Implementation of an integrated on-line process surveillance and diagnostic system at the Halden reactor project: MOAS

MOAS is an integrated on-line process surveillance and diagnostic system that uses several different models for knowledge acquisition and diagnostic reasoning, such as goal-tree success-tree model, process monitor trees, and sensor failure diagnosis trees. Within these models, the knowledge of the process and its operation, including deep knowledge, like mass balance or controller algorithm, is incorporated. During an extensive review, made as part of the integrated diagnosis system project of the Halden reactor project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used for the NORS (Nokia Research Simulator) process at the Halden man-machine laboratory HAMMLAB of the OECD Halden reactor project. The performance tests of MOAS, implemented in G2 real-time expert system shell, show that MOAS successfully carries out its intended functions, i.e. quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.