The oxidative coupling of methane (OCM) performance over Na-W-Mn/SiO2 at elevated pressures has been simulated by adaptive neuro fuzzy inference system (ANFIS) using reaction data gathered in an isothermal fixed bed microreactor. In the designed neuro fuzzy models, three important parameters such as methane to oxygen ratio, gas hourly space velocity (GHSV), and reaction temperature were considered as inputs and methane conversion and the selectivity of product hydrocarbons (C2+) were chosen as outputs. Two five-layer neuro fuzzy models based on the partitioning algorithm were designed at each reaction pressure to predict the product hydrocarbons (C2+) selectivity and methane conversion separately as a linear combination of inputs by the optimal selection of number and type of the membership functions. Moreover, to evaluate the ability and accuracy of the developed neuro fuzzy models in the prediction of OCM reaction performance, the results of ANFIS models were compared with experimental data and artificial neural network outputs. The comparison was carried out by the calculation of some statistical parameters such as correlation coefficient (R2), mean squared error (MSE), and average relative deviation (ARD). The results show that there are excellent agreement between model predictions and experimental data and the proposed ANFIS model can predict the methane conversion and product hydrocarbons (C2+) selectivity under different operating conditions by high accuracy.
 Fang X., Li S., Gu J., and Yang D., “Preparation and Characterization of W-Mn Catalyst for Oxidative Coupling of Methane,” J. Mol. Catal., 1992, 6, 255-261.
 Fang X., Li S., Lin J., and Chu Y., “Oxidative Coupling of Methane on W-Mn Catalysts,” J. Mol. Catal., 1992, 6, 427-433.
 Wang D. J., Rosynek M. P., and Lunsford J. H., “Oxidative Coupling of Methane over Oxide Supported Sodium-manganese Catalysts,” J. Catal., 1995, 55, 390-402.
 Chua Y. T., Mohamed A. R., and Bhatia S., “Process Optimization of Oxidative Coupling of Methane for Ethylene Production Using Response Surface Methodology,” J. Chem. Technol. Biotechnology, 2007, 82, 81-91.
 Wang J., Chou L., Zhang B., Song H., et al., “Comparative Study on Oxidation of Me-thane to Ethane and Ethylene over Na2WO4-Mn/SiO2 Catalysts Prepared by Different Methods,” J. Mol. Catal. A-Chem., 2006, 245, 272-277.
 Ji S., Xiao T., Li S., Xu C., et al., “The Relationship between the Structure and the Performance of Na-W-Mn/SiO2 Catalysts for the Oxidative Coupling of Methane,” Appl. Catal. A-Gen., 2002, 225, 271-284.
 Palermo A., Vazquez J. P. H., Lee A. F., Tikhov M. S., et al., “Critical Influence of the Amorphous Silica-to-Cristobalite Phase Transition on the Performance of Mn/Na2WO4/SiO2 Catalysts for the Oxidative Coupling of Methane,” J. Catal., 1998, 177, 259-266.
 Ji Sh., Li Sh., Liu Y., Gao L., et al., “Role of Sodium in the Oxidative Coupling of Methane Over Na-W-Mn/SiO2 Catalyst,” J. Nat. Gas Chem., 1999, 8, 1-8.
 Chou L., Cai Y., Zhang B., Niu J., et al., “Oxidative Coupling of Methane over Na-Mn-W/SiO2 Catalyst at Higher Pressure,” React. Kinet. Catal. Lett., 2002, 76, 311-315.
 Sadeghzadeh Ahari J., Ahmadi R., Mikami H., Inazu K., et al., “Application of a Simple Kinetic Model for the Oxidative Coupling of Methane to the Design of Effective Catalysts,” Catal. Today, 2009, 145, 45-54.
 Liu Y., Liu X., Xue J., Hou R., et al., “Effect of Pressure on Oxidative Coupling of Methane over MgO/BaCO3 Catalyst-Studies of its Deactivation at Elevated Pressure,” Appl. Catal. A-Gen., 1998, 168, 139-149.
 Kamali Shahri S. M. and Alavi S. M., “Kinetic Studies of the Oxidative Coupling of Methane over the Mn/Na2WO4/SiO2 Catalyst,” J. Nat. Gas Chem., 2009, 18, 25-34.
