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, was 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 chat 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. / Graduation date: 1997
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/34052 |
| Date | 21 July 1994 |
| Creators | Heaberlin, Joan Oylear |
| Contributors | Robinson, Alan H. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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