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A new approach toward modeling of mixed-gas sorption in glassy polymers based on metaheuristic algorithms |
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| Authors: | Saeed Khoshhal Salestan Ahmad Rahimpour Reza Abedini Mohammad Amin Soleimanzade Mohtada Sadrzadeh |
| Institution: | 1. Department of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran;2. Department of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
Contribution: Conceptualization (supporting), Methodology (supporting), Validation (supporting), Visualization (supporting), Writing - review & editing (equal);3. Department of Mechanical Engineering, 10-367 Donadeo Innovation Center for Engineering, Advanced Water Research Lab (AWRL), University of Alberta, Edmonton, Canada Contribution: Data curation (supporting), Formal analysis (supporting), Validation (supporting), Visualization (supporting);4. Department of Mechanical Engineering, 10-367 Donadeo Innovation Center for Engineering, Advanced Water Research Lab (AWRL), University of Alberta, Edmonton, Canada |
| Abstract: | Modeling mixed-gas sorption has always been associated with computational challenges due to the existence of two or more conflicting objective functions. This study aims to use an artificial intelligence approach toward modeling mixed-gas sorption in PIM-1 and TZ-PIM polymeric membranes. Non-dominated sorting genetic algorithm (NSGA-II) has been applied to identify the extended Henry-Langmuir (EHL) isotherm based on CO2-CH4 mixed-gas sorption data. Also, the group method of data handling (GMDH) neural network is implemented to obtain a formula for the calculation of equilibrium partial pressure corresponding to three effective parameters, which are easily measurable. The formula provides an accurate estimation from the equilibrium relationship between the partial pressure of each gas in the binary gas mixtures over the PIM-1 and TZ-PIM membranes. Eventually, the calculated coefficients of EHL isotherm and obtained formula for computing the partial pressure of each component are simultaneously applied into the isotherm model to predict the mixed-gas sorption behavior. The results showed that the computed lines well reproduce the experimental data points, proving that the applied artificial intelligence approach offers a suitable approximation for mixed-gas sorption. |
| Keywords: | CO2 separation GMDH mixed-gas sorption NSGA-II PIM-1 membrane |