Adsorption of CO2 and SO2 on multi-walled carbon nanotubes: experimental data and modeling using artificial neural network

Document Type : Research Article


1 Department of Chemical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

2 Chemical Engineering Department, Faculty of Chemical, Oil and Gas Engineering, Shiraz University, Shiraz, Iran


Multi-walled carbon nanotubes (MWCNTs) containing hydroxylgroups (OH-MWCNT) were modified by functionalization with 3-[2-(2-aminoethylamino)ethylamino]propyl trimethoxysilane (TRI). Adsorption isotherms of pure CO2 and SO2 on the pristine MWCNT, OH-MWCNT, and amine functionalized MWCNT (amine-MWCNT) were measured at two temperatures of 313.2 K and 323.2 K and pressures up to 2.1 bar by a static volumetric method. Capacities of all three types of adsorbents for CO2 adsorption are greater than those of CO2. The performance of amine-MWCNT in adsorpting CO2 is higher than the other two adsorbents. The average saturated capacity of amine-MWCNT for adsorption of pure CO2 at 313.2 K are about 38.6% and 20.8% higher than OH-MWCNT and pristine-MWCNT, respectively. Corresponding values for adsorption of pure CO2 are about 51.3% and 89.65%. Also, the equilibrium adsorption capacity of pristine MWCNT and amine-MWCNT for mixtures for CO2, nitrogen, and water vapor at 299.2 K was obtained. The equilibrium adsorption of CO2 increases as the water content increases in the presence of diluting gas (nitrogen). Freundlich and Langmuir equations were fitted on experimental adsorption isotherms. The Freundlich equation predicts experimental data better than the Langmuir equation. A multi-layer perceptron artificial neural network (ANN) model has been also proposed for predicting adsorption experimental data. The average and maximum difference between experimental data and values predicted by ANN model are about 3% and 24%, respectively.

Graphical Abstract

Adsorption of CO2 and SO2 on multi-walled carbon nanotubes: experimental data and modeling using artificial neural network


  • An experimental approach for CO2 and SO2 capturing by using multi-walled carbon nanotubes (MWCNT).
  • Modification of MWCNTs via functionalization and then studied the effects of oxygen and nitrogen functional groups on the adsorption performance of the adsorbents.
  • Designing an ANN model to predict the equilibrium adsorption of CO2 and SO2.
  • Assessment of the ability of an artificial neural network model to predict adsorption experimental data.
  • Comparison of ANN model with well-known adsorption isotherms.


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