Design and Control of PEM Fuel Cell Diffused Aeration System using Artificial Intelligence Techniques

Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

Abstract:

Fuel cells have become one of the major areas of research in the academia and the industry. The goal of most fish farmers is to maximize production and profits while holding labor and management efforts to the minimum. Risk of fish kills, disease outbreaks, poor water quality in most pond culture operations, aeration offers the most immediate and practical solution to water quality problems encountered at higher stocking and feeding rates. Many units of aeration system are electrical units so using a continuous, high reliability, affordable, and environmentally friendly power sources is necessary. Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered a diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by control input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC.

Keywords: PEM fuel cell, Diffused aeration system, Artificialintelligence (AI) techniques, neural network control, fuzzy logiccontrol

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076290

References:

[1] Abhishek Sakhare a, Asad Davari, Ali Feliachi,'' Fuzzy logic control of fuel cell for stand-alone and grid connection'', Journal of Power Sources Vol., 135, PP., 165-176, 2004.
[2] M.J. Khan, M.T. Iqbal, ''Analysis of a small wind-hydrogen stand-alone hybrid energy system", Applied Energy, Vol., 86, PP., 2429-2442, 2009.
[3] Soteris A. Kalogirou "Artificial neural networks in renewable energy systems applications: a review", Renewable and Sustainable Energy Reviews Vol. 5 pp. 373-401, 2001.
[4] Abdel Ghani Aissaoui, "a fuzzy logic controller for synchronous machine'', Journal of Electrical Engineering, Vol. 58, pp. 285-290, 2007.
[5] D. Driankov, H. Hellendoorn, and M. Reinfrank, ''an introduction to fuzzy control'', Springer-Verlag, Berlin, Heidelberg, 1993.
[6] Mark C. Williams, ''Fuel Cell Handbook'', fifth ed., EG&G Services Parsons, Inc., 2000.
[7] Colleen Spiegel, "PEM fuel cell modeling and simulation using MATLAB", Academic Press, 2008.
[8] Energy Efficiency Guide for Industry in Asia - www.energyefficiencyasia.org.
[9] Austin Hughes, ''Electric motors and drives fundamentals, types and applications'', Newnes, 2006.
[10] P. Thepsatom, A. Numsomran, V. TipsuwanpoM and T. Teanthong, ''DC motor speed control using fuzzy logic based on Lab VIEW'', SICEICASE International Joint Conference 2006.
[11] Kalogirou SA. ''Artificial intelligence for the modeling and control of combustion processes: a review'', Prog Energy Combust Sci; Vol., 29, 515-66, 2003.
[12] C.W. Tao, ''Design of fuzzy-learning fuzzy controllers, fuzzy systems proceedings'', 1998. IEEE World Congress on Computational Intelligence.,.
[13] Zhan Yuedong, Zhu Jianguo , Guo Youguang , Jin Jianxun, "Control of proton exchange membrane fuel cell based on fuzzy logic", Proceedings of the 26th Chinese Control Conference, 2007, China, IEEE.
[14] Christina N. Papadimitriou and Nicholas A.Vovos, "A Fuzzy Control Scheme for Integration of DGs into a Microgrid ", IEEE, 2010.
[15] Ahmed M. Ibrahim, "Fuzzy logic for embedded systems application", Newnes press, 2004.
[16] M. Azouz, A. Shaltout and M. A. L. Elshafei, "Fuzzy logic control of wind energy systems", Proceedings of the 14th International Middle East Power Systems Conference (MEPCON-10), Egypt, 2010.
[17] S. Lalouni, D. Rekioua, T. Rekioua and E. Matagne, "Fuzzy logic control of stand-alone photovoltaic system with battery storage", Journal of Power Sources, Vol. 193, PP. 899-907, 2009.
[18] Ch. Ben Salah, M. Chaaben, M. Ben Ammar, "Multi-criteria fuzzy algorithm for energy management of a domestic photovoltaic panel", Renewable Energy Vol. 33, PP. 993 -1001, 2008.
[19] Soteris A Kalogiroua, Soa Pantelioub, Argiris Dentsoras, "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater", Renewable Energy, Vol., 18, PP., 87-99, 1999.
[20] James A. Freeman, David M. Skapura, ''Neural networks algorithms, applications, and programming techniques'', Addison-Wesley Publishing Company, Inc., Paris, 1991.
[21] M.N. Cirstea, A. Dinu, J.G. Khor, M. McCormick, "Neural and fuzzy logic control of drives and power systems", Replika Press Delhi , India, 2002.
[22] Soteris A. Kalogirou, "Prediction of flat-plate collector performance parameters using artificial neural networks", Solar Energy, Vol., 80, PP., 248-259, 2006.
[23] Adnan Sozen, Tayfun Menlik, Sinan Unvar, "Determination of efficiency of flat-plate solar collectors using neural network approach", Expert Systems with Applications, Vol., 35, PP., 1533-1539, 2008.