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   Prediction the Pem Fuel Cell Performance Based on Cathod Properties Using Neural Network Modeling  
   
DOR 20.1001.2.9919199705.1399.11.1.251.6
نویسنده - - ,- - ,- -
منبع كنگره مهندسي شيمي - 1399 - دوره : 11 - یازدهمین کنگره بین المللی مهندسی شیمی - کد همایش: 99191-99705
چکیده    The aim of this study is to predict the performance of proton exchange membrane (pem) fuel cells composed of various cathod materials by applying neural network modeling# to predict pem fuel cell performance, cyclic voltammetry charts and electrochemical active surface area (ecsa) calculations are required# at the first stage, required informations were selected based on the experimental data for various cathod materials# pure platinum, reduced graphene oxide- carbon black, carbon black and graphene nanoplatelets- carbon black are the common materials as cathod# after that, avaiable data were classified based on two variables: total surface area and percentage of platinum in the electrode# then, the artificial neural network (ann) code is written for each group# by running the code, the cathode cyclic voltammetry diagram as well as ecsa is obtained and compared with exprimental data# results showed that ann code could accurately predict the cathode cyclic voltammetry diagram and ecsa values# the higher amount of ecsa shows better performace of fuel cell#
کلیدواژه Pem Fuel Cell ,Artificial Neural Network ,Electrochemical Active Surface Area ,Cathod Materials
آدرس Ferdowsi University Of Mashhad, Iran, Ferdowsi University Of Mashhad, Iran, Ferdowsi University Of Mashhad, Iran
 
     
   
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