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

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• 作者：Hossein Rezaei ; Mahmoud Rahmati ; Hamid Modarress
• 关键词：Zeolite ; Adsorption ; Artificial neuro ; fuzzy inference system (ANFIS) ; Multilinear regression ; MLR
• 刊名：Neural Computing and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：2
• 页码：301-312
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Visi
• 出版者：Springer London
• ISSN：1433-3058
• 卷排序：28

文摘

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 (R2), 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.