© 2020 X Energy, LLC, all rights reserved 1© 2020 X Energy LLC, all rights reserved 1
ARPA-E Energy Innovation Summit
Advanced Operation & Maintenance Techniques implemented in the Xe-100 Plant Digital Twin to reduce Fixed O&M Cost
X-Energy, NCSU, Zachry, EPRI, Sandia, SimGenics May 2021
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X-energy was Created to Change the World
“President Kennedy once said that we are in a space race and my work with NASA reflects the progress he had hoped for.
Today, I believe we are in an energy race. Providing clean energy across the world is my vision for X-energy and I believe that clean, safe, reliable nuclear energy is necessary to making this possible.”
• Dr. Kam Ghaffarian is a globally recognized technology visionary across energy, space and information technology.
• Created and grew Stinger Ghaffarian Technologies (SGT), Inc. to $650 million in annual revenue and 2,400 employees. SGT was ranked as the U.S. National Aeronautics and Space Administration’s second largest engineering services company prior to being acquired by KBRwyle, subsidiary of KBR, Inc.
• Founded X-energy in 2009 to address innovation in critical energy solutions. X-energy was awarded ~$60M from DOE to focus on an advanced nuclear reactor and TRISO fuel.
• Began Intuitive Machines in 2016 to leverage NASA technologies for commercial space and terrestrial applications. Intuitive Machines won its first Commercial Lunar Lander Contract from NASA in 2018.
• Began Axiom Space in 2017 to develop the first commercial space station, to be launched by 2021.
Dr. Kam Ghaffarian, Founder and Executive Chairman
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UCO TRISO Particle – Primary Fission Product Barrier
UCO kernel
≈ 19 000TRISO coated
particles in a pebble
Primary safety goal is to ensure that fission products are retained within the TRISO coated fuel particles to the maximum extent possible
This is achieved through production of high quality TRISO fuel and ensuring that temperatures in the core never exceed the temperatures for which the fuel has been tested (AGR Experiments)
≈220 000 pebbles in the core
0.425 mm 0.855 mm60 mm
Porous Carbon
Silicon Carbide Pyrolytic Carbon
Pyrolytic Carbon
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Xe-100 Plant Overview
Standard X-energy plant have 4 Reactors - 4 Turbines producing 320 MWe, attributes include:
● 200MWth/80MWe Per Module● Process heat applications● Proven intrinsically safe● Meltdown proof● Walk-away safe● Modular construction● Requires less time to construct (2.5-4
years)● Road transportable for diverse
geographic areas● Uses factory-produced components● Load-following to 40% power within
15 minutes● Continuous fueling; resilient on-site
fuel storage
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ARPA-E GEMINA Project Status
• Project Title: Advanced Operation & Maintenance Techniques Implemented in the Xe-100 Plant Digital Twin to Reduce Fixed O&M Cost
• $7.5 Million award from DOE for Digital Twin (DT) and Central Maintenance Model (CMM) concepts
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What is a Digital Twin?
• What does “Digital Twin” mean to X-energy?– From IBM, “A digital twin is a virtual representation of a physical object or system across its
lifecycle, using real-time data to enable understanding, learning and reasoning.”
Something physical: Nuclear Power Plant Digital representation of the physical something: Digital Twin
Physical structures → 3D CAD ModelPhysics (fluid flow + heat transfer) → Systems Analysis SoftwarePhysics (neutronics) → Neutronics Analysis SoftwarePhysics (electromagnetism) → Systems Analysis Software
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What is a Digital Twin?
• What does “Digital Twin” mean to X-energy?– From IBM, “A digital twin is a virtual representation of a physical object or system across its
lifecycle, using real-time data to enable understanding, learning and reasoning.”
Something physical: Nuclear Power Plant Digital representation of the physical something: Digital Twin
Physical structures → 3D CAD ModelPhysics (fluid flow + heat transfer) → Systems Analysis SoftwarePhysics (neutronics) → Neutronics Analysis SoftwarePhysics (electromagnetism) → Systems Analysis Software
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Xe-100 Digital Twin Tools
3D Models with AR / VR
Operator TrainingSimulator
PlantHistorian
AI / MLModels
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Xe-100 Digital Twin Overview
Plant Historian(e.g. PI)
Plant
Equipment Sensors & Instrumentation
Other Sources and Systems:Vibration / SeismicElectricalHVACCyber Security
Manual Data InputE.g. plant walk downs, maintenance work orders
RPS / IPS / DCS / PEMS
Data Collector
Data CollectorApplication
server
Software based
Web based
Nuclear Regulators
X-energy
3rd Party Clients
PerformanceDigital Twin
3D ImmersiveDigital Twin
T
Data Diode
(2) (3)
(1)
(4)
(5)
(6)
(7)
(8)
(9)
(10)(11)
(12)
Feedback information from DT to update simulation models
Utility
Components:- Training Simulator- 3D Models- Plant Historian- Client Tools
Xe-100Digital Twin
(13)
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Functional Diagram
Xe-100Digital Twin
3D Immersive Digital Twin
Performance Digital Twin
Operator Training
Simulator
Intergraph Smart® P&ID
Intergraph Smart® 3D
SimGenics3D PACT
SimGenics SimuPACT
Distributed Control System
Emulation
OperationsMaintenanceSecurity
Human Factors Engineering
Plant Historian
OSIsoft PI System
Sandia Modeling and Simulation
Tool Suite
Probabilistic Risk Analysis
Machine Learning Models
Intergraph Smart® Review
Cyber Security
Parent-Child Relationship
Software Coupling
Program Involvement
Software
Xe-100 Programs
No label
Top-level item
Second-level item
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Training Simulator Integration with the PI System
Model Server
PI DataLink
PI System Access
OPC DA Server
PI Vision
Xe-100 TrainingSimulator
PI Interface(software)
XE-OSIWEB(vm) XE-OSIPI
(vm)XE-OSIWEB
(vm)
PI Server(software)
DataArchive
Asset Framework
(machine-friendly)
(human-friendly)
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Anomaly Detection with Machine Learning
• Central Maintenance Model– Predictive Maintenance Models– Thermal Performance Monitoring– System/Component
Performance Monitoring• Machine Learning
– Diagnostic Models– Prognostic Models
• Systems/Components of interest:– Reactor– Steam Generator– Turbine– Helium Circulators– Feedwater Pumps
● Detect anomalies● Categorize event● Identify deviating
variables
● Identify equipment anomaly
● Degradation monitoring
● Predict time to exceed setpoint
● Maximize equipment use time
● Minimize loss of revenue
● Supplement prognostic prediction
● Ex. weather model
Operator decision
I&C measurements
System-level diagnosis
Component diagnosis
Auxiliary models
Component prognosis
System performance evaluation
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Diagnostic Model
● The diagnostic model aims to ○ Detect system component anomalies○ Identify deviating variables○ Initiate the correct prognostic model○ Be continuously trained online
● Machine learning algorithms include○ Auto-Encoder (AE) for feature extraction○ Long-Short Term Memory (LSTM) for temporal data
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Prognostic Model
● The prognostic model aims to ○ Predict time to abnormal condition○ Provide time window to auxiliary models
● Machine learning algorithms include○ Bayesian Neural Network (BNN) for uncertainty○ AE-LSTM for input space reduction and temporal data○ Convolutional Neural Network (CNN) for efficient
spatiotemporal data processing
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Deep Neural Networks (DNN) to Support Plant Operation
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