 Daneshpayeh M., Khodadadi A., Mostoufi N., Mortazavi Y., et al., “Kinetic Modeling of Oxidative Coupling of Methane over Mn/Na2WO4/SiO2 Catalyst,” Fuel Process Technol., 2009, 90, 403-410.
 Chou L., Cai Y., Zhang B., Niu J., et al., “Oxidative Coupling of Methane over Na-W-Mn/SiO2 Catalysts at Elevated Pressures,” J. Nat. Gas Chem., 2002, 11, 131-136.
 Zhou Q., Wu Y., Chan C. W., and Tontiwachwuthikul P., “From Neural Network to Neuro Fuzzy Modeling: Applications to the Carbon Dioxide Capture Process,” Energy Procedia., 2011, 4, 2066-2073.
 Civelekoglu G., Yigit N. O., Diamadopoulos E., and Kitis M., “Modeling of COD Removal in a Biological Wastewater Treatment Plant Using Adaptive Neuro Fuzzy Inference System and Artificial Neural Network,” Water Sci. Technol., 2009, 60, 1475-1487.
 Sedighi M., Keyvanloo K., and Towfighi J., “Modeling of Thermal Cracking of Heavy Liquid Hydrocarbon: Application of Kinetic Modeling, Artificial Neural Network, and Neuro-Fuzzy Models,” Ind. Eng. Chem. Res., 2011, 50, 1536-1547.
 Sargolzaei J. and Kianifar A., “Neuro Fuzzy Modeling Tools for Estimation of Torque in Savonius Rotor Wind Turbine,” Adv. Eng. Soft., 2010, 41, 619-626.
 Khazraeea S. M., and Jahanmiri A. H., “Composition Estimation of Reactive Batch Distillation by using Adaptive Neuro Fuzzy Inference System,” Chinese J. Chem. Eng., 2010, 18, 703-710.
 Sua X., Zenga G., Huang G., Lic J., et al., “Modeling Research on the Sorption Kinetics of Pentachlorophenol (PCP) to Sediments based on Neural Networks and Neuro-Fuzzy Systems,” Eng. Appl. Atif. Intel., 2007, 20, 239-247.
 Khajeh A., Modarress H., and Rezaee B., “Application of Adaptive Neuro Fuzzy Inference System for Solubility Prediction of Carbon Dioxide in Polymers,” Expert Syst. Appl., 2009, 36, 5728-5732.
 Khajeh A. and Modarress H., “Prediction of Solubility of Gases in Polystyrene by Adaptive Neuro Fuzzy Inference System and Radial Basis Function Neural Network,” Expert Syst. Appl., 2010, 37, 3070-3074.
 Savkovic Stevanovic J., “A Neural-Fuzzy Controller for Product Composition Control of the Ethanol Distillation Plant,” CHISA–15th International Congress of Chemical and Process Engineering, Praha, 2002.
 Entchev E. and Yang L., “Application of Adaptive Neuro Fuzzy Inference System Techniques and Artificial Neural Networks to Predict Solid Oxide Fuel Cell Performance in Residential Micro generation Installation,” J. Power Sources, 2007, 170, 122-129.
 Vassilopoulos A. P. and Bedi R., “Adaptive Neuro Fuzzy Inference System in Mod-eling Fatigue Life of Multidirectional Composite Laminate,” Comput. Mat. Sci., 2008, 43, 1086-1093.
 Shabanian M. and Montazeri M., “A Neuro-Fuzzy Online Fault Detection and Diagnosis Algorithm for Nonlinear and Dynamic Systems,” Int. J. Cont. Auto. Syst., 2011, 9, 665-670.
 Blázqueza L. F., De Miguelb L. J., Allera F., and Peránc J. R., “Neuro-Fuzzy Identifica-tion Applied to Fault Detection in Non-linear Systems,” Int. J. Sys. Sci., 2011, 42, 1771-1787.
 Zahedi Abghari S., and Sadi M., “Applica-tion of Adaptive Neuro-Fuzzy Inference System for the Prediction of the Yield Distribution of the Main Products in the Steam Cracking of Atmospheric Gasoil,” J. Taiwan Ins. Chem. Eng., 2013, 44, 365-376.
 Sadeghzadeh Ahari J., Sadeghi M. T., and Zarrinpashne S., “Optimization of OCM Reactions over Na-W-Mn/SiO2 Catalyst at Elevated Pressure,” J. Taiwan Inst. Chem. Eng., 2011, 42, 751-759.