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Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite

Abdul Kareem, F.A. and Shariff, A.M. and Ullah, S. and Garg, S. and Dreisbach, F. and Keong, L.K. and Mellon, N. (2017) Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite. Energy Technology, 5 (8). pp. 1373-1391.

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Abstract

In this work, GERG2008 EoS embedded in a volumetric–gravimetric technique was utilized to measure multicomponent partial uptakes into the mixture. The sophisticated combination may overlap recent theoretical measurements and replace it with real-time and experimental selective adsorption analysis. 13X zeolite was utilized as a solid adsorbent for the adsorption of binary and ternary CO2/CH4/H2O mixtures. Premixed and preloaded water vapor was studied at 323 K temperature and up to 10 bar pressure. The isotherms of individual components within the mixture were identified and compared to the adsorption data of the pure components for assured benchmarking and validation. Artificial neural network (ANN) modeling was used to predict ternary mixtures. The ANN results showed a good agreement with the experimental data. Moreover, simulated configurations by utilizing an ANN model reflected the high consistency. We identified the behavior of the single components in ternary and higher multicomponent mixtures. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Item Type:Article
Impact Factor:cited By 2
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Chemical Engineering
ID Code:19414
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 00:44
Last Modified:20 Apr 2018 00:44

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