This research applies neural network technology to the monitoring and control of the feedwater system and resultant steam generator water level during startup and low power operations of large pressurized water reactors. During these operational modes and plant transients, control of the feedwater system is difficult. The neural network would be used to monitor the input signals used by the existing automatic control system and then "learn" the response characteristics of this system. In this manner, the neural network would be trained over a period of time to expect the normal response signals and detect any off-normal signals from the feedwater system operation. Personnel would be notified in advance of any off-normal activity. This neural network application would improve the control of the feedwater system during startup, low power operations, and plant transients. This would reduce the number of feedwater related reactor trips. During Phase I, a thorough study of the sensor requirements will be made to establish the neural network architecture. A prototype subset of the system will be simulated and demonstrated using an IBM PC-AT. Impell Corporation and Commonwealth Edison will participate in developing the system and evaluating the results. In Phase II, a full scale system will be developed to simulate and test the application of this technology to the feedwater control system.Anticipated Results/Potential Commercial Applications as described by the awardee:This project is expected to result in a neural network control system for feedwater control during startup and low power operations.
