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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’
• The central problems of AI include such traits as
–
–
–
–
–
reasoning,
knowledge, planning,
learning, communication,
perception and
the ability to move and manipulate objects
• 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
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.
• design,
• construct operate,
• test, and
• evaluate
 a prototype integrated monitoring and
diagnostic system for a nuclear power plant
Investigations and studies have included
a) instrumentation surveillance and
b) calibration validation,
c) inferential sensing to calibration of
feedwater venturi flow,
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).
A Typical General System Architecture
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.
• 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
Benefits
a) Assurance that sensors are in calibration,
b) Ability to detect intermittent failures and noisy
sensors,
c) Availability of a surrogate sensor reading if
needed,
d) Ability to identify which sensor has drifted,
became noisy, or failed, and
e) Ability to differentiate between process change
and sensor failure.
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.
• 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
• 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.
• 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.
• 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.
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