ThermaFlux AI
AI platform for thermal plant efficiency monitoring that optimizes combustion in waste-to-energy operations and improves district heating performance through heat utilization and bidirectional energy exchange.
The Problem
“Optimize waste-to-energy combustion and district heating energy routing with AI”
Organizations face these key challenges:
Highly variable calorific value and moisture content in incoming waste streams
Combustion instability causing efficiency loss, emissions spikes, and slagging risk
Manual tuning of air flow, grate speed, feed rate, and recirculation settings
Limited ability to coordinate boiler output with district heating demand in real time
Impact When Solved
The Shift
Human Does
- •Review boiler trends, fuel quality checks, and emissions readings to judge combustion stability
- •Manually tune air flow, grate speed, feed rate, and recirculation setpoints during operating shifts
- •Plan heat routing, storage use, and district heating supply with static schedules and spreadsheets
- •Adjust boiler output and exchanger loading in response to demand changes, alarms, and market conditions
Automation
- •No AI-driven monitoring or optimization is used in the legacy workflow
- •No automated prediction of fuel variability, heat demand, or exchanger utilization is available
- •No system-generated ranking of combustion or heat routing actions is provided
- •No closed-loop execution of setpoint changes or energy exchange dispatch is performed
Human Does
- •Approve recommended combustion targets and heat routing actions within operating policies
- •Handle safety, compliance, and abnormal operating exceptions that require human judgment
- •Set business priorities for efficiency, emissions, heat delivery, storage use, and energy exchange
AI Handles
- •Continuously monitor combustion stability, fuel variability, emissions risk, heat demand, and asset constraints
- •Generate ranked setpoint recommendations for air distribution, grate speed, feed rate, recirculation, and boiler targets
- •Optimize heat utilization across exchanger lines, storage assets, district heating supply, and bidirectional energy exchange
- •Forecast short-term demand and plant conditions, then triage deviations and trigger corrective actions or approved closed-loop adjustments
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 operating bounds, automation level, or business priorities for efficiency, emissions, heat delivery, storage use, or energy exchange without human authorization [S1].
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 ThermaFlux AI implementations:
Key Players
Companies actively working on ThermaFlux AI solutions:
Real-World Use Cases
AI-driven combustion optimization for waste-to-energy boilers
Software watches how a waste-to-energy furnace is burning and continuously adjusts controls so trash burns more efficiently and cleanly.
Waste-to-energy district heating load orchestration for DTU area
Heat from burning waste is captured, stored, and sent through heat exchangers to warm buildings instead of using natural gas boilers. The system can also send extra heat back to the wider network when there is more than needed.