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Neural Computing and Applications
Erschienen 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

verfasst von: Hossein Rezaei, Mahmoud Rahmati, Hamid Modarress

Erschienen in: Neural Computing and Applications | Ausgabe 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.
Vorheriger Artikel Neural network models for group behavior prediction: a case of soccer match attendance
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Metadaten
Titel
Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution
verfasst von
Hossein Rezaei
Mahmoud Rahmati
Hamid Modarress
Publikationsdatum
22.09.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2057-y

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