Dynamic Model of a PEM Electrolyzer based on Artificial Neural Networks

Dynamic Model of a PEM Electrolyzer based on Artificial Neural Networks

A. U. Chavez-Ramirez R. Munoz-Guerrero V. Sanchez-Huerta Juan M. Ramirez-Arredondo R. Ornelas L. G. ArriagaS. Siracusano G. Brunaccini G. Napoli V. Antonucci A. S. Arico 

Department of Electrical Engineering, CINVESTAV-IPN, Av. Instituto Politécnico Nacional No. 2508, CP 07360, D.F.

Department of Electrical Engineering, CINVESTAV-IPN Unidad Guadalajara, Av. Científica 1145, Zapopan, Jalisco, C.P. 45015

Tozzi Renewable Energy SpA, Via Zuccherificio, 10-48010 Mezzano (RA)

Centro de Investigación y Desarrollo Tecnológico en Electroquímica S.C., Pedro Escobedo, C.P. 76703, Querétaro.

CNR-ITAE, Via Salita S. Lucia sopra Contesse, 5-98126 Messina

Corresponding Author Email: 
1 December 2010
30 January 2011
6 April 2011
| Citation

Hydrogen production by electrolysis is emerging as one of the most promising ways to meet future fuel demand; likewise, the development of models capable of simulating the operation of electrolysis devices is indispensable in the efficient design of power generation systems, reducing manufacturing costs and resources savings. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role at the development of models for predicting the performance of complex systems. The behavior of a Polymer Electrolyte Membrane (PEM) Electrolyzer of three cell stack (100 cm2 of active area) was modeled successfully using a Multilayer Perceptron Network (MLP). This dynamic model has been trained to learn the internal relationships that govern this electrolysis device and predict its behavior without any physical equations. The electric current supply and the operation temperature were used as input vector able to predict each cell voltage behavior. A reliable accuracy (< 2%) was reached in this work after comparing the single cell performance of the real electrolyzer versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.


Artificial Neural Network (ANN), Model, Multilayer Perceptron Network (MLP), PEM Electrolyzer

1. Introduction
2. Experimental
3. Methodology
4. Results and Discussion
5. Conclusions

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