relation: https://khub.utp.edu.my/scholars/8490/
title: Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
creator: Abdul Kareem, F.A.
creator: Shariff, A.M.
creator: Ullah, S.
creator: Garg, S.
creator: Dreisbach, F.
creator: Keong, L.K.
creator: Mellon, N.
description: 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
publisher: Wiley-VCH Verlag
date: 2017
type: Article
type: PeerReviewed
identifier:   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.  ISSN 21944288     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018807980&doi=10.1002%2fente.201600688&partnerID=40&md5=87bf6a433663efdbf46aa373ad9fffa4
relation: 10.1002/ente.201600688
identifier: 10.1002/ente.201600688