Amirreza Farajnezhadi - School of Chemical Engineering, College of Engineering, University of Tehran, ۱۱۱۵۵/۴۵۶۳ Tehran, Iran
Mohammad Khodaparast - School of Chemical Engineering, College of Engineering, University of Tehran, ۱۱۱۵۵/۴۵۶۳ Tehran, Iran
Zahra Mansourpour - School of Chemical Engineering, College of Engineering, University of Tehran, ۱۱۱۵۵/۴۵۶۳ Tehran, Iran

Facilitated transport membranes fabricated by Polyvinylamine show great potential to competewith convenient carbon capture technologies in a post-combustion CO۲/N۲ separation, with lowerenergy consumption and zero toxicity. Precise mathematical models are needed to predict membranecharacteristics for designing suitable membrane equipment and optimizing process configuration. Twomain features of a membrane are the Permeance of CO۲ gas and CO۲/N۲ Selectivity that shows theoverall performance of each membrane by considering the solution-diffusion model. Two knownmachine learning algorithms were employed to predict the Permeance and Selectivity of a recentlydeveloped membrane based on its four major parameters. Both MLP-ANN and SVM functions had greatpotential to fit experimental data, while the MLP-ANN method works better for Permeance (R۲ equal to۰.۹۷۵) and the SVM method fits Selectivity better (R۲ equal to ۰.۹۴۸).

CO۲ Capture, Polyvinylamine Membrane, Machine Learning, Support vector machine, Artificial neural network