negative correlation with the volume of the adsorbed phase. Hence, the CO(2) adsorption characteristic curve of coal conforms to the logarithmic function. Based on the adsorption potential theory, the prediction model of CO(2) adsorption by coal is derived. The deviation analysis from measured data shows that the average relative deviation of the three coal samples is ∼5%, and the prediction results are accurate and reliable. Under different temperature and pressure conditions of the three coal samples, the results from the prediction model of CO(2) adsorption by coal and the Langmuir model have a strong correlation with the experimental results. In comparison with the Langmuir model, the prediction model of CO(2) adsorption by coal can predict the adsorption capacity under different temperature and pressure conditions. Hence, it has a wide range of applications when compared to that of the Langmuir model. In practical applications, better results are achieved with a significant reduction in experimental time and labor. American Chemical Society 2021-05-17 /pmc/articles/PMC8158826/ /pubmed/34056475 http://dx.doi.org/10.1021/acsomega.1c00957 Text en © 2021 The Authors. Published by American Chemical Society Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Hao, Jianchi Wen, Hu Ma, Li Fei, Jinbiao Ren, Lifeng Theoretical Derivation of a Prediction Model for CO(2) Adsorption by Coal |
title | Theoretical Derivation of a Prediction Model for CO(2) Adsorption
by Coal |
title_full | Theoretical Derivation of a Prediction Model for CO(2) Adsorption
by Coal |
title_fullStr | Theoretical Derivation of a Prediction Model for CO(2) Adsorption
by Coal |
title_full_unstemmed | Theoretical Derivation of a Prediction Model for CO(2) Adsorption
by Coal |
title_short | Theoretical Derivation of a Prediction Model for CO(2) Adsorption
by Coal |
title_sort | theoretical derivation of a prediction model for co(2) adsorption
by coal |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158826/ https://www.ncbi.nlm.nih.gov/pubmed/34056475 http://dx.doi.org/10.1021/acsomega.1c00957 |
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