Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

Kristian Kjær Justesen; Søren Juhl Andreasen; Hamid Reza Shaker; Mikkel Præstholm Ehmsen; John Andersen

doi:10.1016/j.ijhydene.2013.06.013

Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

Kristian Kjær Justesen, Søren Juhl Andreasen, Hamid Reza Shaker, Mikkel Præstholm Ehmsen, John Andersen

AAU Energy

Research output: Contribution to journal › Journal article › Research › peer-review

13 Citations (Scopus)

10 Downloads (Pure)

Abstract

This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS models are able to predict the mass flows with mean absolute errors for the H2 and CO2 models of less than 1% and 6.37% for the CO model and 4.56% for the CH3OH model.
The models have a wide range of applications such as dynamic modeling, stoichiometry observation and control, advanced control algorithms, or fuel cell diagnostics systems.

Original language	English
Journal	International Journal of Hydrogen Energy
Volume	38
Issue number	25
Pages (from-to)	10577-10584
Number of pages	8
ISSN	0360-3199
DOIs	https://doi.org/10.1016/j.ijhydene.2013.06.013
Publication status	Published - 21 Aug 2013

Keywords

HTPEM fuel cell
Methanol
Reformed Methanol Fuel Cell
Gas composition modeling
ANFIS
Fuzzy-logic and Neural Networks

Access to Document

10.1016/j.ijhydene.2013.06.013

http://www.sciencedirect.com/science/article/pii/S0360319913014511

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@article{4748ef2e761146deba471e6342df7c12,

title = "Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems",

abstract = "This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS models are able to predict the mass flows with mean absolute errors for the H2 and CO2 models of less than 1% and 6.37% for the CO model and 4.56% for the CH3OH model.The models have a wide range of applications such as dynamic modeling, stoichiometry observation and control, advanced control algorithms, or fuel cell diagnostics systems.",

keywords = "HTPEM fuel cell, Methanol, Reformed Methanol Fuel Cell, Gas composition modeling, ANFIS, Fuzzy-logic and Neural Networks",

author = "Justesen, {Kristian Kj{\ae}r} and Andreasen, {S{\o}ren Juhl} and Shaker, {Hamid Reza} and Ehmsen, {Mikkel Pr{\ae}stholm} and John Andersen",

year = "2013",

month = aug,

day = "21",

doi = "10.1016/j.ijhydene.2013.06.013",

language = "English",

volume = "38",

pages = "10577--10584",

journal = "International Journal of Hydrogen Energy",

issn = "0360-3199",

publisher = "Pergamon Press",

number = "25",

}

TY - JOUR

T1 - Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

AU - Justesen, Kristian Kjær

AU - Andreasen, Søren Juhl

AU - Shaker, Hamid Reza

AU - Ehmsen, Mikkel Præstholm

AU - Andersen, John

PY - 2013/8/21

Y1 - 2013/8/21

N2 - This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS models are able to predict the mass flows with mean absolute errors for the H2 and CO2 models of less than 1% and 6.37% for the CO model and 4.56% for the CH3OH model.The models have a wide range of applications such as dynamic modeling, stoichiometry observation and control, advanced control algorithms, or fuel cell diagnostics systems.

AB - This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS models are able to predict the mass flows with mean absolute errors for the H2 and CO2 models of less than 1% and 6.37% for the CO model and 4.56% for the CH3OH model.The models have a wide range of applications such as dynamic modeling, stoichiometry observation and control, advanced control algorithms, or fuel cell diagnostics systems.

KW - HTPEM fuel cell

KW - Methanol

KW - Reformed Methanol Fuel Cell

KW - Gas composition modeling

KW - ANFIS

KW - Fuzzy-logic and Neural Networks

U2 - 10.1016/j.ijhydene.2013.06.013

DO - 10.1016/j.ijhydene.2013.06.013

M3 - Journal article

SN - 0360-3199

VL - 38

SP - 10577

EP - 10584

JO - International Journal of Hydrogen Energy

JF - International Journal of Hydrogen Energy

IS - 25

ER -

Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

Abstract

Keywords

Access to Document

AUB Link

Fingerprint

Cite this