Document Type : Research Article


1 Department of Industrial Engineering, Yazd University, Yazd, Iran

2 School of Architecture and Planning, University of New Mexico, NM, USA

3 Department of Mechanical Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

4 Department of Electrical Engineering, Shiraz University, Shiraz, Iran


Fuel cells are potential candidates for storing energy in many applications; however, their implementation is limited due to poor efficiency and high initial and operating costs. The purpose of this research is to find the most influential fuel cell parameters by applying the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS method is implemented to select highly influential parameters for proton exchange membrane (PEM) element of fuel cells. Seven effective input parameters are considered including four parameters of semi-empirical coefficients, parametric coefficient, equivalent contact resistance, and adjustable parameter. Parameters with higher influence are then identified. An optimal combination of the influential parameters is presented and discussed. The ANFIS models used for predicting the most influential parameters in the performance of fuel cells were performed by the well-known statistical indicators of the root-mean-squared error (RMSE) and coefficient of determination (R2). Conventional error statistical indicators, RMSE, r, and R2, were calculated. Values of R2 were calculated as of 1.000, 0.9769, and 0.9652 for three different scenarios, respectively. R2 values showed that the ANFIS could be properly used for yield prediction in this study


Main Subjects

1.     Rezaei, M., Mostafaeipour, A., Qolipour, M. and Tavakkoli-Moghaddam, R., "Investigation of the optimal location design of a hybrid wind-solar plant: A case study", International Journal of Hydrogen Energy, Vol. 43, No. 1, (2018), 100-114. (
2.     Rezaei, M., Salimi, M., Momeni, M. and Mostafaeipour, A., "Investigation of the socio-economic feasibility of installing wind turbines to produce hydrogen: Case Study", International Journal of Hydrogen Energy, Vol. 43, No. 52, (2018), 23135-23147. (
3.     Mostafaeipour, A., Saidi-Mehrabad, M., Rezaei, M. and Qolipour, M., "The ranking of southern ports and islands of Iran for seawater desalination plants using ELECTRE III method", Journal of Renewable Energy and Environment, Vol. 4, No. 2&3, (2017), 10-22.
4.     Mostafaeipour, A., Saidi-Mehrabad, M., Qolipour, M., Basirati, M., Rezaei, M. and Golmohammadi, A.M., "Ranking locations based on hydrogen production from geothermal in Iran using the Fuzzy Moora hybrid approach and expanded entropy weighting method", Journal of Renewable Energy and Environment, Vol. 4, No. 4, (2017), 9-21.
5.     Rezaei-Shouroki, M., Mostafaeipour, A. and Qolipour, M., "Prioritizing of wind farm locations for hydrogen production: A case study", International Journal of Hydrogen Energy, Vol. 42, No. 15, (2017), 9500-9510. (
6.     Ramazankhani, M.E., Mostafaeipour, A., Hosseininasab, H. and Fakhrzad, M.B., "Feasibility of geothermal power assisted hydrogen production in Iran", International Journal of Hydrogen Energy, Vol. 41, No. 41, (2016), 18351-18369. ( 2016.08.150).
7.     Fereidooni, M., Mostafaeipour, A., Kalantar, V. and Goudarzi, H., "A comprehensive evaluation of hydrogen production from photovoltaic power station", Renewable and Sustainable Energy Reviews, Vol. 4, (2018), 415-423. (
8.     Zarezade, M. and Mostafaeipour, A., "Identifying the effective factors on implementing the solar dryers for Yazd province, Iran", Renewable and Sustainable Energy Reviews, Vol. 57, (2016), 765-775. (
9.     Jahangiri, M., Alidadi Shamsabadi, A. and Saghaei, H., "Comprehensive evaluation of using solar water heater on a household scale in Canada", Journal of Renewable Energy and Environment, Vol. 5, No. 1, (2018), 35-42.
10.   Vahdatpour, S., Behzadfar, S., Siampour, L., Veisi, E. and Jahangiri, M., "Evaluation of off-grid hybrid renewable systems in the four climate regions of Iran", Journal of Renewable Energy and Environment, Vol. 4, No. 1, (2017), 61-70.
11.   Mostafaeipour, A., Qolipour, M. and Goudarzi, H., "Feasibility of using wind turbines for renewable hydrogen production in Firuzkuh, Iran", Frontiers in Energy, Vol. 13, No. 3, (2019), 494-505. (
