Implementation of Neural Network Models for Parameter Estimation of a PEM-Electrolyzer
Steffen Becker* and Vishy Karri**
*University of Tasmania, GPO Box 252-65, Hobart 7001, Tasmania, Australia
**Australian College of Kuwait, P.O.Box 1411, Safat-13015, Kuwait
Predictive models were built using neural networks for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used online for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications.
-  Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), 2007.
-  International Energy Agency (IEA),World Energy Outlook (WEO), 2008.
-  T. N. Veziroglu, “Hydrogen Energy,” Part A, New York: Plenum, 1975.
-  H. Miland, “Operational Experience and Control Strategies for Stand-Alone Power Systems based on Renewable Energy and Hydrogen,” Ph.D. dissertation, Norwegian University of Science and Technology, Trondheim, 2005.
-  F. Barbir, “PEM Electrolysis for Production of Hydrogen from Renewable Energy Sources,” Int. J. Hydrogen Energy, Vol. 78, pp. 661-669, 2005.
-  M. Ni et al., “Potencial of renewable hydrogen production for energy supply in Hong Kong,” Int. J. Hydrogen Energy, Vol.31, pp. 1401-1412, 2006.
-  B. Suresh et al., “CEH product review: Hydrogen, SRI Consulting,” Chemical Economics Handbook, 2001.
-  O. Ulleberg, “Modeling of advanced alkaline electrolyzers: a system simulation approach,” Int. J. Hydrogen Energy, Vol.28, pp. 21-33, 2003.
-  H. Gorgun, “Dynamic modelling of a proton exchange membrane (PEM) electrolyzer,” Int. J. of Hydrogen Energy, Vol.31, pp. 29-38, 2006.
-  K. Onda et al., “Performance analysis of polymer-electrolyte water electrolysis cell at a small-unit test cell and performance prediction of large stacked cell,” J. of The Electrochemical Society, Vol.149, A1069-A1078, 2001.
-  S. Lecoeuche and M. E. Lebbal, “Identification and monitoring of a PEM electrolyser based on dynamical modelling,” Int. J. Hydrogen Energy, In Press, Corrected Proof, 2009.
-  J. Divisek, B. Steffen, and H. Schmitz, “Theoretical analysis and evaluation of the operating data of a bipolar water electrolyser,” Int. J. Hydrogen Energy, Vol.19, No.7, pp. 579-586, 1994.
-  R. G. M. Crocket, M. Newborough, D. J. Highgate, and S. D. Probert, “Electrolyser-based electricity management,” Appl. Energy, Vol.51, pp. 249-263, 1995.
-  R. H. Leaver and T. R. Thomas, “Analysis and presentation of experimental results,” The Macmillan Press Ltd, London, 1974.
-  S. H. Huang and H. C. Zang, “Artificial neural networks in manufacturing: concepts, applications and perspectives,” IEEE Trans. Components Packaging Manuf. Technology, Vol.17, No.2, pp. 212-228, 1994.
-  V, Karri and T. Kiatcharoenpol, “Prediction of Thrust and Torque in Drilling Using Conventional and an Optimised Layer by Layer Neural Network,” Proc. of the 3rd Asia Pacific Conf. on Systems Integrity and Maintenance (Systems Integrity and Maintenance - ACSIM 2002), Cairns, Australia, pp. 178-183, 2002.
-  V. Karri and F. Frost, “Need for optimisation techniques to select neural network algorithms for process modelling of reduction cell,” Proc. of the international conference on artificial intelligence and technology (AISAT), Hobart, Australia, pp. 134-140, Dec. 2000.
-  D. E. Rumelhart, J. L.McClelland, “Parallel distributed processing: explorations in the microstructure of cognition,” Vol.1, Cambridge: The MIT Press, 1988.
-  N. P. Padhy, “Artificial intelligence and intelligent systems,” Oxford University Press, New Delhi, 2005.
-  S. Haykin, “Neural networks: a comprehensive foundation,” second edition, Prentice Hall, New Jersey, 1999.
-  M. Negnevitsky, “Artificial Intelligence-A Guide to Intelligent Systems,” second edition, Pearson Education Limited, Harlow (England), 2005.
-  V. Karriet al., “Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction,” Int. J. Hydrogen Energy, Vol.33, pp. 2857-2867, 2008.
-  T. Ho et al., “An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car,” Int. J. Hydrogen Energy, Vol.33, pp. 3837-3846, 2008.
-  V. Karri, “Design and development of hydrogen laboratory,” Vol.1-3, University of Tasmania Research Report, 2004.
-  S. Becker and V. Karri, “Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems,” Int. J. of Hydrogen Energy. In Press, Corrected Proof.
-  Hogen 40 series, “2 Hydrogen Generator, Installation and Operation Instruction Manual,” Distributed Energy Systems, 2004.
-  PM 3000 ACE User Manual, Version 10, Voltech Instruments Inc.
-  L. Fausett, “Fundamentals of neural networks: Architectures, algorithms and applications,” Prentice Hall Int. Inc., 1994.
-  S. Ergezinger and E. Thomsen, “An accelerated learning algorithm for multilayer perceptrons: Optimisation Layer-by-Layer,” IEEE Trans. on Neural Networks, Vol.6, pp. 31-42, 1995.
-  V. Karri and T. Kiatcharoenpol, “Prediction of Internal Surface Roughness in Drilling using Three Feedforward Neural Networks – A Comparison,” CDRom Proc. of 9th Int. Conf. on Neural Information Processing, Singapore, EJ, 2002.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.