A knowledge-based approach for monitoring and situation assessment at nuclear power plants.

摘要

An approach for developing a computer-based aid to assist in monitoring and assessing nuclear power plant status during situations requiring emergency response has been developed. It is based on the representation of regulatory requirements and plant-specific systems and instrumentation in the form of hierarchical rules. Making use of inferencing techniques from the field of artificial intelligence, the rules are combined with dynamic state data to determine appropriate emergency response actions.; In a joint project with Portland General Electric Company, a prototype system, called EM-CLASS, has been created to demonstrate the knowledge-based approach for use at the Trojan Nuclear Power Plant. The knowledge domain selected for implementation addresses the emergency classification process that is used to communicate the severity of the emergency and the extent of response actions required. EM-CLASS was developed using Personal Consultant Plus (PCPlus), a knowledge-based system development shell from Texas Instruments which runs on IBM-PC compatible computers. The knowledge base in EM-CLASS contains over 200 rules.; The regulatory basis, as defined in 10 CFR 50, calls for categorization of emergencies into four emergency action level classes: (1) notification of unusual event, (2) alert, (3) site area emergency, and (4) general emergency. Each class is broadly defined by expected frequency and the potential for release of radioactive materials to the environment. In a functional sense, however, each class must be ultimately defined by a complex combination of in-plant conditions, plant instrumentation and sensors, and radiation monitoring information from stations located both on- and off-site. The complexity of this classification process and the importance of accurate and timely classification in emergency response make this particular application amenable to an automated, knowledge-based approach.; EM-CLASS has been tested with a simulation of a 1988 Trojan Nuclear Power Plant emergency exercise and was found to produce accurate classification of the emergency using manual entry of the data into the program.