5th holmug technical working group meeting, ringhals nuclear power plant site october 16th - 17th....
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5th HOLMUG 5th HOLMUG Technical Working Group MeetingTechnical Working Group Meeting, , Ringhals nuclear power plant site Ringhals nuclear power plant site
October 16th - 17th.October 16th - 17th.
on on “Nuclear Power Plant Control & Instrumentation”“Nuclear Power Plant Control & Instrumentation”
Super-Safety ConceptSuper-Safety Concept
in Nuclear Integrated Safety Managementin Nuclear Integrated Safety Management
An example of on-line monitoring application to An example of on-line monitoring application to
CRDM for operational safetyCRDM for operational safety
Adam Maria Gadomski, Massimo Sepielli, Corrado Antonio Kropp, Antonino RattoAdam Maria Gadomski, Massimo Sepielli, Corrado Antonio Kropp, Antonino Ratto
Italian National Research Agency ENEA, FPNItalian National Research Agency ENEA, FPNResearch Center Casaccia , Research Center Casaccia ,
0123 Roma, Italy0123 Roma, Italy
adam@casaccia.enea.it, sepielli @ casaccia.enea.itadam@casaccia.enea.it, sepielli @ casaccia.enea.it
ENEAHOLMUG, Oct. 15, 2008
- Stop to nuclear energy IS OVER in Italy AFTER 20 Stop to nuclear energy IS OVER in Italy AFTER 20 years from referedum in Nov. 1987years from referedum in Nov. 1987
- New national policy on Nuclear Energy New national policy on Nuclear Energy - New regulatory body (Agenzia Sicurezza Nucleare)New regulatory body (Agenzia Sicurezza Nucleare)- New nuclear agency (ENES replacing ENEA)New nuclear agency (ENES replacing ENEA)- New nuclear industry group (Ansaldo + Sogin..)New nuclear industry group (Ansaldo + Sogin..)
2008ENEA
Advanced researchAdvanced research responses relate to the integrated life cycle of nuclear plant systems:
- New rules for COLs – Production Technologies – New rules for COLs – Production Technologies –
- Safety and Safety and Economy of exploitation-Economy of exploitation-
ENEA/S Responses
Italian situation
IntroductionIntroductionHOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008
Super-Safety conceptSuper-Safety concept
ENEAHOLMUG, Oct. 15, 2008
Super-Safety (SS) –What is it?Super-Safety (SS) –What is it?
SS is the unified and complete supervision of critical systems,
its dynamic functions and consequences involved in the operators and managers decisions.
SS is a total protection extendend in time and space, as well as related to the cause-consequences propagation managed together in technological, cognitive and socio-organizational layers
SS has to satisfy current society requirements related to its self and the environment safety (on sustaiability level) New tasks New technologies
+ New social constrains
----------------------------------------- New RTD approach is necessary
SS Strategy SS Strategy (SSS)(SSS)
SAFETY: Four layers of safety buildingSAFETY: Four layers of safety building
The systemic socio-cognitive (top -down object-based goal-oriented approach) is applied to the modelling of the problem.
4 layers of safety building from the operator goal-oriented points of view:
• (1) natural safety, it employs only the safety properties of physical processes engaged in the system external functions.
• (2) critical safety; it is realized by the shut-down of the system functions under critical conditions. An automatic switch-off equipment is installed.
• (3) controlled safety; a supervision of safety-indicating variables and the model-based regulation of their control variables (in open and close loops) are realized.
• (4) super-safety; an integrated supervision of the controlled safety is performed, the models employed in the controlled safety layer can be modified according to the managerial preferences of the object/process owner or some external normative requirements.
An intrinsically safe nuclear technology is included in the safety analysis in
the above defined layers.
Operational SafetyOperational Safety
Allocation of nuclear On-Line Monitoring Strategy (OLMS) in frame of SSS
Operational Supper Safety
Policy-Making Safety
Technological Safety
Human-technology & Organizational Safety
Top-down Approach
… On-Line Monitoring Strategy ?
Operational Safety
On-Line Monitoring Strategy: On-Line Monitoring Strategy: Instrumentation, Controls, and Human-Instrumentation, Controls, and Human-
Machine-InterfaceMachine-InterfaceIntroductionIntroduction
Instrumentation, Controls, Instrumentation, Controls, and Human-Machine-Interface and Human-Machine-Interface ((ICHMIICHMI), are essential ), are essential enabling technologies that enabling technologies that strongly influence nuclear strongly influence nuclear power plants performance.power plants performance.
Plant sensor on-line Plant sensor on-line monitoring, data validation monitoring, data validation through soft-computing through soft-computing process and plant condition process and plant condition monitoring techniques would monitoring techniques would help identify plant sensors help identify plant sensors drift or malfunction and drift or malfunction and operator actions in addressing operator actions in addressing nuclear reactor control. On-nuclear reactor control. On-line recalibration can often line recalibration can often avoid intervene with manual avoid intervene with manual calibration or physical calibration or physical replacement of the drifting replacement of the drifting component.component.
Operational SafetyOperational Safety
MIND
Organization
Controlled Nuclear Plant
Control and Measurement System (in-core, …)
Computer Console System
Physical environment
Psycho-social environment
Operational SafetyOperational Safety :: Plant Context, Operator LevelPlant Context, Operator Level(system representation)
Cognitive Interactions
Human operator
Constrains
Machine Organization
Casaccia Research Center, May 24, 2005 A.M.Gadomski, M.Sepielli
Operational safety : Insight Functions areasOperational safety : Insight Functions areas
Operational
Safety
Monitoring:Anomalies
detection
DiagnosisPrediction:
What-ifDecision Making
Decomposition
SSS: Goal-Function-Process-System decompositions: New methodology applied: TOGA (Top-down Object-Based Goal-oriented Approach) meta-theory, A.M. Gadomski,1993.
Neural Network Neural Network application to application to
On-line monitoringOn-line monitoringof CRDM rod of CRDM rod
position position by by
thermo-hydraulic thermo-hydraulic conditioncondition
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
The activity will concern the The activity will concern the application of the method to application of the method to validation and thermohydraulic validation and thermohydraulic prediction in a III+ generation prediction in a III+ generation light water nuclear power plant light water nuclear power plant featured with an integral featured with an integral pressurised primary system, pressurised primary system, where access to CRDM (Control where access to CRDM (Control Rod Drive Mechanism) system is Rod Drive Mechanism) system is physically hampered and rod physically hampered and rod positioning can be accurately positioning can be accurately and safely controlled from and safely controlled from exterior only.exterior only.
The activity described is aimed The activity described is aimed at validating data obtained by at validating data obtained by TRIGA reactor measurements TRIGA reactor measurements through soft-computing models through soft-computing models based on neural networks (NN).based on neural networks (NN).
ActivityActivity
HOLMUG 2008 - (Halden On-Line Monitoring User Group) HOLMUG 2008 - (Halden On-Line Monitoring User Group)
Ringhals, Sweden, October 16th – 17th, 2008Ringhals, Sweden, October 16th – 17th, 2008
Description of the reactorDescription of the reactor The reactor is a typical TRIGA light-The reactor is a typical TRIGA light-water (research) reactor with an water (research) reactor with an annular graphite reflector cooled by annular graphite reflector cooled by natural convection, with a power of natural convection, with a power of 1MW.1MW.The reactor core is placed at the The reactor core is placed at the bottom of the 6.25-m-high open bottom of the 6.25-m-high open tank with 2-m diameter. The core tank with 2-m diameter. The core has a cylindrical configuration. has a cylindrical configuration. Inside the core there are 91 Inside the core there are 91 locations, which can be filled either locations, which can be filled either by fuel elements or other by fuel elements or other components like control rods, a components like control rods, a neutron source, irradiation channels, neutron source, irradiation channels, etc.etc.The core temperature is measured The core temperature is measured by 8 thermocouples situated above by 8 thermocouples situated above and under the core, while the fuel and under the core, while the fuel temperature is measured in two fuel temperature is measured in two fuel elements instrumented with elements instrumented with thermocouplesthermocouples
Neural Network application to Neural Network application to on-line on-line monitoringmonitoring of CRDM and thermo-hydraulic condition of CRDM and thermo-hydraulic condition
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
Neural Network StructureNeural Network Structure
Validation NetValidation Net: : used for data used for data validation, initially trained validation, initially trained through the use of reactor through the use of reactor datadata
Train netTrain net: : : : used for training used for training and parameters and parameters updatesupdates every every time an time an errorerror, due to the , due to the difference between the value difference between the value generated by the first net and generated by the first net and the value measured from the the value measured from the instruments occurs.instruments occurs.
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
Reactor data:Reactor data: To train the net a campaign of data acquisition has been To train the net a campaign of data acquisition has been
carried out.carried out. From this campaign emerged that some signals, as From this campaign emerged that some signals, as
originated by the thermocouples, are very close when originated by the thermocouples, are very close when the control rods position is invertedthe control rods position is inverted
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition This unexpected difficulty could cause a mistake in the training This unexpected difficulty could cause a mistake in the training
phase of the net. phase of the net. In order to remedy to this, since the rods can be moved one for In order to remedy to this, since the rods can be moved one for
time, and since the position of a control rod depends both on time, and since the position of a control rod depends both on thermo-hydraulic conditions and the position of the other control thermo-hydraulic conditions and the position of the other control rod, it is chosen to divide the net in two different netsrod, it is chosen to divide the net in two different nets
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 2008
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200816th – 17th, 2008
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
Data validation SHIM1 netData validation SHIM1 net Input data: Input data: core temperature (8 thermocouple); fuel temperature; core temperature (8 thermocouple); fuel temperature;
SHIM2 rod position.SHIM2 rod position.
Output data: Output data: SHIM1 rod position.SHIM1 rod position.
Data validation SHIM2 netData validation SHIM2 net Input data: Input data: core temperature (8 thermocouple); fuel temperature; core temperature (8 thermocouple); fuel temperature;
SHIM1 rod position.SHIM1 rod position.
Output data: Output data: SHIM2 rod positionSHIM2 rod position
Train data setTrain data set
TOPSAFE 2008 TOPSAFE 2008
International Topical Meeting on Safety of Nuclear International Topical Meeting on Safety of Nuclear Installation - Dubrovnik, Croatia, 30 September - 3 Installation - Dubrovnik, Croatia, 30 September - 3
October 2008October 2008
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
TrainingTraining After the training After the training
process (42 process (42 epochs), the value epochs), the value of the achieved of the achieved train performance train performance has been 3.6e-28.has been 3.6e-28.
Training outcomeTraining outcome experimental data experimental data
match with match with training datatraining data
rod position rod position percentage error is percentage error is 1.5e-41.5e-4
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
Neural Network application to Neural Network application to on-line monitoring of CRDM and thermo-hydraulic on-line monitoring of CRDM and thermo-hydraulic
conditioncondition
In order to make the case study concrete several In order to make the case study concrete several simulations, with different data, from those used simulations, with different data, from those used for training, have been carried out.for training, have been carried out.
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
TECHNICAL Conclusion (1)TECHNICAL Conclusion (1)Neural Network application to Neural Network application to
on-line monitoring of CRDM and thermo-hydraulic conditionon-line monitoring of CRDM and thermo-hydraulic condition
The outcome obtained in this applications The outcome obtained in this applications have been satisfactory as the error in steady have been satisfactory as the error in steady state resulted less than the expected one and state resulted less than the expected one and the training method quite effective. Testing the training method quite effective. Testing will continue with increasing of data scanning will continue with increasing of data scanning rate and signal filtering, to improve the rate and signal filtering, to improve the answer during status transitions and answer during status transitions and investigate how to decrease oscillations investigate how to decrease oscillations during the steady-statesduring the steady-states..
HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – HOLMUG 2008 - (Halden On-Line Monitoring User Group) Ringhals, Sweden, October 16th – 17th, 200817th, 2008
ENEA/S’ SPECIFIC INTERESTS ENEA/S’ SPECIFIC INTERESTS (2)(2)
1. New Approach to Intelligent Console NetworkIntelligent Console Network for Nuclear Supper Safety
2. Suggested initiative: to propose for UE and IAEA the organization of:
European Network of the Research and Consulting Centers for the development of Super-Safety & High Intelligent Nuclear Operations
Grid (SSHINO).
This new SS Strategy should:
- include in the safety operation human management and organization responsibility
- extend the concept of safety on the emergency propagation in space and time
- adapt technology to humans by high-intelligent ICT support network.
Operational Super-Safety mission . ENES intends to follow two main closely interdependent RTD directions:
- 1. Nuclear integrated super safety management
- 2. High intelligence add network for design, planning and operations.
Super-Safety Concept Super-Safety Concept in Nuclear Integrated Safety Managementin Nuclear Integrated Safety Management
An example of on-line monitoring application to An example of on-line monitoring application to
CRDM for operational safety CRDM for operational safety
END
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