Improving heat exchanger supervision using neural networks and rule based techniques

The work is aiming to the supervision of heat exchangers fouling monitoring. The fouling known as deposition of undesirable material on the heat transfer surface degrades the performance of heat exchangers. The fouling of heat exchangers in process plants results in a significant cost impact in terms of production losses, energy efficiency, and maintenance costs. To overcome mentioned inconveniences a novel supervision strategy is proposed, reporting innovative techniques and main results of an application tool to diagnose the heat transfer efficiency of a heat exchanger of a pilot plant using neural network based models and parity space approaches associated to a rule based decision making strategy. The developed strategy is fragmented into several modules connected between them following a causal logic flowchart. The first module checks the consistence of the supervision system. The second module monitories the heat exchanger for fouling condition with the ability to diagnose the probable causes of fouling. A third module predicts the remaining operating time under acceptable conditions, associated to a decision making task to schedule the supervision flowchart.