Advanced Energy Technologies
Ali Mostafaeipour; Mojtaba Qolipour; Hossein Goudarzi; Mehdi Jahangiri; Amir-Mohammad Golmohammadi; Mostafa Rezaei; Alireza Goli; Ladan Sadeghikhorami; Ali Sadeghi Sedeh; Seyad Rashid Khalifeh Soltani
Abstract
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 ...
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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
Renewable Energy Resources and Technologies
Ali Mostafaeipour; Mohammad Saidi Mehrabad; Mojtaba Qolipour; Mohadese Basirati; Mostafa Rezaei; Amir Mohammad Golmohammadi
Abstract
The present study aimed at ranking and selecting the superior geothermal project for hydrogen production in 14 provinces of Iran using a multi-objective optimization fuzzy hybrid approach through analyzing the ratio (fuzzy Moora) and expanded entropy weighting method. In this research, the extended entropy ...
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The present study aimed at ranking and selecting the superior geothermal project for hydrogen production in 14 provinces of Iran using a multi-objective optimization fuzzy hybrid approach through analyzing the ratio (fuzzy Moora) and expanded entropy weighting method. In this research, the extended entropy weighing method and the Fuzzy-Moora approach were utilized to weigh the criteria and project the ranking, respectively. In this research, 13 criteria for ranking the geothermal projects in Iran have been selected for hydrogen production. At first, the technical-economic feasibility of the projects was carried out in Homer software, and then the ranking process was performed with the proposed hybrid approach. The results showed that among 14 studied provinces using geothermal energy, the provinces of Bushehr, Hormozgan, Isfahan, Mazandaran, East Azarbaijan, Fars, Qazvin, Zanjan, Ardebil, Khorasan Razavi, Kerman, Sistan and Baluchestan, South Khorasan and West Azarbaijan were ranked in that order in terms of hydrogen production.