Using halloysite nanotubes modified by tetraethylenepentamine for advanced carbon capture: Experimental and modeling via RSM and ANNs

In this research, halloysite nanotube was studied as a natural adsorbent for CO2 capture. The sorbents were prepared by the impregnation of several amounts of tetraethylenepentamine (TEPA) (10, 20, 30, and 40 wt%) on IMSiNTs support. Carbon dioxide adsorption was studied with the help of artificial neural network modeling and response surface methodology, and the effects of parameters were determined. Using the surface response method (RSM) simulation technique, the model obtained a suitable orientation to optimize the effective parameters in the process to select the best combination of operating conditions, and the adsorption capacity was measured in the temperature range of 20-50 degrees C and at pressures of 1-9 bar via tetraethylenepentamine (TEPA)-functionalized pristine nanotubes of halloysite (HNTs). Artificial neural network modeling was used to predict the adsorption process. The multi-layer perceptron (MLP) and radial-based function (RBF) models using different algorithms were investigated. According to the optimization process of the neurons in the MLP structure, the number of neurons in the first hidden layer was 25, and in the second hidden layer was 5. In addition, in RBF, there was a single hidden layer in which the number of neurons was 30. Response surfaces were created using the central composite design and a correlation coefficient of 0.992 based on a quadratic model was used. The CO2 capture experimental and simulation results were found to agree competently. A value of 9.3041 mmol/g at 9 bar and the temperature of 20 degrees C resulted in the maximum amount of CO2 adsorption capacity.