Machine learning models for predicting characteristics of PVAm membranes for post-combustion CO۲ capture application

Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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NTOGP03_026

تاریخ نمایه سازی: 3 تیر 1401

Abstract:

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).

Authors

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