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Approaches for AI  no specific appr . Tools for AI

INTEGRATION OF ARTIFICIAL INTELLIGENCE [ AI ] SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE & DIAGNOSTICS. AI. Artificial Intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it . ‘ the study and design of intelligent agents ’

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Approaches for AI  no specific appr . Tools for AI

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  1. INTEGRATION OF ARTIFICIAL INTELLIGENCE [AI] SYSTEMS FOR NUCLEAR POWER PLANTSURVEILLANCE&DIAGNOSTICS

  2. AI • Artificial Intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it. • ‘the study and design of intelligent agents’ • The central problems of AI include such traits as • reasoning, • knowledge, planning, • learning, communication, • perception and • the ability to move and manipulate objects

  3. Approaches for AI  no specific appr. • Tools for AI • Optimization/ evolutionary computation [genetic prog.] • Logic [heuristics – e.g., using a rule of thumb, an educated guess, an intuitive judgment, or common sense.] • Probabilistic methods for uncertain reasoning [Bayesian network, HMM, Kalman filter] • Classifiers & statistical learning methods – machine learning, pattern matching, pattern recognition • Neural network [NN/ANN] • Control theory

  4. A nuc power plant ~ • may utilize various methodologies of artificial intelligence - expert systems, - neural networks, - fuzzy systems and - genetic algorithms • to enhance the performance • Safety, • efficiency, • reliability, and • Availability ~ of nuclear power plants.

  5. design, • construct operate, • test, and • evaluate  a prototype integrated monitoring and diagnostic system for a nuclear power plant

  6. Investigations and studies have included • instrumentation surveillance and • calibration validation, • inferential sensing to calibration of feedwaterventuriflow,

  7. d) thermodynamic performance modeling with iterative improvement of plant heat rate, f) diagnosis of nuclear power plant transients, and g) increase in thermal power through monitoring of DNBR (Deviation from Nucleate Boiling Regime).

  8. A Typical General System Architecture

  9. A. Instrumentation, Surveillance and Calibration Verification • Traditional approaches to instrument calibration at nuclear power plants, especially instruments inside containment, are expensive in terms of labor, money, and radiation exposure. • These calibrations require that the instrument be taken out of service and be falsely-loaded to simulate actual in-service stimuli.

  10. On-line monitoring systems for calibration will allow utilities to determine when recalibration is needed, thereby reducing the frequency of calibration and the efforts necessary to assure the instruments continue to be calibrated properly. • Nuclear Regulatory Commission (NRC) requirements On-line vs. off-line

  11. Benefits • Assurance that sensors are in calibration, • Ability to detect intermittent failures and noisy sensors, • Availability of a surrogate sensor reading if needed, • Ability to identify which sensor has drifted, became noisy, or failed, and • Ability to differentiate between process change and sensor failure.

  12. B. On-Line Thermodynamic Performance Modeling and Improvement • An expert system combined with thermodynamic modeling • to provide a reference heat rate is used to advise operators on steps to be taken to improve plant the heat rate.

  13. A potential drawback of this approach is that it, • Is usually dependent upon system models based on ideal conditions, and • Often involves empirical relationships, and • Approximations of the actual processes, and • Linearizations of nonlinear phenomena. Empirical  source of knowledge acquired by means of observation or experimentation

  14. In a study, a nonlinear thermodynamic process model was obtained using a neural network, trained on actual thermodynamic measurements from the Sequoyah Nuclear Power Plant over a one-year period. • Another utilizes genetic algorithms and principal component analysis [PCA] to identify the optimal grouping of input parameters to the neural network models.

  15. An on-line heat rate monitor based on a neural network model can be utilized to determine which variables are the most important ones to adjust and whether they should be increased or decreased. • If one or more of these variables are adjusted, the resultant change in heat rate can he monitored with the neural network model.

  16. Then another sensitivity measurement can be performed to indicate the next variable or set of variables that should be adjusted. • This process can be continued on an iterative basis to achieve optimal efficiency under all existing or changing conditions, e.g.,  changing load,  fouling of heat transfer surfaces,  removal of components from service,  changing air or river water temperature, etc.

  17. 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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