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Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite
Author(s) -
Abdul Kareem Firas A.,
Shariff Azmi M.,
Ullah Sami,
Garg Sahil,
Dreisbach Frieder,
Keong Lau Kok,
Mellon Nurhayati
Publication year - 2017
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.201600688
Subject(s) - ternary operation , adsorption , gravimetric analysis , zeolite , methane , materials science , carbon dioxide , bar (unit) , thermodynamics , ternary numeral system , artificial neural network , chemistry , organic chemistry , computer science , catalysis , physics , machine learning , meteorology , programming language
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 CO 2 /CH 4 /H 2 O 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.