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MONITORING AND DIAGNOSIS OF A HEAT EXCHANGER USING HYBRID SYSTEM MODELING

Hybrid modeling integrates first principles models and data-based models, such as neural networks, to correctly model physical phenomena. This paper presents the results of the development of hybrid models for monitoring and diagnostics of heat exchanger systems. This paper presents the continuation of the work presented at MARCON 2002, where simulated data for a heat exchanger was used to develop the hybrid modeling in both parallel and series architectures. In this paper, experimental data are used and the results show that parallel hybrid systems can be used to model the heat exchanger and that the hybrid modeling performs better than a first principle model within the normal operating regime. The model is also able to revert back to the first principle model when new operating conditions are encountered giving them a significant advantage over purely data-based models.