Journal
NEURAL COMPUTING & APPLICATIONS
Volume 28, Issue 2, Pages 301-312Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-015-2057-y
Keywords
Zeolite; Adsorption; Artificial neuro-fuzzy inference system (ANFIS); Multilinear regression; MLR
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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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