CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

Abstract

AbstractIn this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).