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

journal contribution
submitted on 2024-01-22, 09:23 and posted on 2024-01-23, 07:21 authored by Majedeh Gheytanzadeh, Alireza Baghban, Sajjad Habibzadeh, Amin Esmaeili, Otman Abida, Ahmad Mohaddespour, Muhammad Tajammal Munir

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.

Other Information

Published in: Scientific Reports
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  • English


Springer Nature

Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Engineering and Technology - UDST
  • College of the North Atlantic - Qatar (2002-2022)
  • School of Engineering Technology and Industrial Trades - CNA-Q (2002-2022)