Monitoring and Uncertainty Analysis of Feedwater Flow Rate Using Data-Based Modeling Methods

The Venturi flow meters that are being used to measure the feedwater flow rate in most pressurized water reactors are confronted with fouling phenomena, resulting in an overestimation of the flow rate. In this paper, we will therefore develop two soft-sensing models based on a fuzzy inference system and support vector regression for online prediction of the feedwater flow rate. The data-based models are developed using a training data set and a verification data set, and validated using an independent test data set. These data sets are divided from the startup data of Yonggwang Nuclear Power Plant Unit 3. The data for training the data-based models is selected with the aid of a subtractive clustering scheme because informative data increases the learning effect. The uncertainty of the data-based models is analyzed using 100 sampled training and verification data sets, and a fixed test data set. The prediction intervals are very small, which means that the predicted values are very accurate. The root mean square error and relative maximum error of the models were quite small. Also, the residual signal between the measured value and the estimated value is used to determine the overmeasure due to the fouling phenomena by a sequential probability ratio test which consequently monitors the existing feedwater flow meters.