Experimental Investigation on CO۲ and CH۴ Gas Separation Using SPEEK/PVA/ZnO Polymer Nanocomposite Membranes for Greenhouse Gas Emissions Reduction and Environmental Improvement
- سال انتشار: 1404
- محل انتشار: Iranian Journal of Chemistry and Chemical Engineering، دوره: 44، شماره: 11
- کد COI اختصاصی: JR_IJCCE-44-11_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 21
نویسندگان
Department of Chemical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, I.R. IRAN
Department of Chemical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, I.R. IRAN
Department of Chemical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, I.R. IRAN
چکیده
Reducing greenhouse gas emissions is a critical environmental challenge, with fossil fuel consumption and deforestation as major contributors to rising CO۲ levels. Membrane-based gas separation has emerged as a promising technology due to its low energy requirements, compact design, and environmental compatibility. In this study, a novel Sulfonated PolyEther Ether Ketone (SPEEK)/polyvinyl alcohol (PVA)/zinc oxide (ZnO) nanocomposite membrane was fabricated and evaluated for CO۲/CH۴ separation. The incorporation of ZnO nanoparticles into the SPEEK/PVA matrix was optimized to achieve uniform dispersion, enhancing both permeability and stability. Characterization by SEM, FT-IR, XRD, AFM, and TGA confirmed the successful integration of ZnO nanoparticles and demonstrated favorable structural and thermal properties. The SPEEK (۷۰ %)/PVA (۲۸ %)/ZnO (۲ %) membrane exhibited a CO۲ permeability of ۴۰–۶۰ Barrer with CO۲ /CH₄ selectivity of ۲۰–۲۵, representing an effective balance of transport and selectivity. Compared to state-of-the-art membranes such as Pebax/PEG/SiO۲ and Matrimid/ZIF-۸, the proposed membrane delivers comparable separation performance at significantly lower production costs (≈۷.۵–۸.۵ USD/m²), highlighting its industrial scalability and economic advantage. In addition, Artificial Neural Network (ANN) modeling using a multilayer perceptron (MLP) with the Levenberg–Marquardt algorithm achieved excellent predictive accuracy (R² = ۰.۹۹۹۹), confirming strong agreement between experimental and predicted values. This combined experimental and modeling approach demonstrates the potential of SPEEK/PVA/ZnO membranes as cost-effective, high-performance candidates for CO۲ capture and natural gas purification.کلیدواژه ها
Gas separation, SPEEK/PVA/ZnO nanocomposite membrane, CO۲/CH۴ separation, Artificial neural network modeling, Robeson&rsquo, s upper bound.اطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.