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Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor. / Bagnato, G.; Liguori, S.; Iulianelli, A. et al.
Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015. ENEA, 2015. p. 363-364.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Bagnato, G, Liguori, S, Iulianelli, A, Curcio, S & Basile, A 2015, Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor. in Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015. ENEA, pp. 363-364, 6th European Fuel Cell Piero Lunghi Conference, Naples, Italy, 16/12/15. <https://www.enea.it/en/publications/abstract/proceedings-6-european-fuel-cell-lunghi>

APA

Bagnato, G., Liguori, S., Iulianelli, A., Curcio, S., & Basile, A. (2015). Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor. In Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015 (pp. 363-364). ENEA. https://www.enea.it/en/publications/abstract/proceedings-6-european-fuel-cell-lunghi

Vancouver

Bagnato G, Liguori S, Iulianelli A, Curcio S, Basile A. Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor. In Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015. ENEA. 2015. p. 363-364

Author

Bagnato, G. ; Liguori, S. ; Iulianelli, A. et al. / Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor. Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015. ENEA, 2015. pp. 363-364

Bibtex

@inproceedings{88ed2d12cec94512a4058fe0d196d66b,
title = "Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor",
abstract = "The water gas shift reaction was studied in membrane reactors for training an artificial neural network model. In particular, we have lead experiment varying many parameters as the reaction pressure, reaction temperature, gas hourly space velocity, sweep gas flow rate, H2O/CO feed molar ratio and feed configuration have been considered from both a modelling and an experimental point of view in order to analyze their influence on the water gas shift performance in two membrane reactors. Meanwhile, the artificial neural network model has been validated by using experimental tests as training results and it was validated whit a new data set, obtained optimizing the system to achieve as much as possible high hydrogen recovery. The model predicted the experimental performance of the water gas shift membrane reactors with an error on CO conversion lower than 0.5% and around 10% for the H2 recovery.",
keywords = "Artificial neural network, Membrane reactor, Pure hydrogen, Water gas shift",
author = "G. Bagnato and S. Liguori and A. Iulianelli and S. Curcio and A. Basile",
year = "2015",
month = dec,
day = "18",
language = "Undefined/Unknown",
isbn = "9788882863241",
pages = "363--364",
booktitle = "Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015",
publisher = "ENEA",
note = "6th European Fuel Cell Piero Lunghi Conference ; Conference date: 16-12-2015 Through 18-12-2015",
url = "https://www.enea.it/en/publications/abstract/proceedings-6-european-fuel-cell-lunghi",

}

RIS

TY - GEN

T1 - Artificial neural network model for water gas shift reaction in a dense Pd-Ag membrane reactor

AU - Bagnato, G.

AU - Liguori, S.

AU - Iulianelli, A.

AU - Curcio, S.

AU - Basile, A.

PY - 2015/12/18

Y1 - 2015/12/18

N2 - The water gas shift reaction was studied in membrane reactors for training an artificial neural network model. In particular, we have lead experiment varying many parameters as the reaction pressure, reaction temperature, gas hourly space velocity, sweep gas flow rate, H2O/CO feed molar ratio and feed configuration have been considered from both a modelling and an experimental point of view in order to analyze their influence on the water gas shift performance in two membrane reactors. Meanwhile, the artificial neural network model has been validated by using experimental tests as training results and it was validated whit a new data set, obtained optimizing the system to achieve as much as possible high hydrogen recovery. The model predicted the experimental performance of the water gas shift membrane reactors with an error on CO conversion lower than 0.5% and around 10% for the H2 recovery.

AB - The water gas shift reaction was studied in membrane reactors for training an artificial neural network model. In particular, we have lead experiment varying many parameters as the reaction pressure, reaction temperature, gas hourly space velocity, sweep gas flow rate, H2O/CO feed molar ratio and feed configuration have been considered from both a modelling and an experimental point of view in order to analyze their influence on the water gas shift performance in two membrane reactors. Meanwhile, the artificial neural network model has been validated by using experimental tests as training results and it was validated whit a new data set, obtained optimizing the system to achieve as much as possible high hydrogen recovery. The model predicted the experimental performance of the water gas shift membrane reactors with an error on CO conversion lower than 0.5% and around 10% for the H2 recovery.

KW - Artificial neural network

KW - Membrane reactor

KW - Pure hydrogen

KW - Water gas shift

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84994579936&partnerID=MN8TOARS

M3 - Conference contribution/Paper

SN - 9788882863241

SP - 363

EP - 364

BT - Proceedings of the 6th European Fuel Cell - Piero Lunghi Conference, EFC 2015

PB - ENEA

T2 - 6th European Fuel Cell Piero Lunghi Conference

Y2 - 16 December 2015 through 18 December 2015

ER -