ARPA-E Energy Innovation Summit

© 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

© 2020 X Energy, LLC, all rights reserved 2

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


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


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


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

✓

✓


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


Xe-100 Digital Twin Tools

3D Models with AR / VR

Operator TrainingSimulator

PlantHistorian

AI / MLModels

Presenter

Presentation Notes

https://www.nrc.gov/docs/ML1924/ML19241A472.pdf


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)


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


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)


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


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


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


Deep Neural Networks (DNN) to Support Plant Operation

ARPA-E Energy Innovation Summit

Documents

Transcript of ARPA-E Energy Innovation Summit