The paper presents use of an artificial neural network (ANN) for predicting the thermal-flow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.

References

1.
Bartela
,
Ł.
,
Skorek-Osikowska
,
A.
, and
Kotowicz
,
J.
,
2012
, “
Integration of a Supercritical Coal-Fired Heat and Power Plant With Carbon Capture Installation and Gas Turbine
,”
Rynek Energii
,
100
(
3
), pp.
56
62
.
2.
Jannelli
,
E.
Minutillo
,
M.
, and
Perna
,
A.
,
2013
, “
Analyzing Microcogeneration Systems Based on LT-PEMFC and HT-PEMFC by Energy Balances
,”
Appl. Energy
,
108
, pp.
82
91
.10.1016/j.apenergy.2013.02.067
3.
Bakalis
,
D.
, and
Stamatis
,
A.
,
2013
, “
Incorporating Available Micro Gas Turbines and Fuel Cell: Matching Considerations and Performance Evaluation
,”
Appl. Energy
,
103
, pp.
607
617
.10.1016/j.apenergy.2012.10.026
4.
Kupecki
,
J.
,
Jewulski
,
J.
, and
Badyda
,
K.
,
2013
, “
Comparative Study of Biogas and DME Fed Micro-CHP System With Solid Oxide Fuel Cell
,”
Appl. Mech. Mater.
,
267
, pp.
53
56
.10.4028/www.scientific.net/AMM.267.53
5.
Jeong
,
H.
,
Cho
,
S.
,
Kim
,
D.
,
Pyun
,
H.
,
Ha
,
D.
,
Han
,
C.
,
Kang
,
M.
,
Jeong
,
M.
, and
Lee
,
S.
,
2012
, “
A Heuristic Method of Variable Selection Based on Principal Component Analysis and Factor Analysis for Monitoring in a 300 kW MCFC Power Plant
,”
Int. J. Hydrogen Energy
,
37
(
15
), pp.
11394
11400
.10.1016/j.ijhydene.2012.04.135
6.
Sanchez
,
D.
,
Monje
,
B.
,
Chacartegui
,
R.
, and
Campanari
,
S.
,
2013
, “
Potential of Molten Carbonate Fuel Cells to Enhance the Performance of CHP Plants in Sewage Treatment Facilities
,”
Int. J. Hydrogen Energy
,
38
(
1
), pp.
394
405
.10.1016/j.ijhydene.2012.09.145
7.
De Lorenzo
,
G.
, and
Fragiacomo
,
P.
,
2012
, “
A Methodology for Improving the Performance of Molten Carbonate Fuel Cell/Gas Turbine Hybrid Systems
,”
Int. J. Energy Res.
,
36
(
1
), pp.
96
110
.10.1002/er.1789
8.
Discepoli
,
G.
,
Cinti
,
G.
,
Desideri
,
U.
,
Penchini
,
D.
, and
Proietti
,
S.
,
2012
, “
Carbon Capture With Molten Carbonate Fuel Cells: Experimental Tests and Fuel Cell Performance Assessment
,”
Int. J. Greenhouse Gas Control
,
9
, pp.
372
384
.10.1016/j.ijggc.2012.05.002
9.
W.
Budzianowski
,
2012
, “
Sustainable Biogas Energy in Poland: Prospects and Challenges
,”
Renewable and Sustainable Energy Reviews
,
16
(
1
), pp.
342
349
.10.1016/j.rser.2011.07.161
10.
Qian
,
J.
,
Tao
,
Z.
,
Xiao
,
J.
,
Jiang
,
G.
, and
Liu
,
W.
,
2013
, “
Performance Improvement of Ceria-Based Solid Oxide Fuel Cells With Yttria-Stabilized Zirconia as an Electronic Blocking Layer by Pulsed Laser Deposition
,”
Int. J. Hydrogen Energy
,
38
(
5
), pp.
2407
2412
.10.1016/j.ijhydene.2012.11.112
11.
Marzooghi
,
H.
,
Raoofat
,
M.
,
Dehghani
,
M.
, and
Elahi
,
G.
,
2012
, “
Dynamic Modeling of Solid Oxide Fuel Cell Stack Based on Local Linear Model Tree Algorithm
,”
Int. J. Hydrogen Energy
,
37
(
5
), pp.
4367
4376
.10.1016/j.ijhydene.2011.11.149
12.
Pianko-Oprych
,
P.
, and
Jaworski
,
Z.
,
2012
, “
Numerical Modelling of the Micro-Tubular Solid Oxide Fuel Cell Stacks (Przeglad Metod Modelowania Numerycznego Mikrorurowych Stałotlenkowych Stosów Ogniw Paliwowych)
,”
Przemysl Chemiczny
,
91
(
9
), pp.
1813
1815
.
13.
Arriagada
,
J.
,
Olausson
,
P.
, and
Selimovic
,
A.
,
2002
, “
Artificial Neural Network Simulator for SOFC Performance Prediction
,”
J. Power Sources
,
112
(
1
), pp.
54
60
.10.1016/S0378-7753(02)00314-2
14.
Jurado
,
F.
,
2003
, “
Power Supply Quality Improvement With a SOFC Plant by Neural-Network-Based Control
,”
J. Power Sources
,
117
(
1–2
), pp.
75
83
.10.1016/S0378-7753(03)00309-4
15.
Huo
,
H.
,
Zhu
,
X.
, and
Cao
,
G.
,
2006
, “
Nonlinear Modeling of a SOFC Stack Based on a Least Squares Support Vector Machine
,”
J. Power Sources
,
162
(
2
), pp.
1220
1225
.10.1016/j.jpowsour.2006.07.031
16.
Wu
,
X.
Zhu
,
X.
Cao
,
G.
Tu
,
H.
,
2007
,
Nonlinear Modelling of a SOFC Stack by Improved Neural Networks Identification
, Vol.
8
,
Zhejiang University Press
, Hangzhou, China, pp.
1505
1509
.
17.
Wu
,
X.
,
Zhu
,
X.
,
Cao
,
G.
, and
Tu
,
H.
,
2007
, “
Modeling a SOFC Stack Based on GA-RBF Neural Networks Identification
,”
J. Power Sources
,
167
(
1
), pp.
145
150
.10.1016/j.jpowsour.2007.01.086
18.
Entchev
,
E.
, and
Yang
,
L.
,
2007
, “
Application of Adaptive Neuro-Fuzzy Inference System Techniques and Artificial Neural Networks to Predict Solid Oxide Fuel Cell Performance in Residential Microgeneration Installation
,”
J. Power Sources
,
170
(
1
), pp.
122
129
.10.1016/j.jpowsour.2007.04.015
19.
Milewski
,
J.
, and
Świrski
,
K.
,
2009
, “
Modelling the SOFC Behaviors by Artificial Neural Network
,”
Int. J. Hydrogen Energy
,
34
(
13
), pp.
5546
5553
.10.1016/j.ijhydene.2009.04.068
20.
Milewski
,
J.
,
Świrski
,
K.
,
Santarelli
,
M.
, and
Leone
,
P.
,
2011
,
Advanced Methods of Solid Oxide Fuel Cell Modeling
, 1st ed.,
Springer-Verlag, London
.
21.
Bozorgmehri
,
S.
, and
Hamedi
,
M.
,
2012
, “
Modeling and Optimization of Anode-Supported Solid Oxide Fuel Cells on Cell Parameters Via Artificial Neural Network and Genetic Algorithm
,”
Fuel Cells
,
12
(
1
), pp.
11
23
.10.1002/fuce.201100140
22.
Marra
,
D.
,
Sorrentino
,
M.
,
Pianese
,
C.
, and
Iwanschitz
,
B.
,
2013
, “
A Neural Network Estimator of Solid Oxide Fuel Cell Performance for On-Field Diagnostics and Prognostics Applications
,”
J. Power Sources
,
241
, pp.