12.   Jahangiri, M., Haghani, A., Shamsabadi, A.A., Mostafaeipour, A. and Pomares, L.M., "Feasibility study on the provision of electricity and hydrogen for domestic purposes in the south of Iran using grid-connected renewable energy plants", Energy Strategy Reviews, Vol. 23, (2019), 23-32. (
13.   Priya, K., Sudhakar Babu, T., Balasubramanian, K., Kumar, K.S. and Rajasekar, N., "A novel approach for fuel cell parameter estimation using simple genetic algorithm", Sustainable Energy Technologies and Assessments, Vol. 12, (2015), 46-52. ( 2015.09.001).
14.   Raj, A., Sasmito, A.P. and Shamim, T., "Numerical investigation of the effect of operating parameters on a planar solid oxide fuel cell", Energy Conversion and Management, Vol. 90, (2015), 138-145. (
15.   Maghsoodi, A., Afshari, E. and Ahmadikia, H., "Optimization of geometric parameters for design a high-performance ejector in the proton exchange membrane fuel cell system using artificial neural network and genetic algorithm", Journal of Applied Thermal Engineering, Vol. 71, No. 1, (2014), 410-418. (
16.   Verma, A. and Pitchumani, R., "Influence of transient operating parameters on the mechanical behavior of fuel cells", International Journal of Hydrogen Energy, Vol. 40, No. 26, (2015), 8442-8453. (
17.   Dadvar, M. and Afshari, E., "Analysis of design parameters in anodic recirculation system based on ejector technology for PEM fuel cells: A new approach in designing", International Journal of Hydrogen Energy, Vol. 39, No. 23, (2014) 12061-12073. (
18.   Li, C.H., Wu, S. and Yu, W., "Parameter design on the multi-objectives of PEM fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms", International Journal of Hydrogen Energy, Vol. 39, No. 9, (2014), 184502-4515. ( j.ijhydene.2014.01.034).
19.   Askarzadeh, A. and dos Santos Coelho, L., "A backtracking search algorithm combined with Burger’s chaotic map for parameter estimation of PEMFC electrochemical model", International Journal of Hydrogen Energy, Vol. 39, (2014), 11165-74. ( j.ijhydene.2014.05.052).
20.   Yang, S. and Wang, N., "A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model", International Journal of Hydrogen Energy, Vol. 37, No. 10, (2012), 8465-76. (
21.   Askarzadeh, A. and Rezazadeh, A., "An innovative global harmony search algorithm for parameter identification of a PEM fuel cell model", IEEE Trans Industrial Electron, Vol. 59, No. 9, (2012), 3473-80. (
22.   Boas, J.V., Oliveira, V.B., Marcon, L.R.C., Pinto, D.P., Simoes, M. and Pinto, A.M.F.R., "Effect of operating and design parameters on the performance of a microbial fuel cell with Lactobacillus pentosus", Biochemical Engineering Journal, Vol. 104, (2015), 34-40. (
23.   Karpenko-Jereb, L., Stering, C., Fink, C., Hacker, V., Theiler, A. and Tatschl, R., "Theoretical study of the influence of material parameters on the performance of a polymer electrolyte fuel cell", Journal of Power Sources, Vol. 297, (2015), 329-343. ( j.jpowsour.2015.07.011).
24.   Chavan, S.L. and Talange, D.B., "Modeling and performance evaluation of PEM fuel cell by controlling its input parameters", Energy, Vol. 138, (2018), 437-445. (
25.   Chavan, S.L. and Talange, D.B., "System identification black box approach for modeling performance of PEM fuel cell", Journal of Energy Storage, Vol. 18, (2018), 327-332. ( /j.est.2018.05.014).
26.   Li, S., Yuan, J., Xie, G. and Sundén, B., "Effects of agglomerate model parameters on transport characterization and performance of PEM fuel cells", International Journal of Hydrogen Energy, Vol. 43, No. 17, (2018), 8451-8463. (
27.   Wu, H.W., Shih, G.J. and Chen, Y.B., "Effect of operational parameters on transport and performance of a PEM fuel cell with the best protrusive gas diffusion layer arrangement", Applied Energy, Vol. 220, (2018), 47-58. (
28.   El-Fergany, A., "Extracting optimal parameters of PEM fuel cells using Salp Swarm optimizer", Renewable Energy, Vol. 119, (2018), 641-648. (
29.   Fathy, A. and Rezk, H., "Multi-verse optimizer for identifying the optimal parameters of PEMFC model", Energy, Vol. 143, (2018), 634-644. (
30.   Mitov, M., Bardarov, I., Mandjukov, P. and Hubenova, Y., "Chemo metrical assessment of the electrical parameters obtained by long-term operating freshwater sediment microbial fuel cells", Bioelectrochemistry, Vol. 106, (2015), 105-114. ( 10.1016/j.bioelechem.2015.05.017).
