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

Bagnato, G. and Liguori, S. and Iulianelli, A. and Curcio, S. and 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, ITA, pp. 363-364. ISBN 9788882863241

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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.

Item Type:
Contribution in Book/Report/Proceedings
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ID Code:
176119
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Deposited On:
05 Oct 2022 08:25
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Nov 2022 17:48