We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
CO<sub>2</sub> capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology.
- Authors
Moradi, Mohammad Reza; Ramezanipour Penchah, Hamid; Ghaemi, Ahad
- Abstract
In 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).
- Subjects
POLYMERIC sorbents; RESPONSE surfaces (Statistics); CARBON sequestration; RADIAL basis functions; ADSORPTION capacity
- Publication
Canadian Journal of Chemical Engineering, 2023, Vol 101, Issue 10, p5621
- ISSN
0008-4034
- Publication type
Article
- DOI
10.1002/cjce.24887