Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

Abstract 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 CO2 in...

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Autores principales:	Majedeh Gheytanzadeh, Alireza Baghban, Sajjad Habibzadeh, Amin Esmaeili, Otman Abida, Ahmad Mohaddespour, Muhammad Tajammal Munir
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a88e0f0a55c4400bb4507ea11db6ad9d
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spelling	oai:doaj.org-article:a88e0f0a55c4400bb4507ea11db6ad9d2021-12-02T17:06:32ZTowards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach10.1038/s41598-021-95246-62045-2322https://doaj.org/article/a88e0f0a55c4400bb4507ea11db6ad9d2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95246-6https://doaj.org/toc/2045-2322Abstract 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 CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 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 CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 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 R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.Majedeh GheytanzadehAlireza BaghbanSajjad HabibzadehAmin EsmaeiliOtman AbidaAhmad MohaddespourMuhammad Tajammal MunirNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Majedeh Gheytanzadeh Alireza Baghban Sajjad Habibzadeh Amin Esmaeili Otman Abida Ahmad Mohaddespour Muhammad Tajammal Munir Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
description	Abstract 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 CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 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 CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 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 R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.
format	article
author	Majedeh Gheytanzadeh Alireza Baghban Sajjad Habibzadeh Amin Esmaeili Otman Abida Ahmad Mohaddespour Muhammad Tajammal Munir
author_facet	Majedeh Gheytanzadeh Alireza Baghban Sajjad Habibzadeh Amin Esmaeili Otman Abida Ahmad Mohaddespour Muhammad Tajammal Munir
author_sort	Majedeh Gheytanzadeh
title	Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
title_short	Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
title_full	Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
title_fullStr	Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
title_full_unstemmed	Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
title_sort	towards estimation of co2 adsorption on highly porous mof-based adsorbents using gaussian process regression approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a88e0f0a55c4400bb4507ea11db6ad9d
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Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

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