Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Rezaei, Hossein; Rahmati, Mahmoud; Modarress, Hamid
• Is Part Of: Neural computing & applications. Volume 28:Issue 2(2017)
• Page Start: 301
• Page End: 312
• Publication Date: 2017-02
• Subjects: Zeolite -- Adsorption -- Artificial neuro-fuzzy inference system (ANFIS) -- Multilinear regression -- MLR; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2057-y
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract In this work, cationic (Mg2+, Ca2+, Sr2+, and Ba2+ ) substitution inX andY 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 forX andY faujasite zeolites were evaluated, which indicated that ANFIS model can accurately predict the adsorption of methane gas onX andY zeolites in the presence of the substituted cations.