AI Heat Recovery Systems
The Problem
“Waste heat lost due to suboptimal recovery control”
Organizations face these key challenges:
1
Highly variable waste-heat supply and heat demand (load cycling, ambient swings, product mix changes) makes fixed setpoints and manual tuning inefficient
2
Performance degradation from fouling, corrosion, scaling, and sensor drift is hard to detect early, causing gradual efficiency loss and surprise outages
3
Complex interactions and constraints across multiple assets (steam cycle, district heating, ORC, heat pumps, cooling systems) prevent operators from consistently finding the true optimum
Impact When Solved
2–6% increase in recovered heat and 0.5–2.0% heat-rate reduction through real-time optimization of recovery equipment10–30% reduction in unplanned downtime and cleaning-related outages via predictive fouling and failure detection5–15% reduction in heating fuel consumption and measurable CO2 reductions by matching recovered heat to demand and minimizing venting/bypass
Real-World Use Cases
Process Optimization in Energy Operations
It helps machines work better and use less energy to save money.
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Power Plant Efficiency Optimization AI
This AI helps power plants work better and use less fuel.
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Heat Rate Optimization for Power Plants
AI helps power plants use less fuel to make electricity, saving money and reducing pollution.
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