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C. Inferential Sensing and Virtual Measurements

INTEGRATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE AND DIAGNOSTICS. 2. C. Inferential Sensing and Virtual Measurements. Inferential sensing is often used to measure variables that cannot be measured directly.

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C. Inferential Sensing and Virtual Measurements

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  1. INTEGRATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE AND DIAGNOSTICS 2

  2. C. Inferential Sensing and Virtual Measurements • Inferential sensing is often used to measure variables that cannot be measured directly. • An inferential sensing system is defined as an instrumentation system which infers values of complex process variables by integrating information from multiple sensors.

  3. For instance, reactivity in nuclear power plants must be calculated from measurements and reactor design parameters. • Neural networks can be trained to map almost instantaneously appropriate input variables into the desired output, e.g., reactivity.

  4. Inferential sensors, which incorporate a neural network for process modeling, can provide estimates of process variables that are usually measured off-line or through analytical laboratory instruments (e.g., such as the chemical composition of fluids). • One included the inferential measurement of the flow of feedwater to the steam generators in a nuclear power plant after the feedwaterventuri had been cleaned and calibrated.

  5. Since the thermal power calibration of a nuclear power plant is directly dependent on the steam generator feedwater flow, an erroneous high reading gives a calculated power level that is higher than actual. • Since nuclear power plants are usually licensed to a limiting thermal power rating, an erroneously high power level measurement effectively derates the plant.

  6. Virtual measurement. • Through such virtual measurements, the values of (unmeasurable) monitored variables with operational significance - e.g., • performance, • valve position, or • availability can be evaluated.

  7. This methodology was applied to the High Flux Isotopes Reactor at Oak Ridge National Laboratory to evaluate the coolant control valve position.

  8. D. Nuclear Power Plant Transients & Faults • When a nuclear power plant is operating normally, the readings of the instruments in a typical control room form a pattern (or unique set) of readings that represents a normal state of the plant or system.

  9. When a disturbance occurs, the instrument readings undergo a transition to a different pattern, representing a different state that may be normal or abnormal, depending upon the nature of the disturbance. • The fact that the pattern of instrument readings undergoes a transition to a new state is sufficient to identify the fault or cause of the transient.

  10. Benefits of this system • Almost immediate identification of plant transients (and associated faults), • Ability to take mitigating action (e.g., power runback) before trip, if appropriate, and • Identify and quantity very small leaks in the primary and secondary systems from monitoring control room instrumentation only.

  11. E. Increase in Thermal Power through Monitoring Reactor Core Parameters • The concept of increasing the licensed thermal output of the corethrough the use of a core monitoring system was approved in principle - when they licensed the Arkansas Nuclear One, Unit 2 plant (ANO-2) over two decades ago.

  12. A work in South Korea using data from a KEPCO (Korean Electric Power Company) nuclear power plant indicates that it is possible to monitor thermal margin (i.e., the difference between the predicted DNBR and the limiting DNBR) using a neural network model.

  13. DESIGN • The system will receive data from a data acquisition system or of a nuclear power plant, or from a nuclear power plant simulator. • Data will be introduced simultaneously to all five modules and processed, to the extent possible, in real time. • Plant status and other results will be displayed to the user through the graphical interface.

  14. General System Architecture

  15. Source: R. E. Uhrig, J. W. Hines, and W. Nelson, “Integration of Artificial Intelligence Systems for Nuclear Power Plant Surveillance And Diagnostics”, Scientific Research Journal, 2007. Download: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.36.717 Source: ftp://www.engr.utk.edu/pub/hines/HALDNMSP_32.pdf

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