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

AbstractIn 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.

[1]  E. Maginn,et al.  Pure and binary component sorption equilibria of light hydrocarbons in the zeolite silicalite from grand canonical Monte Carlo simulations , 1999 .

[2]  Jane Labadin,et al.  A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction , 2012, Neural Computing and Applications.

[3]  H. Galavi Neuro-fuzzy modelling and forecasting in water resources , 2012 .

[4]  T. N. Singh,et al.  A neuro-fuzzy approach for prediction of longitudinal wave velocity , 2012, Neural Computing and Applications.

[5]  O. Talu,et al.  Effect of cations on methane adsorption by NaY, MgY, CaY, SrY, and BaY zeolites , 1993 .

[6]  Oscar Castillo,et al.  Soft Computing Applications in Optimization, Control, and Recognition , 2012, Studies in Fuzziness and Soft Computing.

[7]  Mohammad Ali Riahi,et al.  Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir , 2012, Neural Computing and Applications.

[8]  W. Sun,et al.  Adsorption of Methane on Several Zeolites by Monte Carlo Method , 2012 .

[9]  Lin Zhao,et al.  Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function , 2011, Neural Computing and Applications.

[10]  Tingjun Hou,et al.  Adsorption and Diffusion of Benzene in ITQ-1 Type Zeolite: Grand Canonical Monte Carlo and Molecular Dynamics Simulation Study , 2000 .

[11]  Tohid Azimzadegan,et al.  An artificial neural-network model for impact properties in X70 pipeline steels , 2012, Neural Computing and Applications.

[12]  H. Modarress,et al.  The effects of structural parameters of zeolite on the adsorption of hydrogen: a molecular simulation study , 2012 .

[13]  R. Krishna,et al.  A computational study of CO2, N2, and CH4 adsorption in zeolites , 2007 .

[14]  Jafar Towfighi,et al.  Modeling of Thermal Cracking of Heavy Liquid Hydrocarbon: Application of Kinetic Modeling, Artificial Neural Network, and Neuro-Fuzzy Models , 2011 .

[15]  S. Yapar,et al.  Modeling the adsorption of textile dye on organoclay using an artificial neural network , 2012 .

[16]  P. Leflaive,et al.  Extraframework cation distributions in X and Y faujasite zeolites: A review , 2008 .

[17]  S. Sandler,et al.  Storage and separation of CO2 and CH4 in silicalite, C168 schwarzite, and IRMOF-1: a comparative study from Monte Carlo simulation. , 2007, Langmuir : the ACS journal of surfaces and colloids.

[18]  T. N. Singh,et al.  Soft computing method for assessment of compressional wave velocity , 2012 .

[19]  U. Özdemir,et al.  Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks , 2011 .

[20]  Adnan Sözen,et al.  Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network , 2007 .

[21]  Sahar Amiri,et al.  An artificial neural network for prediction of gas holdup in bubble columns with oily solutions , 2011, Neural Computing and Applications.

[22]  J. Vermesse,et al.  Gas Adsorption on Zeolites at High Pressure , 1996 .

[23]  Reza Katal,et al.  Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA) , 2013 .

[24]  N. Subramanian,et al.  Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes. , 2005, Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques.

[25]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[26]  Springer-Verlag London Limited RETRACTED ARTICLE: Prediction of condensate-to-gas ratio by using stochastic particle swarm optimization and neural network , 2013 .

[27]  John Kabuba,et al.  Neural Network Technique for Modeling of Cu (II) Removal from Aqueous Solution by Clinoptilolite , 2014 .

[28]  Babak Rezaee,et al.  Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers , 2009, Expert Syst. Appl..

[29]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[30]  Jing Zhang,et al.  Intelligent control of nonlinear systems with application to chemical reactor recycle , 2012, Neural Computing and Applications.

[31]  O. Talu,et al.  High-pressure adsorption of methane in zeolites NaX, MgX, CaX, SrX and BaX , 1991 .

[32]  Sarat Kumar Patra,et al.  Extended date rate prediction for cognitive radio using ANFIS with Subtractive Clustering , 2012, 2012 5th International Conference on Computers and Devices for Communication (CODEC).

[33]  M. S. Tanyıldızı Modeling of adsorption isotherms and kinetics of reactive dye from aqueous solution by peanut hull , 2011 .

