Değişken Hızlı Soğutma Sisteminin Analizi İçin Veri Madenciliği Yaklaşımı

Abstract

Bu çalışmanın amacı, veri Madenciliği tekniği kullanılarak değişken hızlı kompresörlü deneysel bir soğutma sisteminin performansının modellenmesidir. Soğutma sisteminin kapasitesinin değiştirilmesi için, Soğutma sistemlerinin kapasitesini değiştirmenin en iyi yöntemlerinden birisi kompresör motor hızının bir frekans invertörü ile kontrol edilmesidir. Bu amaçla, kompresör elektrik motorunun hızının frekans invertörü ile ayarlanabildiği bir deneysel soğutma sistemi kurulmuştur. Deneyler elektrik motorunun frekansının 35 Hz ile 50 Hz aralığında yapılmıştır. Sistem performansının belirlenmesi deneysel ölçümlerden alınan gerçek veriler kullanılarak veri madenciliği tekniği uygulanmıştır. Sonuç olarak, farklı kompresör frekansları ve soğutma yüklerindeki sistem karakteristiklerini belirlenmesinde birçok deney yapmak yerine veri madenciliği tekniğinin kullanılmasının uygun olduğu tespit edilmiştir.

Keywords

• Veri madenciliği
• Değişken hız
• Soğutma
• Frekans

Data Mining Approach for Analysis of Variable Speed Refrigeration System

Abstract

The aim of this study is to carry out performance modeling of an experimental refrigeration system driven by variable speed compressor using Data Mining techniques with small data sets. In order to vary the capacity of the refrigeration systems, one of the best methods is controlling the rotational speed of the compressor motor with a frequency inverter. For this aim, an experimental refrigeration system is setup with a frequency inverter for controlling the speed of compressor electric motor. The experiments are made for 35 Hz to 50 Hz electric motor frequencies. Data mining technique is applied to determine the system performance parameters using actual data obtained from the measurements. From the results, it is observed that data mining procedure is suitable for forecasting the system characteristics for different compressor frequencies and cooling loads instead of making several experiments

Keywords

• Data mining
• Variable speed
• Refrigeration
• Frequency

References

1. Akdag, U., Komur, M.A., Ozguc, A.F., 2009. Estimation of heat transfer in oscillating annular flow using artifical neural Networks. Advances in Engineering Software, 40, 864-870.
2. Alam S, Kaushik S.C., Garg S.N., 2009. Assessment of diffuse solar energy under general sky condition using artificial neural network. Applied Energy, 86, 554–64.
3. Aprea, C., Rossi, F., Greco, A. Renno, C., 2003. Refrigeration plant exergetic analysis varying the compressor capacity. International Journal of Energy Research, 27, 653-669.
4. Aprea, C., Renno, C., 2004. An experimental analysis of a thermodynamic model of a vapour compression refrigeration plant on varying the compressor speed. International Journal of Energy Research, 28, 537- 549.
5. Aprea, C., Mastrullo, R., Renno, C., Vanoli, G. P., 2004a. An evaluation of R22 substitutes performances regulating continuously the compressor refrigeration capacity. Applied Thermal Engineering, 24, 127-139.
6. Aprea, C., Mastrullo, R. Renno, C., 2004b. Fuzzy control of the compressor speed in a refrigeration plant. International Journal of Refrigeration, 27, 639- 648.
7. Arcaklioglu, E., Erisen, A., Yilmaz, R., 2004. Artificial neural network analysis of heat pumps using refrigerant mixtures. Energy Conversion and Management, 45, 1917-1929.
8. Buzelin, L.O.S., Amico, S.C., Vargas, J.V.C., Parise, J.A.R., 2005. Experimental development of an intelligent refrigeration system. International Journal of Refrigeration, 28, 165-175.

9. Çengel, A.Y., Boles, M.A., 1994. Thermodynamics: an engineering approach. McGraw-Hill, New York.
10. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., 2000. CRISP- DM 1.0 Step-by-step data mining guide, SPSS Inc.
11. Chen, M.S., Han, J., Yu, P.S., 1996. Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8, 6, 866-883.
12. Chiu S.H, Chen, C.C., Lin, T.H., 2008. Using support vector regression to model the correlation between the clinical metastases time and gene expression profile for breast cancer. Artificial Intelligence in Medicine, 44, 221–31.
13. Cunnigham, S.J., Holmes, G., 1999. Developing innovative application in agriculture using data mining. In: Proceedings of the southeast Asia regional computer confederation conference.
14. Dincer, I., Rosen, M.A., 2007. Exergy: Energy, Environment and Sustainable Development. Elsevier Science, Oxford, UK, 472 p.
15. Friedman, J.H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 367–78. Goodwin, L., VanDyne, M., Lin, S., Talbert, S., 2003. Data mining issues and opportunities for building nursing
16. Informatics, 36, 379–88. Journal of
17. Biomedical Hall, M., Holmes, G., Frank, E., 1999. Generating rule sets from model trees. In: Proceedings of the twelfth Australian joint conference on artificial intelligence. 1–12, Sydney, Australia.
18. Harding, J.A., Shahbaz, M., Srivinas, Kusiak, A., 2006. Data Mining in Manufacturing: Review. Journal of Manufacturing Science and Engineering, 128, 969- 976.
19. Kalogirou, S.A., 2006. Artificial Intelligence in Energy And Renewable Energy Systems. Nova Science Publishers Inc, pp. 471.
20. Li, D.C., Liu, C.W., 2009. A neural network weight determination model designed uniquely for small data set learning. Expert Systems with Applications, 36, 9853-9858.
21. Licamele, K., Getoor, L., 2006. Predicting protein– protein interactions using relational features. In: Proc. of ICML workshop on statistical network analysis.
22. Read, B.J., 1999. Data mining and Science? Knowledge discovery in science as opposed to business. In 12th ERCIM workshop on database research. Amsterdam
23. Sozen, A., Ozalp, M., Arcaklioglu, E., 2007. Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network. Applied Thermal Engineering, 27, 551-559.
24. Weka, 2012. http://www.cs.waikato.ac.nz/ml/weka/. Accessed: 08.04.2012.
25. Wu, S., Zhao, X., Shao, H., Ren, D., 2004. Cold rolling process data analysis based on Svm. In: Proceedings of the third international conference on machine learning and cybernetics, Shanghai.
26. Zhang, Y., Wu, X., 2010. Integrating Induction and Deduction for Noisy Data Mining. Information Sciences, 180, 2663-2673.
27. Zhang, J., Xu, Q., 2011. Cascade refrigeration system synthesis based on exergy analysis. Computers & Chemical Engineering, 35, 9, 1901–1914.