Monitoring Feedwater Flow Rate and Component Thermal Performance of Pressurized Water Reactors by Means of Artificial Neural Networks

Abstract

The fouling of venturi meters, used for steam generator feedwater flow rate measurement in pressurized water reactors (PWRs), may result in unnecessary plant power derating. On-line monitoring of these important instrument channels and the thermal efficiencies of the balance-of-plant components are addressed. The steam generator feedwater flow rate and thermal efficiencies of critical components in a PWR are estimated by means of artificial neural networks. The physics of these systems and appropriate plant measurements are combined to establish robust neural network models for on-line prediction of feedwater flow rate and thermal efficiency of feedwater heaters in PWRs. A statistical sensitivity analysis technique was developed to establish the performance of this methodology.