Modeling and Optimization of CO <sub>2</sub> Capture Process With Improved Porosity of PEI-Functionalized Adsorbent: RSM and ANN Approaches

Abstract

Adsorption and reduction of CO2 by amine-modified solid nano adsorbents were optimized by concomitant use of RSM (Response Surface Methodology) and ANNs (Artificial Neuron Networks). Extensive analysis of effective operating conditions including pressure, amine loading, and temperature, in the CO2 capture process by this robust method, was reliable. With the experimental data as training data using ANNs and RSM approach, the resulting model can provide acceptable results in an effect of independent variables and the interaction between them by the impact on the objective function, to optimize the process of CO2 capture by a new mesoporous amine-based nano adsorbent prepared with green chemistry. The models obtained from the ANN and RSM methods have acceptable compliance with the experimental outcomes and due to the minimum error obtained from the simulation, the ANN is recommended for the development of adsorption simulation models.