Process-Data Analytics for heat exchangers
Process-Data Analytics is one of the most effective ways to monitor heat exchangers: it catches developing faults — fouling and scaling on heat-transfer surfaces, tube blockage and flow maldistribution, corrosion and tube leaks — early, so repairs are planned rather than forced by a breakdown.
Why process-data analytics suits heat exchangers
Heat exchangers foul gradually, quietly cutting heat-transfer effectiveness and raising energy use long before they cause a problem. Because the degradation is in process data rather than vibration, analytics that model expected performance are the right tool — flagging when cleaning will actually pay back, rather than cleaning on a fixed, often wasteful, schedule.
How process-data analytics works
Rather than adding sensors, analytics learns the normal relationship between a process and its conditions from existing data, then detects when reality drifts from expectation. A widening approach temperature, a rising pressure drop, a falling efficiency at constant load — all signal fouling, scaling or wear long before failure. It scales across many assets cheaply, depending on the quality of the existing data.
Faults it catches on heat exchangers
- Fouling and scaling on heat-transfer surfaces
- Tube blockage and flow maldistribution
- Corrosion and tube leaks
- Gasket failure
What the data shows
A widening approach temperature and rising pressure drop on a heat exchanger or chiller signal fouling; a rising stack temperature on a boiler signals fouling or scaling; a falling efficiency at constant load on any thermal asset signals degradation.
Process-Data Analytics on heat exchangers: implementation
What the signal looks like. A widening approach temperature and rising pressure drop on a heat exchanger or chiller signal fouling; a rising stack temperature on a boiler signals fouling or scaling; a falling efficiency at constant load on any thermal asset signals degradation.
How that reads on heat exchangers specifically. A widening approach temperature and rising pressure drop at constant flow are the classic fouling signature; a sudden change can indicate a tube leak or blockage. Trending effectiveness shows the optimal, cost-justified time to clean.
Where it is blind. Process-Data Analytics does not cover every failure mode on heat exchangers. The equipment profile also lists Performance trending against a clean baseline, Periodic inspection and leak testing, Thermography on accessible units — they exist because they detect modes this technique cannot see.
Before you set a threshold. Baselining, alarm criteria and the route from alert to work order are the same problem for every technique. They are covered once in setting up a condition monitoring programme and in the process-data analytics overview.
Process-Data Analytics on other equipment
Process-Data Analytics for boilers · Process-Data Analytics for chillers and refrigeration · Process-Data Analytics for cooling towers
Other techniques on heat exchangers
Thermography (Infrared Inspection) for heat exchangers
Related
Predictive maintenance for heat exchangers · Process-Data Analytics overview · Process-Data Analytics