Heat Exchanger Fouling Detection
Black-box AI recommendations face low operator trust in safety-critical plants, and hidden sensor calibration issues can corrupt optimization decisions.
The Problem
“Explainable AI for Heat Exchanger Fouling Detection and Sensor Validation”
Organizations face these key challenges:
Operators do not trust black-box AI recommendations in safety-critical plants
Sensor calibration drift can mimic fouling and mislead optimization systems
Manual historian analysis is slow and inconsistent across shifts
Static thresholds fail under changing operating regimes and load conditions
Root-cause isolation between process degradation and instrumentation issues is difficult
Maintenance decisions are often reactive or overly conservative
Impact When Solved
The Shift
Human Does
- •Review exchanger temperature, flow, and pressure trends to assess possible fouling.
- •Perform manual performance calculations and normalize for load, weather, and feed changes.
- •Investigate suspected degradation using historian data, lab results, and operating context.
- •Decide when to clean, bypass, or derate equipment based on fixed limits and engineering judgment.
Automation
- •No AI-driven fouling detection or severity estimation is used.
- •No automated separation of true fouling from load changes, ambient effects, or sensor drift is available.
- •No predictive estimate of remaining time to cleaning is generated.
Human Does
- •Review AI-flagged fouling severity and confirm operational significance.
- •Approve cleaning windows, operating adjustments, or maintenance deferrals based on production priorities.
- •Handle exceptions when alerts conflict with field observations, lab findings, or known process upsets.
AI Handles
- •Continuously monitor exchanger behavior across changing load, ambient, and feed conditions.
- •Estimate fouling resistance and detect early-stage degradation before fixed alarms are reached.
- •Distinguish likely fouling from transient process changes or sensor issues to reduce false alarms.
- •Prioritize exchangers by severity and forecast likely time to cleaning to support planning.
Operating Intelligence
How Heat Exchanger Fouling Detection runs once it is live
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
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
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not approve cleaning windows, maintenance deferrals, or operating changes without operator or planner judgment. [S1][S3]
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Heat Exchanger Fouling Detection implementations:
Key Players
Companies actively working on Heat Exchanger Fouling Detection solutions: