Neural network nonlinear modeling for hydrogen production using anaerobic fermentation

Ahmed El-Shafie

    Research output: Contribution to journalArticle

    10 Citations (Scopus)

    Abstract

    The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 °C, 6.0 ± 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box-Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records).

    Original languageEnglish
    Pages (from-to)539-547
    Number of pages9
    JournalNeural Computing and Applications
    Volume24
    Issue number3-4
    DOIs
    Publication statusPublished - Mar 2014

    Fingerprint

    Hydrogen production
    Fermentation
    Neural networks
    Hydrogen
    Clostridium
    Substrates
    Experiments
    Temperature

    Keywords

    • Anaerobic fermentation
    • Artificial neural network model
    • Hydrogen production
    • Renewable energy

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software

    Cite this

    Neural network nonlinear modeling for hydrogen production using anaerobic fermentation. / El-Shafie, Ahmed.

    In: Neural Computing and Applications, Vol. 24, No. 3-4, 03.2014, p. 539-547.

    Research output: Contribution to journalArticle

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