320
329
.10.1016/j.jpowsour.2013.04.114
23.
Sisworahardjo
,
N.
,
Yalcinoz
,
T.
,
El-Sharkh
,
M.
, and
Alam
,
M.
,
2010
, “
Neural Network Model of 100 W Portable PEM Fuel Cell and Experimental Verification
,”
Int. J. Hydrogen Energy
,
35
(
17
), pp.
9104
9109
.10.1016/j.ijhydene.2010.05.124
24.
Amirinejad
,
M.
,
Tavajohi-Hasankiadeh
,
N.
,
Madaeni
,
S.
,
Navarra
,
M.
,
Rafiee
,
E.
, and
Scrosati
,
B.
,
2013
, “
Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network Modeling of Proton Exchange Membrane Fuel Cells Based on Nanocomposite and Recast Nafion Membranes
,”
Int. J. Energy Res.
,
37
(
4
), pp.
347
357
.10.1002/er.1929
25.
Milewski
,
J.
, and
Świrski
,
K.
,
2009
, “
Hybrid–Artificial Neural Network as Solid Oxide Fuel Cell Model
,” Hydrogen + Fuel Cells 2009: International Conference and Trade Show, Vancouver, Canada, June 1–3.
26.
Chaichana
,
K.
,
Patcharavorachot
,
Y.
,
Chutichai
,
B.
,
Saebea
,
D.
,
Assabumrungrat
,
S.
, and
Arpornwichanop
,
A.
,
2012
, “
Neural Network Hybrid Model of a Direct Internal Reforming Solid Oxide Fuel Cell
,”
Int. J. Hydrogen Energy
,
37
(
3
), pp.
2498
2508
.10.1016/j.ijhydene.2011.10.051
27.
Kishor
,
N.
, and
Mohanty
,
S.
,
2010
, “
Fuzzy Modeling of Fuel Cell Based on Mutual Information Between Variables
,”
Int. J. Hydrogen Energy
,
35
(
8
), pp.
3620
3631
.10.1016/j.ijhydene.2010.01.049
28.
Grondin
,
D.
,
Deseure
,
J.
,
Ozil
,
P.
,
Chabriat
,
J.
,
Grondin-Perez
,
B.
, and
Brisse
,
A.
,
2013
, “
Solid Oxide Electrolysis Cell 3D Simulation Using Artificial Neural Network for Cathodic Process Description
,”
Chem. Eng. Res. Des.
,
91
(
1
), pp.
134
140
.10.1016/j.cherd.2012.06.003
29.
Stempien
,
J.
,
Sun
,
Q.
,
Chan
,
S.
,
2013
, “
Performance of Power Generation Extension System Based on Solid-Oxide Electrolyzer Cells Under Various Design Conditions
,”
Energy
,
55
, pp.
647
657
.10.1016/j.energy.2013.03.031
30.
Zamaniyan
,
A.
,
Joda
,
F.
,
Behroozsarand
,
A.
, and
Ebrahimi
,
H.
,
2013
, “
Application of Artificial Neural Networks (ANN) for Modeling of Industrial Hydrogen Plant
,”
Int. J. Hydrogen Energy
,
38
(
15
), pp.
6289
6297
.10.1016/j.ijhydene.2013.02.136
31.
Demuth
,
H.
,
Beale
,
M.
, and
Hagan
,
M.
, 1992, “Neural Network Toolbox 6 User's Guide Matlab.
32.
Jiang
,
Y.
, and
Virkar
,
A. V.
,
2003
, “
Fuel Composition and Diluent Effect on Gas Transport and Performance of Anode-Supported SOFCS
,”
J. Electrochem. Soc.
,
150
(
7
), pp.
A942
A951
.10.1149/1.1579480
33.
Virkar
,
A.
, and
Wilson
,
L.
,
2003
, “
Low-Temperature, Anode-Supported High Power Density Solid Oxide Fuel Cells With Nanostructured Electrodes
,”
U.S. Department of Energy
, Technical Report. 10.2172/812922
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