Title : ( Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell )
Authors: morteza esfandyari , Mohammad Ali Fanaei Shykholeslami , Reza Gheshlaghi , Mahmood Akhavan Mahdavi ,
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Abstract
Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modeling were used to investigate the effect of power density and Columbic efficiency (CE) in a microbial fuel cell. Four influential factors, including ionic strength, initial pH, medium nitrogen concentration, and temperature were selected as operating variables. Five levels are used for every factor. A feedforward neural network was trained using the back propagation algorithm and Levenberg Marquardt algorithm. Besides, an adaptive neuro-fuzzy inference system (ANFIS) model for simulation this process has been utilized. The results revealed that for predicting power density and CE values both ANN and ANFIS model have acceptable performance (R2>0.99), but ANN model has simpler structure and tuning procedure.
Keywords
, Microbial fuel cell modeling; Artificial neural network; Adaptive neuro, fuzzy inference system
@article{paperid:1053315,
author = {Esfandyari, Morteza and Fanaei Shykholeslami, Mohammad Ali and Gheshlaghi, Reza and Akhavan Mahdavi, Mahmood},
title = {Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell},
journal = {Journal of the Taiwan Institute of Chemical Engineers},
year = {2016},
volume = {58},
number = {1},
month = {January},
issn = {1876-1070},
pages = {84--91},
numpages = {7},
keywords = {Microbial fuel cell modeling; Artificial neural network; Adaptive neuro-fuzzy inference system},
}
%0 Journal Article
%T Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell
%A Esfandyari, Morteza
%A Fanaei Shykholeslami, Mohammad Ali
%A Gheshlaghi, Reza
%A Akhavan Mahdavi, Mahmood
%J Journal of the Taiwan Institute of Chemical Engineers
%@ 1876-1070
%D 2016