31.   Rajasekar, N., Jacob, B., Balasubramanian, K., Priya, K., Sangeetha, K. and Babu, T.S., "Comparative study of PEM fuel cell parameter extraction using genetic algorithm", Ain Shams Engineering Journal, Vol. 6, No. 4, (2015), 1187-1194. ( j.asej.2015.05.007).
32.   Mohandes, M., Rehman, S. and Rahman, S.M., "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)", Applied Energy, Vol. 88, No. 11, (2011), 4024-32. (
33.   Jang, J.S.R., "ANFIS: adaptive-network-based fuzzy inference systems", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, (1993), 665-685. (
34.   Naderpour, H., Rafiean, A. and Fakharian, P., "Compressive strength prediction of environmentally friendly concrete using artificial neural networks", Journal of Building Engineering, Vol. 16, (2018), 213-219. (
35.   Ghandoor, A.A. and Samhouri, M., "Electricity consumption in the industrial sector of Jordan: Application of multivariate linear regression and adaptive neuro-fuzzy techniques", Journal of Mechanical and Industrial Engineering, Vol. 3, No. 1, (2009), 69-76. (
36.   Singh, R., Kianthola, A. and Singh, T.N., "Estimation of elastic constant of rocks using an ANFIS approach", Applied Soft Computing, Vol. 12, (2012), 40-45. (
37.   Mostafaei, M., "ANFIS models for prediction of biodiesel fuels cetane number using desirability function", Fuel, Vol. 216, (2018), 665-672. (
38.   Vakhshouri, B. and Nejadi, S., "Prediction of compressive strength of self-compacting concrete by ANFIS models", Neurocomputing, Vol. 280, (2017), 13-22. (
39.   Kurnaz, S., Cetin, O. and Kaynak, O., "Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles", Expert Systems with Applications, Vol. 37, (2010), 1229-1234. (
40.   Yang, Y., Chen, Y., Wang, Y., Li, C. and Li, L., "Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting", Applied Soft Computing, Vol. 49, (2016), 663-675. (
41.   Tian, L. and Collins, C., "Adaptive neuro-fuzzy control of a flexible manipulator", Mechatronics, Vol. 15, (2005), 1305-1320. (
42.   Ekici, B.B. and Aksoy, U.T., "Prediction of building energy needs in early stage of design by using ANFIS", Expert Systems with Applications, Vol. 38, No. 5, (2011), 5338-5352. (
43.   Khajeh, A., Modarress, H. and Rezaee, B., "Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers", Expert Systems with Applications, Vol. 36, (2009), 5728-5732. (
44.   Inal, M., "Determination of dielectric properties of insulator materials by means of ANFIS: A comparative study", Journal of Materials Processing Technology, Vol. 195, (2008), 34-43. (
45.   Lo, S.P., Lin, Y.Y., "The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process", Journal of Materials Processing Technology, Vol. 168, (2005), 250-257. (
46.   Sen, B., Mandal, U.K. and Mondal, S.P., "Advancement of an intelligent system based on ANFIS for predicting machining performance parameters of Inconel 690-A perspective of metaheuristic approach", Measurement, Vol. 109, (2017), 9-17. ( j.measurement.2017.05.050).
47.   Ahmadi-Nedushan, B., "Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models", Construction and Building Materials, Vol. 36, (2012), 665-673. (
48.   Pourtousi, M., Sahu, J.N., Ganesan, P., Shamshirband, S.H. and Redzwan, G.H., "A combination of computational fluid dynamics (CFD) and adaptive neuro-fuzzy system (ANFIS) for prediction of the bubble column hydrodynamics", Powder Technology, Vol. 274, (2015), 466-481. (
49.   Basser, H., Karimi, H., Shamshirband, S.H., Akib, S., Amirmojahedi, M., Ahmad, R., Jahangizadeh, A. and Javidnia, H., "Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike", Applied Soft Computing, Vol. 70, (2015), 642-649. (
50.   Khosravi, A., Koury, R.N.N., Machado, L. and Pabon, J.J.G., "Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system", Sustainable Energy Technologies and Assessments, Vol. 25, (2018), 146-160. (
51.   Hashim, R., Roy, C., Motamedi, S., Shamshirband, S.H., Petkovic, D., Gocic, M. and Lee, S.C.H., "Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology", Atmospheric Research, Vol. 171, (2016), 21-30. (
52.   Hong, T., Kim, C., Jeong, J., Kim, J., Koo, C., Jeong, K. and Lee, M., “Framework for approaching the minimum CV (RMSE) using energy simulation and optimization tool", Energy Procedia, Vol. 88, (2016), 265-270. (
53.   Faizollahzadeh Ardabili, S., Mahmoudi, A. and Mesri Gundoshmian, T., "Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers", Journal of Building Engineering, Vol. 6, (2016), 301-308. ( j.jobe.2016.04.010).