Machine learning models for predicting characteristics of PVAm membranes for post-combustion CO۲ capture application
Publish place: 3nd International Conference on the New Technologies in the Oil, Gas and Petrochemical Industries
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
View: 203
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
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).
Keywords:
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