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http://dx.doi.org/10.5516/NET.2008.40.1.069

A SOFT-SENSING MODEL FOR FEEDWATER FLOW RATE USING FUZZY SUPPORT VECTOR REGRESSION  

Na, Man-Gyun (Department of Nuclear Engineering, Chosun University)
Yang, Heon-Young (Department of Nuclear Engineering, Chosun University)
Lim, Dong-Hyuk (Department of Nuclear Engineering, Chosun University)
Publication Information
Nuclear Engineering and Technology / v.40, no.1, 2008 , pp. 69-76 More about this Journal
Abstract
Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.
Keywords
Feedwater Flow Rate Monitoring; Fuzzy C-means Clustering; Fuzzy Support Vector Regression; Genetic Algorithm; Soft-sensing; Subtractive Clustering;
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Times Cited By Web Of Science : 4  (Related Records In Web of Science)
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