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Thermal vs hydraulic monitoring: discover how heat transfer and pressure drop reveal different types of heat exchanger fouling and why both matter.
Thermal vs. hydraulic monitoring: what each one catches, and what it misses Read Post »
Learn the minimum historian data needed for heat exchanger condition monitoring, including key temperature, flow, and pressure measurements, and why data quality matters for reliable performance insights.
Historian data quality: the minimum tag set you need to start monitoring Read Post »
Learn how existing historian data can help monitor legacy heat exchangers, detect fouling, and track performance without new sensors, capital projects, or shutdowns.
Monitoring of legacy assets without capital projects or shutdowns Read Post »
Your DCS may already hold the clues to heat exchanger fouling. Learn how existing control signals reveal declining performance without new sensors.
No new sensors required: why your DCS already knows your exchanger is fouling Read Post »
Discover how existing historian data enables heat exchanger condition monitoring without additional instrumentation.
Learn how a clean-condition baseline helps measure heat exchanger performance, detect fouling, track degradation, and set meaningful maintenance thresholds.
What “good” looks like: setting a clean-condition baseline and why it matters Read Post »
Pressure drop is usually treated as a cost to minimise. Normalised for flow, it becomes a second health signal that catches restrictive fouling.
Pressure drop as a diagnostic signal, not just a nuisance Read Post »
Outlet temperature shows how a heat exchanger behaves today, not how healthy it is. UA gives a load-independent signal from existing instrumentation.
Fouling isn’t one problem. What builds up on a heat exchanger surface depends on what flows through it, how hot the surface runs and how the fluid moves.
Fouling 101: the main mechanisms — and why each behaves differently Read Post »
A heat exchanger rarely fails dramatically. It declines slowly and expensively as a thin film of deposit builds on its surfaces.
Fluctuating oil demand is pushing tank terminal operators to improve asset reliability and operational efficiency through predictive maintenance.
Tank Terminals: How to Ensure Asset Availability? Read Post »
Learn how the ARMA (Auto Regressive Moving Average) model can predict anomalies in process data and raise alarms before failures occur.
Understanding Time Series: ARMA – Auto Regressive Moving Average Read Post »
APM for tank terminals: how predictive and condition-based maintenance on real-time data addresses the bulk storage industry’s challenges.
Tank Terminals: How to Ensure Asset Availability? Read Post »
