On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks

M. Sorrentino (Corresponding Author), D. Marra, C. Pianese, M. Guida, F. Postiglione, K. Wang, Antti Pohjoranta

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    18 Citations (Scopus)

    Abstract

    The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies.
    Original languageEnglish
    Pages (from-to)298-307
    Number of pages9
    JournalEnergy Procedia
    Volume45
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    Event68th Conference of the Italian Thermal Machines Engineering Association, ATI 2013 - Bologna, Italy
    Duration: 11 Sep 201313 Sep 2013
    Conference number: 68

    Fingerprint

    Solid oxide fuel cells (SOFC)
    Neural networks
    Recurrent neural networks
    Classifiers
    Degradation
    Monitoring
    Experiments

    Keywords

    • Black-box models
    • neural network based classification
    • recurrent neural network
    • solid oxide fuel cells
    • step-wise regression analysis

    Cite this

    Sorrentino, M. ; Marra, D. ; Pianese, C. ; Guida, M. ; Postiglione, F. ; Wang, K. ; Pohjoranta, Antti. / On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks. In: Energy Procedia. 2013 ; Vol. 45. pp. 298-307.
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    title = "On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks",
    abstract = "The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies.",
    keywords = "Black-box models, neural network based classification, recurrent neural network, solid oxide fuel cells, step-wise regression analysis",
    author = "M. Sorrentino and D. Marra and C. Pianese and M. Guida and F. Postiglione and K. Wang and Antti Pohjoranta",
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    On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks. / Sorrentino, M. (Corresponding Author); Marra, D.; Pianese, C.; Guida, M.; Postiglione, F.; Wang, K.; Pohjoranta, Antti.

    In: Energy Procedia, Vol. 45, 2013, p. 298-307.

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    TY - JOUR

    T1 - On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks

    AU - Sorrentino, M.

    AU - Marra, D.

    AU - Pianese, C.

    AU - Guida, M.

    AU - Postiglione, F.

    AU - Wang, K.

    AU - Pohjoranta, Antti

    N1 - CA2: BA2154

    PY - 2013

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    N2 - The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies.

    AB - The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies.

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    KW - recurrent neural network

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    KW - step-wise regression analysis

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    M3 - Article in a proceedings journal

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    SP - 298

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    JO - Energy Procedia

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    SN - 1876-6102

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