Evaluating High-Precision Machine Learning Techniques for Optimizing Plate Heat Exchangers’ Performance

Plate heat exchangers have the advantages of high heat transfer coefficients and compact structures, and they are widely used in aerospace, nuclear power, and other fields. Nevertheless, several scalability challenges have emerged during the utilization process. If not addressed promptly, the issue will reduce heat transfer efficiency, consequently causing energy waste, diminished production capacity, and a shortened lifespan. In this study, we employed the long short-term memory (LSTM) algorithm model and the multi-layer perceptron (MLP) algorithm model to monitor the health status of plate heat exchangers. This was achieved by fine-tuning the hidden layers and neurons of the models. The individual model exhibiting the highest prediction accuracy was incorporated into a more sophisticated ensemble model to monitor the health status of plate heat exchangers. The study revealed that the MLP 2 × 64 + LSTM 2 × 64 model achieved the highest prediction accuracy, scoring 0.9942. According to the simulation program for plate heat exchangers, the fouling thermal resistance was determined to be 0.0003 m²·K/W when the heat exchange efficiency decreased by 50%. An early warning threshold was established within the health condition value (HCV), triggering an alert when the heat transfer efficiency of the plate heat exchanger fell below 50%. Combining the LSTM and MLP algorithms provides new ideas and technical support for the health assessment and maintenance of plate heat exchangers.