AI Heat Exchanger Fouling Detection
The Problem
“Detect heat exchanger fouling before efficiency collapses”
Organizations face these key challenges:
1
Fouling signals are confounded by changing load, ambient conditions, and feed composition, making early detection difficult with simple trending.
2
Late detection leads to higher fuel consumption, reduced heat recovery, elevated pressure drop, and forced derates or bypassing that erode margins.
3
Conservative time-based cleaning causes unnecessary maintenance cost, production disruption, and risk of damage from frequent mechanical/chemical cleaning.
Impact When Solved
0.2–1.0% heat-rate/fuel reduction via earlier fouling identification and targeted cleaning windows.10–30% fewer unnecessary cleanings with condition-based maintenance and quantified fouling severity.Reduced unplanned outages/derates by catching exchanger performance collapse days to weeks earlier, improving unit availability by ~0.2–0.5 percentage points.
Real-World Use Cases
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