Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite

Ali Hosin Alibak; Seyed Mehdi Alizadeh; Shaghayegh Davodi Monjezi; As'ad Alizadeh; Falah Alobaid; Babak Aghel

doi:10.3390/membranes12111147

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO₂) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite

Membranes (Basel). 2022 Nov 16;12(11):1147. doi: 10.3390/membranes12111147.

Authors

Ali Hosin Alibak¹, Seyed Mehdi Alizadeh², Shaghayegh Davodi Monjezi³, As'ad Alizadeh⁴, Falah Alobaid⁵, Babak Aghel^{5

6}

Affiliations

¹ Chemical Engineering Department, Faculty of Engineering, Soran University, Soran 44008, Iraq.
² Petroleum Engineering Department, Australian University, West Mishref 11411, Kuwait.
³ Department of Environmental Science, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Nur 46414356, Iran.
⁴ Department of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil 44001, Iraq.
⁵ Institut Energiesysteme und Energietechnik, Technische Universität Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany.
⁶ Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah 6715685420, Iran.

Abstract

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO₂) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO₂ permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO₂ separation.

Keywords: SAPO-34 zeolite; adaptive neuro-fuzzy inference system (ANFIS); carbon dioxide separation; mixed matrix membrane; theoretical analysis.

Grants and funding

This research received no external funding.