Neural Networks Modeling of the Alkaline Water Electrolyzes

Of all the hydrogen production technologies, water-electrolysis based on renewable electricity is ideal for a sustainable and clean hydrogen production. In this paper, artificial neural network (ANN) is used to build the nonlinear alkaline water electrolyzes, considering the current and temperature as inputs, the voltage of the alkaline water electrolyzes as the output and establishing the electrical characteristic model (voltage/current) of this electrolyzes according to the different temperature, the Results from this analysis are presented and discussed.

[1]  Sargur N. Srihari,et al.  Bayesian and neural network pattern recognition: a theoretical connection and empirical results with handwritten characters , 1991 .

[2]  Daniel Lumanauw.  Hydrogen bubble characterization in alkaline water electrolysis , 2000 .

[3]  M. J. Khan,et al.  Dynamic modeling and simulation of a small wind–fuel cell hybrid energy system , 2005 .

[4]  Sadik Kara,et al.  Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis , 2006, Comput. Biol. Medicine.

[5]  Okyay Kaynak,et al.  Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism , 2004 .

[6]  Caisheng Wang,et al.  Modeling and control of hybrid wind/photovoltaic/fuel cell distributed generation systems , 2006 .

[7]  H. Burke,et al.  Artificial neural networks for cancer research: outcome prediction. , 1994, Seminars in surgical oncology.

[8]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[9]  M. Tioursi,et al.  Power management strategy in the alternative energy photovoltaic/PEM Fuel Cell hybrid system , 2011 .

[10]  Gonzalo Pajares,et al.  A Hopfield Neural Network for combining classifiers applied to textured images , 2010, Neural Networks.

[11]  S. Ovshinsky,et al.  Development of a small scale hydrogen production-storage system of hydrogen applications , 1997, IECEC-97 Proceedings of the Thirty-Second Intersociety Energy Conversion Engineering Conference (Cat. No.97CH6203).

[12]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[13]  F. Harrell,et al.  Artificial neural networks improve the accuracy of cancer survival prediction , 1997, Cancer.

[14]  Keith W. Hipel,et al.  Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data , 2005 .

[15]  William G. Baxt,et al.  Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion , 1990, Neural Computation.

[16]  Gérard Dreyfus,et al.  Handwritten digit recognition by neural networks with single-layer training , 1992, IEEE Trans. Neural Networks.

[17]  Tania Binos,et al.  Evolving Neural Network Architecture and Weights Using An Evolutionary Algorithm , 2003 .

[18]  Moshe Leshno,et al.  Neural network prediction analysis: The bankruptcy case , 1996, Neurocomputing.

[19]  Ø. Ulleberg.  Modeling of advanced alkaline electrolyzers: a system simulation approach , 2003 .