Stochastic regularization of feedwater flow rate evaluation for the venturi meter fouling problem in nuclear power plants

摘要

Inferential sensing is a method that can be used to evaluate the value of physical system parameters, such as flows, pressures or temperatures based on a set of related measurements. The most common method of inferential sensing uses mathematical models to infer a parameter value from a set of correlated sensor values. However, since inferential sensing is an inverse problem, it can produce inconsistent results due to minor perturbations in the data. This research shows that regularization techniques can be used in inferential sensing to produce consistent results. The important example of monitoring nuclear power plant feedwater flow rate is presented using data from Florida Power Corporation's Crystal River Nuclear Power Plant.