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

Majedeh Gheytanzadeh,Alireza Baghban,Ahmad Mohaddespour,Amin Esmaeili,Muhammad Tajammal Munir,Otman Abida,Sajjad Habibzadeh

doi:10.1038/s41598-021-95246-6

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.

Highlights

Abbreviations adaptive neuro-fuzzy inference system (ANFIS) Adaptive neuro fuzzy inference system artificial neural network (ANN) Artificial neural network BDP 1,4-Benzenedipyrazolate beryllium benzene tribenzoate (Be-BTB) Beryllium benzene tribenzoate BTC Benzene-1,3,5-tricarboxylate BTTri 1,3,5-Benzenetristriazolate GPR Gaussian process regression H Hat matrix H* Critical leverage limit In Unit array k Covariance function
The sensitivity analysis indicates that the pressure and the surface area of Metal–organic frameworks (MOFs) with r values of 0.68 and 0.52 are the most influential input variables on the C O2 adsorption estimation
The GPR models based on different kernel functions have been established to estimate the

Summary

Introduction

Abbreviations ANFIS Adaptive neuro fuzzy inference system ANN Artificial neural network BDP 1,4-Benzenedipyrazolate Be-BTB Beryllium benzene tribenzoate BTC Benzene-1,3,5-tricarboxylate BTTri 1,3,5-Benzenetristriazolate GPR Gaussian process regression H Hat matrix H* Critical leverage limit In Unit array k Covariance (kernel) function. Several approaches have been studied for C O2 capture: m embranes[5,6], chemical a bsorption[7,8], physical a dsorption[9], and fluidized bed technologies[10]. These methods suffer from some drawbacks, such as high energy consumption, complex regeneration processes, and low CO2 capture capacity. In order to build up a long-lasting chance in CO2 elimination, an appropriate adsorption medium should provide the following conditions: (1) a periodical structure for both the capture and release of CO2 reversibly, (2) high CO2 selectivity, (3) optimized CO2 adsorption capacities through modifying by chemical functionalization, and (4) thermal, chemical, and mechanical stabilities[11,12]. Metal–organic frameworks (MOFs) have been one of the most applicable porous compounds due to their regulating chemical structure, adjustable chemical functionality, and high thermal stability, allowing potential applications in gas adsorption[13,14,15,16]

Methods

Results

Conclusion