Experimental Investigation and Fuzzy Logic Modelling of Performance Hydroxy Dry Cell with Different Plate Combination

Öz

In this study, hydroxy (HHO) dry cell with different plate combination performances in terms of current and temperature were experimentally investigated and modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters plate number and time; output parameters current, temperature were described by RBMTF if-the rules. The dimensions of the plates were          9x9 cm², 10x10 cm² and 11x11 cm². Current and temperature were measured for the different plate combination. Tap water was used in the experiments and the system was set to 5 minutes. For each combination, new cells were prepared. Experimental data which obtained for current and temperature according to combination and time were used in the training step. Numerical parameters of input and output variables were fuzzificated as linguistic variables: very very low (L1), very low (L2), low (L3), negative medium (L4), medium (L5), positive medium (L6), high (L7), very high (L8) and very very high (L9) linguistic classes. With the linguistic variables used, rules were obtained for this system. The comparison between experimental data and RBMTF is done by using statistical methods like the coefficient of multiple determinations (R²). The actual values and RBMTF results indicated that RBMTF can be successfully used in HHO dry cell.

Anahtar Kelimeler

• HHO Dry Cell,Plate Combination,Fuzzy Logic

Kaynakça

1. E. Leelakrishnan, N. Lokesh, H. Suriyan, “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, 2013.
2. R. Cameron, “Effects of on-board HHO and water injection in a diesel generator,” Bachelor of Engineering Research Project, University of Southern Queensland Faculty of Engineering and Surveying, 2012.
3. S. Yadav Milind, S.M. Sawant, “Investigations on oxyhydrogen gas and producer gas as alterntive fuels on the performance of twin cylinder diesel engine,” International Journal of Mechanical Engineering and Technology (IJMET), vol.2, pp.85-93, 2011.
4. A. Işıktaş, K. Dincer, S. Ata,”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.
5. S. Ata, K. Dincer, “Rule-based Mamdani-type fuzzy modeling of performance proton exchange membrane fuel cell with carbon nanotube,” 15th International Multidisciplinary Scientific GeoConference SGEM 2015, Bulgaria, 487-494, 2015.
6. S. Ata, K. Dincer, “Anot Tarafı Karbon Nanotüp İle Kaplanmış PEM Yakıt Hücresi Performansının Bulanık Mantık Yöntemiyle Modellenmesi,” Ulusal Hidrojen Teknolojileri Kongresi UHTEK-2015, İstanbul, 2015.
7. S. Ata, “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.
8. S. Ata, K. Dincer, “Bulanık Mantık Yaklaşımıyla Yakıt Hücresi Performansının Belirlenmesi,” V. Ulusal Güneş ve Hidrojen Enerjisi Kongresi, Eskişehir, 2016.

9. S. Ata, K. Dincer, “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.
10. A. Özek, M. Sinecen, “Klima sistem kontrolünün bulanık mantık ile modellemesi,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt 10, Sayı 3, 2004.
11. Y. Kim, S. Kim, “An Electrical Modelling and Fuzzy Logic Control of a Fuel Cell Generation System,” IEEE Transactions on Energy Conversion, vol. 14, no. 2, pp. 239-244, 1999.
12. S.W. Tong, D.W. Qian, J.J. Fang, H.X. Li, “Integrated Modelling and Variable Universe Fuzzy Control of a Hydrogen-Air Fuel Cell System,” Int. J. Electrochem. Sci., vol.8, pp. 3636-3652, 2013.
13. A.E. Tiryaki, R. Kazan, “Bulaşık makinesinin bulanık mantık ile modellenmesi,” Mühendis ve Makine, Cilt 48, Sayı 565, ss. 3-8, 2007.
14. A. Ates, T. Akbiyik, K. Dincer, “The fuzzy logic modelling of diesel engine emissions using fuel mixed with different ratios of hydrogen,” International Journal of Automotive Engineering and Technologies, vol. 2, issue 4, pp. 111 – 117, 2013.
15. M. Bölgen, “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, Turkey, 2010.
16. M. Tosun, K. Dincer, S. Baskaya, “Rule based Mamdani-type fuzzy modelling of thermal performance of wall types most used in residential buildings in Turkey,” 10th International Multidisciplinary Scientific Geoconference SGEM 2010, Albena, Bulgaria, 2010.
17. G. Önal, K. Dincer, S. Yayla, Y. Yılmaz, A.S. Ersoyoğlu, “Pt/C Coating for Proton Exchange Membrane Fuel Cell (PEMFC) and Rule-Based Mamdani-Type Fuzzy Modeling of PEMFC Performance,” International Journal of Mining, Metallurgy & Mechanical Engineering, vol.3, no.3, pp.122-128, 2015.