the 4th International Conference on Nanostructures (ICNS4 , 2012-03-12

Title : ( Modeling of carbon nanostructure materials based on adaptive neuro-fuzzy inference system )

Authors: Sajad Rashidi , Mohammad Ali Fanaei Shykholeslami , Ali Ahmadpour ,
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Todays, carbon nanostructures are famous structure which used in several industries like separation, treatment, energy storage (i.e. hydrogen storage), etc. Accordingly this is so obvious that why finding a time and money wasteless approach through modeling for the fabrication of a carbon structure like activated carbon could be so important. However, there are some attempts to achieve the appropriate theoretical modeling of activated carbon they were almost unsuccessful due to the complexity between the dependent and independent variables. This guides us in this paper to use empirical modeling like artificial intelligence and in specific way through adaptive neuro-fuzzy inference system (ANFIS). Establishing ANFIS model to delineate the relationship between the parameters like agent type, agent ration, etc. conclude to a good accuracy with a coefficient of determination values (R2) of 0.9885 and normalized mean square error (NMSE) of 0.00027 which mark this research higher accurate and more trustfully between the others.

Keywords

carbon nanostructure activated carbon optimization hydrogen storage methane
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@inproceedings{paperid:1029870,
author = {Rashidi, Sajad and Fanaei Shykholeslami, Mohammad Ali and Ahmadpour, Ali},
title = {Modeling of carbon nanostructure materials based on adaptive neuro-fuzzy inference system},
booktitle = {the 4th International Conference on Nanostructures (ICNS4},
year = {2012},
location = {کیش, IRAN},
keywords = {carbon nanostructure activated carbon optimization hydrogen storage methane storage},
}

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%0 Conference Proceedings
%T Modeling of carbon nanostructure materials based on adaptive neuro-fuzzy inference system
%A Rashidi, Sajad
%A Fanaei Shykholeslami, Mohammad Ali
%A Ahmadpour, Ali
%J the 4th International Conference on Nanostructures (ICNS4
%D 2012

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