Applications of intelligent methods in various types of heat exchangers: a review

Heat exchangers are applicable in different industries and technologies, and their performance is influenced by different parameters. In addition to experimental and time-consuming computational approaches, intelligent methods can be used for the investigation of heat exchanger performance due to their abilities in accurate prediction and relatively fast performance. The accuracy and applicability of machine learning methods, mainly based on intelligent techniques, in modeling and forecasting the performance of heat exchangers are dependent on some factors including architecture of algorithm, inputs of the model, and complexity of the system. Owing to the aforementioned facts, it would be crucial to consider the influential factors in the proposed mode to produce models with the greatest accuracy. In this work, different applications of intelligent methods in performance modeling heat exchangers are reviewed, and the key outcomes of the reviewed works are represented. Moreover, the items influencing the performance of these methods are investigated. In the final stage of the current paper, some ideas are recommended for future works in the relevant fields.

Automated summary

The performance modeling and forecasting of heat exchangers can utilize intelligent methods, with accuracy and applicability dependent on factors such as algorithm architecture, model inputs, and system complexity. