Online transient behavior prediction in nuclear power plants

This article describes an online simulator that works on a fuzzy network model to predict transient behavior of a nuclear power plant. The model can be a causal fuzzy, qualitative fuzzy, or quantitative fuzzy network depending on which simulation, fuzzy, qualitative fuzzy, or quantitative fuzzy simulation, is requested. The networks are derived from underlying mathematical models about the system. This deep design knowledge empowers the simulator to dotransient behavior prediction. Specifically, each network contains properly grained fuzzy operators to relate system variables together. The operators are represented as mapping tables so that time-consuming fuzzy computations can be reduced to efficient table-lookup during online simulation. In addition, the simulator is equipped with a mechanism to dynamically decide a best time increment between two simulation steps, which makes the simulation even more efficient. Finally, the simulator includes analysis knowledge, operational knowledge, and input-output records to support diagnosis, prediction, and learning capabilities, which allow the simulator to make fast responses, answer anytime status queries, propose reasonable solutions, and perform adaptive modeling. It has been shown that it can successfully predict transient behavior of the temperature and pressure of a steam generator with ruptured U-tubes undergoing the operator recovery process in a nuclear power plant. This helps the plant operator immensely in correctly making and successfully carrying out emergency response plans to deal with critical accidents, whether or not they are unprecedented.

[1]  Zita A. Vale,et al.  Sparse: An Intelligent Alarm Processor and Operator Assistant , 1997, IEEE Expert.

[2]  B. E. Boyack,et al.  TRAC PF1/MOD1 calculations and data comparisons for mist feed and bleed and steam generator tube rupture experiments , 1988 .

[3]  Ezra Elias,et al.  A DSNP Movable Boundary U-Tube Steam Generator Model , 1991 .

[4]  Johan de Kleer,et al.  Readings in qualitative reasoning about physical systems , 1990 .

[5]  Françoise Decortis,et al.  COSIMO: A cognitive simulation model of human decision making and behavior in accident management of complex plants , 1992, IEEE Trans. Syst. Man Cybern..

[6]  Bernard P. Zeigler,et al.  Endomorphic modeling concepts for high-autonomy architectures , 1992, Appl. Artif. Intell..

[7]  Jerry Banks,et al.  DESIGN OF A KNOWLEDGE-BASED ON-LINE SIMULATION SYSTEM TO CONTROL A MANUFACTURING SHOP FLOOR , 1992 .

[8]  Z. A. Sabri,et al.  Automation of Nuclear Power Plants , 1990 .

[10]  Soon Heung Chang,et al.  Development strategies of an expert system for multiple alarm processing and diagnosis in nuclear power plants , 1993 .

[11]  Juichiro Itoh,et al.  An intelligent man-machine system for future nuclear power plants , 1994 .

[12]  Alois Höld.  UTSG-2—A Theoretical Model Describing the Transient Behavior of a Pressurized Water Reactor Natural-Circulation U-Tube Steam Generator , 1990 .

[13]  Kjell Haugset,et al.  Evaluation Program of the Integrated Surveillance and Control System ISACS—An Advanced Control Room Prototype , 1994 .

[14]  Hiroo Kanamaru,et al.  Development of a Parallelism Analysis Integrated System for a Fast-Running Pressurized Water Reactor Plant Predictive Simulator , 1991 .

[15]  Jan Eric Larsson,et al.  Guardian: An Intelligent Autonomous Agent for Medical Monitoring and Diagnosis , 1998, IEEE Intell. Syst..

[16]  Richard R. Schultz,et al.  RELAP5/MOD3 Code Manual , 1992 .

[17]  Jacky Montmain,et al.  A supervision support system for industrial processes , 1993, IEEE Expert.

[18]  D. R. Liles,et al.  TRAC-PF1/MOD1: an advanced best-estimate computer program for pressurized water reactor thermal-hydraulic analysis , 1986 .

[19]  F.E. Cellier,et al.  High-autonomy control of space resource processing plants , 1993, IEEE Control Systems.

[20]  Toshio Ida,et al.  Designing a knowledge-based operator support system for practical applications , 1991 .

[21]  Robert E. Uhrig.  Opportunities for automation and control of the next generation of nuclear power plants , 1989 .

[22]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[23]  Panos J. Antsaklis,et al.  Towards intelligent autonomous control systems: Architecture and fundamental issues , 1989, J. Intell. Robotic Syst..

[24]  Greg L. Zacharias,et al.  A computational situation assessment model for nuclear power plant operations , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[25]  Cheng-Seen Ho,et al.  HASLEARN: a highly autonomous system with learning behavior , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[26]  Yoon On Yang,et al.  A diagnostic expert system for the nuclear power plant based on the hybrid knowledge approach , 1989 .

[27]  Adam Farquhar,et al.  Modeling and Simulation of a Complex Industrial Process , 1997, IEEE Expert.

[28]  Jerzy W. Rozenblit,et al.  Design for high autonomy: An overview , 1992, Appl. Artif. Intell..

[29]  Rosanna Costaguta,et al.  The knowledge-based simulation , 1998, Proceedings 31st Annual Simulation Symposium.

[30]  Borut Mavko,et al.  Feed-and-bleed procedure mitigating the consequences of a steam generator tube rupture accident , 1995 .

[31]  C.M. Mitchell,et al.  ALLY: an operator's associate for cooperative supervisory control systems , 1993, IEEE Trans. Syst. Man Cybern..

[32]  Ioannis A. Papazoglou,et al.  A decision support system for emergency response to major nuclear accidents , 1994 .

[33]  José del R. Millán,et al.  Autonomous Mobile Robots and Teleoperation , 1992 .

[34]  Brian Tolley.  Teleman: A European Communities Robotics Programme for the Nuclear Industry , 1992 .

[35]  Bonnie L. Webber,et al.  TraumaTIQ: Online Decision Support for Trauma Management , 1998, IEEE Intell. Syst..

[36]  C. Chieng,et al.  A two-fluid two-phase model for thermal-hydraulic analysis of a U-tube steam generator , 1993 .