AI Condenser Performance Prediction

The Problem

“Predict condenser performance before efficiency losses”

Organizations face these key challenges:

Performance losses are detected late because vacuum/backpressure alarms trigger after efficiency has already degraded, especially during seasonal cooling-water swings.

Root-cause diagnosis is slow and ambiguous: similar symptoms can be caused by fouling, air in-leakage, CW flow restrictions, or air-ejector issues, leading to trial-and-error maintenance.

Maintenance is often time-based rather than condition-based, causing either over-cleaning (unnecessary cost/outage time) or under-cleaning (derates, tube leaks, and forced outages).

Impact When Solved

0.5–1.5% heat-rate improvement by maintaining condenser cleanliness and vacuum closer to baseline conditions2–10 MW avoided derates during high ambient/CW temperature periods through earlier interventions and optimized operating setpoints30–60% reduction in diagnostic time for condenser-related issues via automated anomaly detection and probable-cause classification