TY  - JOUR
EP  - 113
SN  - 13871811
PB  - Elsevier B.V.
N1  - cited By 19
SP  - 95
TI  - Total and partial uptakes of multicomponent vapor-gas mixtures on 13X zeolite at 343K: Experimental and modeling study
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029511559&doi=10.1016%2fj.micromeso.2017.09.004&partnerID=40&md5=019825d1502d63beb5dcb5241a3e75bc
A1  - Abdul Kareem, F.A.
A1  - Shariff, A.M.
A1  - Ullah, S.
A1  - Keong, L.K.
A1  - Mellon, N.
JF  - Microporous and Mesoporous Materials
VL  - 258
Y1  - 2018///
N2  - In this work, GERG2008 EoS embedded in volumetric-gravimetric system was developed to allocate multicomponent partial/individual components uptakes in the mixture. The refined arrangement may overlay the current theoretical anticipated outcomes and interchange it with experimental and more trustworthy selective adsorption outcomes. 13X zeolite was utilized as a solid adsorbent for binary and ternary CO2:CH4:H2O mixtures adsorption. Premixed and preloaded water vapor was studied at 343 K and up to 10 bar. Artificial neural network (ANN) modeling was engaged to predict binary and ternary mixtures. ANN results disclosed decent promise with experimental data. Besides, simulated formations utilizing ANN model replicated high consistency. The testified outcomes magnificently identified particular components behavior in ternary and higher multicomponent mixtures. © 2017 Elsevier Inc.
KW  - Adsorption; Bins; Carbon dioxide; Neural networks; Water vapor; Zeolites
KW  -  ANN modeling; Artificial neural network models; Binary and ternary mixtures; Multicomponent mixture; Selective adsorption; Solid adsorbents; Total and partial uptakes; Vapor-gas mixtures
KW  -  Binary mixtures
ID  - scholars10950
ER  -