Identifying fouling in a heat exchanger can be challenging because the symptoms of fouling, such as an increase or decrease in temperatures, increased pressure drop, and decrease in flow can also be caused by other factors such as equipment malfunction, changes in operating conditions, the impact of upstream/downstream conditions or even fluid properties. Additionally, fouling can occur in different forms, such as deposition, corrosion, or biological growth, making it difficult to identify the specific cause of the problem.

Traditional solutions for detecting fouling in a heat exchanger, such as manual inspections or rule-based monitoring, may not always be effective because they rely on limited human interpretation and are often reactive rather than proactive.

Learn how AI Expert’s early detection of heat loss helped save $150,000 annually.

Download the case study to know how AI-powered predictive analytics can do wonders for your asset performance & predictive maintenance strategy.

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Companies using UptimeAI are saving millions with timely alerts & near-zero downtime

“We were monitoring limited equipment in isolation. UptimeAI’s approach enables us to look at the interrelations & scale the plant”– Oil & Gas, Executive Asia

“Building a full plant monitoring application on an IoT platform is too difficult. In contrast, ‘AI Plant Expert’ offers higher ROI & quick scale-up with an out-of-box, comprehensive solution.”– Power, CIO Asia