Prediction of Cell Voltage and Chlorine Current Efficiency of Aqueous HCl Electrolysis Utilizing an Oxygen Reducing Cathode Based on Artificial Neural Network

2019 ◽  
Vol 25 (33) ◽  
pp. 27-42
Author(s):  
Seyed Nezamedin Ashrafizadeh ◽  
Fereydoon Mohammadi ◽  
Abolfazl Sattari ◽  
Narjes Shojaikaveh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Current Efficiency ◽  
Cell Voltage ◽  
Artificial Neural

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

2013 ◽  
Vol 10 (4) ◽  
Author(s):  
Mehdi Tafazoli ◽  
Hamid Baseri ◽  
Ebrahim Alizadeh ◽  
Mohsen Shakeri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Neural Network Models ◽  
Input Parameters ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

The performance of a direct methanol fuel cell (DMFC) has complex nonlinear characteristics. In this paper, the performance of a DMFC has been modeled using a neural network approach. The input parameters of the DMFC model include cell geometrical and operational parameters such as the cell temperature, oxygen flow rate, channel depth of the bipolar plate, methanol concentration, cathode back pressure, and current density and the output parameter is the cell voltage. In order to predict the performance of a DMFC single cell, two types of artificial neural network (ANN) have been developed to correlate the input parameters of the DMFC to the cell voltage. The performance of the networks was investigated by varying the number of neurons, number of layers, and transfer function of the ANNs and the best one is selected based on the mean square error. The results indicated that the neural network models can predict the cell voltage with an acceptable accuracy.

Modeling Constructional Parameters of a Solid Oxide Fuel Cell by Using an Artificial Neural Network

2013 ◽  
Vol 343 ◽  
pp. 69-75 ◽  
Author(s):  
Jarosław Milewski ◽  
Konrad Świrski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cell Voltage ◽  
Physical Factors ◽  
Training Procedure ◽  
Ann Model ◽  
Mathematical Expressions ◽  
Algorithmic Solution ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An Artificial Neural Network (ANN) can predict an objects behavior with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure. There are SOFC features mainly architectural in nature that cannot be expressed in numerical form or where numerical expression is difficult to obtain, i.e. electrolyte type, anode type, cathode type etc. In those situations a hybrid model (H-ANN) which contains the ANN model and mathematical expressions can be applied. The H-ANN is able to predict cell voltage with knowledge of minimum physical factors.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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