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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2017

22-09-2015 | Original Article

Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution

Authors: Hossein Rezaei, Mahmoud Rahmati, Hamid Modarress

Published in: Neural Computing and Applications | Issue 2/2017

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Abstract

In this work, cationic (Mg2+, Ca2+, Sr2+, and Ba2+) substitution in X and Y faujasite zeolite structures and their effects on capacity of zeolites for methane adsorption were studied by applying multiple linear regression and expert adaptive neuro-fuzzy inference system (ANFIS) . Temperature, pressure, and molecular weight of cations were used as the input parameters. The results obtained from application of the proposed ANFIS model showed that at high pressures, the zeolite with smaller cation in their structure have higher methane adsorption capacity. The root-mean-square error, square correlation coefficient (R 2), mean absolute error, and percentage of mean absolute relative error for X and Y faujasite zeolites were evaluated, which indicated that ANFIS model can accurately predict the adsorption of methane gas on X and Y zeolites in the presence of the substituted cations.
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Metadata
Title
Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution
Authors
Hossein Rezaei
Mahmoud Rahmati
Hamid Modarress
Publication date
22-09-2015
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2057-y

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