Small Modular Reactor Operations
AI systems for operating and optimizing small modular nuclear reactors
The Problem
“Optimize SMR turbine component life using AI-driven predictive forecasting”
Organizations face these key challenges:
Calendar-based replacement is overly conservative for many operating profiles
Usage severity varies significantly across sites, customers, and dispatch strategies
Operational data is fragmented across SCADA, historian, CMMS, and inspection reports
Engineering teams lack scalable methods to convert raw operating history into life-consumption forecasts
Unexpected degradation can still occur between scheduled inspections
Maintenance planning is reactive and often disconnected from real-time operating conditions
Regulated environments require traceability, validation, and explainability before AI recommendations can influence maintenance decisions
Impact When Solved
The Shift
Human Does
- •Review alarms, trends, and operating procedures to assess plant condition and safety margins
- •Coordinate load-following and dispatch plans using conservative operating limits and market conditions
- •Plan maintenance from fixed intervals, condition checks, and manual review of equipment logs
- •Investigate trips, transients, and abnormal signals through expert judgment and post-event analysis
Automation
- •No AI-driven operational analysis is used in the legacy workflow
- •No automated fusion of sensor, maintenance, and grid context is performed
- •No predictive prioritization of degradation, outage risk, or maintenance timing is available
Human Does
- •Approve operating adjustments and dispatch decisions within procedures, technical specifications, and safety limits
- •Review and authorize maintenance priorities, outage windows, and corrective actions recommended by the system
- •Handle exceptions, ambiguous alerts, and abnormal conditions requiring operator judgment
AI Handles
- •Continuously monitor plant, equipment, and operating context to detect early degradation and anomalous patterns
- •Prioritize risks and generate decision-support recommendations for operating envelopes, setpoints, and maintenance timing
- •Fuse sensor history, maintenance records, and grid or market conditions into real-time operational assessments
- •Track fleet and unit performance against availability, reliability, and cost objectives and surface emerging issues
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not change reactor operating conditions, dispatch decisions, or setpoints without licensed operator approval under plant procedures and safety limits [S2].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Small Modular Reactor Operations implementations:
Key Players
Companies actively working on Small Modular Reactor Operations solutions: