Application of a neural network based feedwater controller to helical steam generators

In current generation pressurized water reactors (PWRs), the control of steam generator level experiences challenges over the full range of plant operating conditions. These challenges can be particularly troublesome in the low power range where the feedwater is highly subcooled and minor changes in the feed flow may cause oscillations in the SG level, potentially leading to reactor trip. The IRIS reactor concept adds additional challenges to the feedwater control problem as a result of a steam generator design where neither level or steam generator mass inventory can be measured directly.Neural networks have demonstrated capabilities to capture a wide range of dynamic signal transformation and non-linear problems. In this paper a detailed engineering simulation of plant response is used to develop and test neural control methods for the IRIS feedwater control problem. The established neural network mass estimator has demonstrated the capability to predict the steam generator mass under transient conditions, especially at low power levels, which is considered the most challenging region for a full range feed water controller.