Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
<p dir="ltr">In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing hi...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , |
| Published: |
2021
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513529393971200 |
|---|---|
| author | Majedeh Gheytanzadeh (17541927) |
| author2 | Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Amin Esmaeili (17541204) Otman Abida (2071714) Ahmad Mohaddespour (17541948) Muhammad Tajammal Munir (17541933) |
| author2_role | author author author author author author |
| author_facet | Majedeh Gheytanzadeh (17541927) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Amin Esmaeili (17541204) Otman Abida (2071714) Ahmad Mohaddespour (17541948) Muhammad Tajammal Munir (17541933) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majedeh Gheytanzadeh (17541927) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Amin Esmaeili (17541204) Otman Abida (2071714) Ahmad Mohaddespour (17541948) Muhammad Tajammal Munir (17541933) |
| dc.date.none.fl_str_mv | 2021-08-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-021-95246-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Towards_estimation_of_CO_sub_2_sub_adsorption_on_highly_porous_MOF-based_adsorbents_using_gaussian_process_regression_approach/24717612 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Theoretical and computational chemistry Engineering Chemical engineering Environmental sciences Climate change impacts and adaptation Pollution and contamination CO2 adsorption MOF-based greenhouse gas |
| dc.title.none.fl_str_mv | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO<sub>2</sub> into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO<sub>2</sub>. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO<sub>2</sub> adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO<sub>2</sub> adsorption will open new doors for their further application in CO<sub>2</sub> separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO<sub>2</sub> adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO<sub>2</sub> uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R<sup>2</sup> value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO<sub>2</sub> adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO<sub>2</sub> adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-021-95246-6" target="_blank">https://dx.doi.org/10.1038/s41598-021-95246-6</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_729a68ee5f87051a02bd4c7534f0782d |
| identifier_str_mv | 10.1038/s41598-021-95246-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717612 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approachMajedeh Gheytanzadeh (17541927)Alireza Baghban (5159648)Sajjad Habibzadeh (5548580)Amin Esmaeili (17541204)Otman Abida (2071714)Ahmad Mohaddespour (17541948)Muhammad Tajammal Munir (17541933)Chemical sciencesTheoretical and computational chemistryEngineeringChemical engineeringEnvironmental sciencesClimate change impacts and adaptationPollution and contaminationCO2 adsorptionMOF-basedgreenhouse gas<p dir="ltr">In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO<sub>2</sub> into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO<sub>2</sub>. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO<sub>2</sub> adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO<sub>2</sub> adsorption will open new doors for their further application in CO<sub>2</sub> separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO<sub>2</sub> adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO<sub>2</sub> uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R<sup>2</sup> value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO<sub>2</sub> adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO<sub>2</sub> adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-021-95246-6" target="_blank">https://dx.doi.org/10.1038/s41598-021-95246-6</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2021-08-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-021-95246-6https://figshare.com/articles/journal_contribution/Towards_estimation_of_CO_sub_2_sub_adsorption_on_highly_porous_MOF-based_adsorbents_using_gaussian_process_regression_approach/24717612CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247176122021-08-03T03:00:00Z |
| spellingShingle | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach Majedeh Gheytanzadeh (17541927) Chemical sciences Theoretical and computational chemistry Engineering Chemical engineering Environmental sciences Climate change impacts and adaptation Pollution and contamination CO2 adsorption MOF-based greenhouse gas |
| status_str | publishedVersion |
| title | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| title_full | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| title_fullStr | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| title_full_unstemmed | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| title_short | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| title_sort | Towards estimation of CO<sub>2</sub> adsorption on highly porous MOF-based adsorbents using gaussian process regression approach |
| topic | Chemical sciences Theoretical and computational chemistry Engineering Chemical engineering Environmental sciences Climate change impacts and adaptation Pollution and contamination CO2 adsorption MOF-based greenhouse gas |