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

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. 1...

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Main Authors: Abdul Kareem, F.A., Shariff, A.M., Ullah, S., Garg, S., Dreisbach, F., Keong, L.K., Mellon, N.
Format: Article
Published: Wiley-VCH Verlag 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018807980&doi=10.1002%2fente.201600688&partnerID=40&md5=87bf6a433663efdbf46aa373ad9fffa4
http://eprints.utp.edu.my/19414/
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spelling my.utp.eprints.194142018-04-20T00:44:30Z Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite Abdul Kareem, F.A. Shariff, A.M. Ullah, S. Garg, S. Dreisbach, F. Keong, L.K. Mellon, N. 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 Wiley-VCH Verlag 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018807980&doi=10.1002%2fente.201600688&partnerID=40&md5=87bf6a433663efdbf46aa373ad9fffa4 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. http://eprints.utp.edu.my/19414/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
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
format Article
author Abdul Kareem, F.A.
Shariff, A.M.
Ullah, S.
Garg, S.
Dreisbach, F.
Keong, L.K.
Mellon, N.
spellingShingle Abdul Kareem, F.A.
Shariff, A.M.
Ullah, S.
Garg, S.
Dreisbach, F.
Keong, L.K.
Mellon, N.
Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
author_facet Abdul Kareem, F.A.
Shariff, A.M.
Ullah, S.
Garg, S.
Dreisbach, F.
Keong, L.K.
Mellon, N.
author_sort Abdul Kareem, F.A.
title Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
title_short Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
title_full Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
title_fullStr Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
title_full_unstemmed Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
title_sort experimental and neural network modeling of partial uptake for a carbon dioxide/methane/water ternary mixture on 13x zeolite
publisher Wiley-VCH Verlag
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018807980&doi=10.1002%2fente.201600688&partnerID=40&md5=87bf6a433663efdbf46aa373ad9fffa4
http://eprints.utp.edu.my/19414/
_version_ 1738656066769518592
score 13.160551

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