[34]  Li Tang,et al.  Adaptive control for a class of chemical reactor systems in discrete-time form , 2013, Neural Computing and Applications.

[35]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[36]  Z. Qu,et al.  Prediction and Experimental Verification of CO2 Adsorption on Ni/DOBDC Using a Genetic Algorithm–Back-Propagation Neural Network Model , 2014 .

[37]  Randall Q. Snurr,et al.  Molecular simulations of methane adsorption in silicalite , 1991 .

[38]  J. Rantala,et al.  Optimised Subtractive Clustering for Neuro-Fuzzy Models , 2022 .

[39]  K. Gubbins,et al.  Characterization of Porous Glasses: Simulation Models, Adsorption Isotherms, and the Brunauer−Emmett−Teller Analysis Method , 1998 .

[40]  H. Modarress,et al.  Selectivity of new siliceous zeolites for separation of methane and carbon dioxide by Monte Carlo simulation , 2013 .

[41]  S. Mousavi,et al.  Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system , 2013, Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering.

[42]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[43]  Mashallah Rezakazemi,et al.  Gas sorption in H2-selective mixed matrix membranes: Experimental and neural network modeling , 2013 .

[45]  D. Tomasko,et al.  High-pressure adsorption of CO2 on NaY zeolite and model prediction of adsorption isotherms. , 2004, Langmuir : the ACS journal of surfaces and colloids.

[46]  K. Khulbe,et al.  Adsorption of methane, ethane and ethylene on molecular sieve zeolites , 1996 .

[47]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[48]  T. N. Singh,et al.  Comparative Study of Intelligent Prediction Models for Pressure Wave Velocity , 2014 .

[49]  T. N. Singh,et al.  A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks , 2012, Neural Computing and Applications.

[50]  Gholamreza Khalaj,et al.  Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels , 2014, Neural Computing and Applications.

[51]  K. Y. Foo,et al.  Insights into the modeling of adsorption isotherm systems , 2010 .

[52]  E. Maginn,et al.  Adsorption Studies of Methane, Ethane, and Argon in the Zeolite Mordenite: Molecular Simulations and Experiments , 2000 .

[53]  M. Jhon,et al.  Theoretical studies on acidity and site selectivity of cations in faujasite zeolite , 1981 .

[54]  C. Grande,et al.  Effect of Ion Exchange on the Adsorption of Steam Methane Reforming Off-Gases on Zeolite 13X , 2010 .

[55]  Peter Stavroulakis,et al.  Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications , 2012 .

[56]  Melih Inal,et al.  Evaluation of Copper Biosorption onto Date Palm (Phoenix dactylifera L.) Seeds with MLR and ANFIS Models , 2013 .

[57]  Richard G. Brereton,et al.  Applied Chemometrics for Scientists , 2007 .

[58]  Amit Kumar Mishra,et al.  ANFIS with Subtractive Clustering-Based Extended Data Rate Prediction for Cognitive Radio , 2012 .

[59]  Mohammad Ali Ahmadi,et al.  RETRACTED ARTICLE: Evolving artificial neural network and imperialist competitive algorithm for prediction permeability of the reservoir , 2012, Neural Computing and Applications.

[60]  Hamid Modarress,et al.  Nitrogen adsorption on nanoporous zeolites studied by Grand Canonical Monte Carlo simulation , 2009 .

[61]  Aboozar Khajeh,et al.  Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network , 2010, Expert Syst. Appl..

[62]  Roger Isanta Navarro,et al.  Study of a neural network-based system for stability augmentation of an airplane , 2013 .

[63]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[64]  Effects of zeolites X and Y on the degradation of malathion in water , 2013 .

[65]  Xiaoqin Liu,et al.  Molecular Simulation for Adsorption and Separation of CH4/H2 in Zeolitic Imidazolate Frameworks , 2010 .

[66]  H. Modarress,et al.  Grand canonical Monte Carlo simulation of isotherm for hydrogen adsorption on nanoporous siliceous zeolites at room temperature , 2009 .

[67]  Govind Sethia,et al.  Sorption of CO, CH4, and N2 in Alkali Metal Ion Exchanged Zeolite-X: Grand Canonical Monte Carlo Simulation and Volumetric Measurements , 2010 .

[68]  Alírio E. Rodrigues,et al.  Adsorption Equilibrium of Methane, Carbon Dioxide, and Nitrogen on Zeolite 13X at High Pressures , 2004 .