Monitoring and uncertainty analysis of feedwater flow rate using data-based modeling methods

IEEE Transactions on Nuclear Science

Volumn 56, Issue 4, 2009, Pages 2426-2433

  Yang, Heon Young ^a   Lee, Sung Han ^a   Na, Man Gyun ^a  

^a Chosun University   (South Korea)

Author keywords

Data based model; Feedwater flow rate; Fuzzy inference; Genetic algorithm; Sequential probability ratio test; Subtractive clustering; Support vector regression (SVR)

Indexed keywords

DATA-BASED MODEL; FEEDWATER FLOW RATE; SEQUENTIAL PROBABILITY RATIO TEST; SUBTRACTIVE CLUSTERING; SUPPORT VECTOR REGRESSION (SVR);

CLUSTER ANALYSIS; FEEDWATER ANALYSIS; FLOW MEASUREMENT; FLOW RATE; FLOWMETERS; FUZZY INFERENCE; NUCLEAR ENERGY; NUCLEAR INDUSTRY; NUCLEAR POWER PLANTS; PLANT STARTUP; PRESSURIZED WATER REACTORS; PROBABILISTIC LOGICS; REGRESSION ANALYSIS; SEQUENTIAL SWITCHING; STATISTICAL TESTS; TEST FACILITIES; UNCERTAINTY ANALYSIS;

DATA FLOW ANALYSIS;

EID: 69549120707     PISSN: 00189499     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNS.2009.2022366     Document Type: Conference Paper

References (27)

1. A. M. C. Chan and A. K. Ahluwalia, Feedwater flow measurement in U.S. nuclear power generation stations Electric Power Res. Inst., EPRI TR-101388, Nov. 1992.
2. J. Regan and H. Estrada, "The elements of uncertainty in feedwater flow measurements with three types of instruments," NPIC&HMIT 2000. Washington, DC, Nov. 2000.
3. K. Kavaklioglu and B. R. Upadhyaya, "Monitoring feedwater flow rate and component thermal performance of pressurized water reactors by means of artificial neural networks," Nucl. Technol., vol.107, pp. 112-123, Jul. 1994.
4. G. Y. Heo, S. S. Choi, and S. H. Chang, "Feedwater flowrate estimation based on the two-step de-noising using the wavelet analysis and an autoassociative neural network," Nucl. Eng. Tech., vol.31, no.2, pp. 192-201, Apr. 1999.
5. A. V. Gribok, I. Attieh, J. W. Hines, and R. E. Uhrig, "Regularization of feedwater flow rate evaluation for Venturi meter fouling problem in nuclear power plants," NURETH-9. San Francisco, CA, Oct. 3-8, 1999.
6. M. G. Na, Y. J. Lee, and I. J. Hwang, "A smart software sensor for feedwater flow measurement monitoring," IEEE Trans. Nucl. Sci., vol.52, no.6, pp. 3026-3034, Dec. 2005.
7. M. G. Na, I. J. Hwang, and Y. J. Lee, "Inferential sensing and monitoring for feedwater flowrate in pressurized water reactors," IEEE Trans. Nucl. Sci., vol.53, no.4, pp. 2335-2342, Aug. 2006.
8. M. G. Na, H. Y. Yang, and D. H. Lim, "A soft-sensing model for feedwater flow rate using fuzzy support vector regression," Nucl. Eng. Tech., vol.40, no.1, pp. 69-76, Feb. 2008.
9. D.-J. Choi and H. Park, "A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process," Water Res., vol.35, no.16, pp. 3959-3967, 2001.
10. N. Régnier, G. Defaye, L. Caralp, and C. Vidal, "Software sensor based control of exothermic batch reactors," Chem. lEng. Sci., vol.51, no.23, pp. 5125-5136, 1996.
11. S. Linko, J. Luopa, and Y.-H. Zhu, "Neural networks as 'software sensors' in enzyme production," J. Biotechnol., vol.52, no.3, pp. 257-266, 1997.
12. A. Chéruy, "Software sensors in bioprocess engineering," J. Biotechnol., vol.52, no.3, pp. 193-199, 1997.
13. M. H. Masson, S. Canu, Y. Grandvalet, and A. Lynggaard-Jensen, "Software sensor design based on empirical data," Ecol. Model., vol.120, no.2-3, pp. 131-139, 1999.
14. J. Garvey, D. Garvey, R. Seibert, and J. W. Hines, "Validation of on-line monitoring techniques to nuclear plant data," Nucl. Eng. Tech., vol.39, no.2, pp. 149-158, Apr. 2007.
15. G. Y. Heo, "Condition monitoring using empirical models: Technical review and prospects for nuclear applications," Nucl. Eng. Tech., vol.40, no.1, pp. 49-68, Feb. 2008.
16. V. Kecman, Learning and Soft Computing. Cambridge, MA: MIT Press, 2001.
17. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
18. D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods. New York: Academic, 1982.
19. S. L. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Systems, vol.2, pp. 267-278, 1994.
20. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Systems, Man, Cy-bern., vol.SMC-1, no.1, pp. 116-132, Jan./Feb. 1985.
21. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison Wesley, 1989.
22. J. W. Hines and B. Rasmussen, "Online sensor calibration monitoring uncertainty estimation," Nucl. Technol., vol.151, pp. 281-288, Sep. 2005. (Pubitemid 41158643)
23. J. W. Hines and R. Seibert, Technical review of on-line monitoring techniques for performance assessment, Volume 1: State-of-the-art U.S. Nucl. Regul. Comm., Washington, DC, NUREG/CR-6895, 2006.
24. J. W. Hines, D. Garvey, R. Seibert, and A. Usynin, Technical review of on-line monitoring techniques for performance assessment, Volume 2: Theoretical issues U.S. Nucl. Regul. Comm., Washington, DC, NUREG/CR-6895, 2007.
25. J. W. Hines, J. Garvey, D. Garvey, and R. Seibert, Technical review of on-line monitoring techniques for performance assessment, Volume 3: Limiting case studies U.S. Nucl. Regul. Comm., Washington, DC, NUREG/CR-6895, 2008.
26. R. Tibshirani, "A comparison of some error estimates for neural network models," Neural Comput., vol.8, pp. 152-163, 1996.
27. A. Wald, Sequential Analysis. New York: Wiley, 1947.