A FUZZY LOGIC APPROACH FOR THE ESTIMATION OF PERFORMANCE HYDROXY DRY CELL WITH DIFFERENT PLATE COMBINATION

Yusuf YILMAZ; Sadık ATA; Gürol ÖNAL; Abdullah IŞIKTAŞ; Kevser DİNCER

A FUZZY LOGIC APPROACH FOR THE ESTIMATION OF PERFORMANCE HYDROXY DRY CELL WITH DIFFERENT PLATE COMBINATION

Yusuf YILMAZ, Sadık ATA, Gürol ÖNAL, Abdullah IŞIKTAŞ, Kevser DİNCER

Öz

In this study, hydroxy (HHO) dry cell with different plate combination performances in terms of current, temperature and flow rate were experimentally investigated and modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters plate number and time; output parameters current, temperature and flow rate were described by RBMTF if-the rules. The dimensions of the plates were 10x10 cm² and 11x11 cm². Current and temperature were measured for the different plate combination. This paper presents a fuzzy logic based study for estimating the uncertainty of the HHO drycell parameters. The 80 values between 90th and 270th seconds, which are not obtained from experimental work for 10x10 cm2 and 11x11 cm2 current, temperature and flow rate are predicted by fuzzy logic method. One of the results is; the current value predicted by RBMTF for the 11-2 plate combination and t=90 s is less than the current value from the results of the experimental work for the 11-2 plate combination and t=60 s, but higher than the current value from the results of the experimental work for 11-2 plate combination and t=120s.The comparison between experimental data and RBMTF is done by using three different statistical method. These are, root mean square error (RMSE), mean absolute error (MAE) and the coefficient of multiple determination (R²). For 10x10 cm² dimension plate, RMSE, MAE and R² for the current is 0.13, 0.111 and 96.44% respectively. For 11x11 cm² dimension plate, RMSE, MAE and R² for the current is 0.07926, 0.06466 and 98.44% respectively. coefficient of multiple determinations (R²). As a result, RBMTF model has shown satisfying relation with experimental results, which suggests an alternative approach to estimation of performance HHO dry cell with different plate combination.

Anahtar Kelimeler

Fuzzy Logic; HHO Dry Cell; Plate Combination

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Referanslar

Madyira D. And Harding W. Effect of HHO on four stroke petrol engine performance. 9th South African Conference on Computational and Applied Mechanics, 14-16 Jan 2014.

Leelakrishnan E, Lokesh N, Suriyan H., (2013) “Performance and emission characteristics of Brown’s gas enriched air in spark ignition engine”, International Journal of Innovative Research in Science, Engineering and Technology, vol.2, pp.393-404

Chakrapani K, Neelamegam P., (2011) “Optimization of fuel consumption using HHO in HDL technique verified in FPGA”, Journal of Theoretical and Applied Information Technology, vol.31, pp.140-146

Keshwani D.R, Jones D.D, Meyer G.E.R, Brand M., (2008) “Rule-based Mamdani-type fuzzy modeling of skin permeability”, Applied Soft Computing, vol.8, pp. 285-294

Shakhawat C, Tahir H, Neil B., (2006) “Fuzzy rule-based modelling for human health risk from naturally occurring radioactive materials in produced water”, Journal of Environmental Radioactivity, vol.89, pp 1-17

Yıldız Ş, Kişoğlu S., (2011) “Bulanık mantık yaklaşımı ile hazır giyimde beden numarası belirleme”, E-Journal of New World Sciences Academy, vol.6, pp.12-22

Özek A, Sinecen M., (2004) “Klima sistem kontrolünün bulanık mantık ile modellemesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol.10, pp.353-358

Tiryaki A.E, Kazan R., (2007) “Bulaşık makinesinin bulanık mantık ile modellenmesi”, Mühendis ve Makine, vol.48, pp. 3-8

Işıktaş A, Dincer K, Ata S., Comparison Between the Effects of Different Types of Membership Functions on Fuzzy Logic for Hydroxy Dry Cell Performance, 16th International Multidisciplinary Scientific GeoConference SGEM 2016, Bulgaria, 2016.

Ata S, Dincer K., Rule-based Mamdani-type fuzzy modeling of performance proton exchange membrane fuel cell with carbon nanotube, 15th International Multidisciplinary Scientific GeoConference SGEM 2015, Bulgaria, 2015, p. 487-494.

Ata S, Dincer K., Anot Tarafı Karbon Nanotüp İle Kaplanmış PEM Yakıt Hücresi Performansının Bulanık Mantık Yöntemiyle Modellenmesi, Ulusal Hidrojen Teknolojileri Kongresi UHTEK-2015, İstanbul, 2015.

Ata S. “PEM Yakıt Hücresinin Membran Performansının Deneysel Olarak İncelenmesi ve Enerji Ayrışımı Olayının Bulanık Mantık Yöntemi ile Modellenmesi,” Yüksek Lisans Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Konya, 2015.

Ata S, Dincer K., Improving the Performance of Proton Exchange Membrane Fuel Cell Using Fuzzy Logic, 18th International Conference on Energy and Sustainable Development, Paris, 16-17 May 2016.

Kim, Y., Kim, S., (1999) “An Electrical Modelling and Fuzzy Logic Control of a Fuel Cell Generation System”, IEEE Transactions on Energy Conversion, vol. 14, pp.239-244

Tong S.W, Qian D.W, Fang J.J., Li H.X., (2013) “Integrated Modelling and Variable Universe Fuzzy Control of a Hydrogen-Air Fuel Cell System”, Int. J. Electrochem. Sci., vol.8, pp. 3636-3652

Ciaramella A. “Situation Awareness in Mobile Recommendation Systems”, PhD Thesis, IMT Institute for Advanced Studies, Lucca Italy, 2011.

Dadone P. “Design Optimization of Fuzzy Logic Systems”, PhD Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 2001.

Bölgen M. “Fuzzy logic and data mining technıques in evaluating of credit risks of companies. Master Thesis”, Graduate School of Natural and Applied Sciences of Dokuz Eylül University, İzmir, 2010.

Esteves R.M. “Data fusion algorithms for assessing sensors’ accuracy in an oil production well - A Bayesian approach”, MSc Thesis, University of Stavanger Faculty of Science and Technology, 2009.