Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell

被引：39

作者：

Esfandyari, Morteza ^{[1
]}

Fanaei, Mohammad Ali ^{[1
]}

Gheshlaghi, Reza ^{[1
]}

Mahdavi, Mahmood Akhavan ^{[1
]}

机构：

[1] Ferdowsi Univ Mashhad, Fac Engn, Dept Chem Engn, POB 1111, Mashhad 9177948944, Khorasan Razavi, Iran

来源：

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS | 2016年 / 58卷

关键词：

Microbial fuel cell modeling; Artificial neural network; Adaptive neuro-fuzzy inference system; INFERENCE SYSTEM; PERFORMANCE; ANFIS; PH; TEMPERATURE;

D O I：

10.1016/j.jtice.2015.06.005

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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 feed forward 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 (R-2 > 0.99), but ANN model has simpler structure and tuning procedure. (C) 2015 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

引用

页码：84 / 91

页数：8

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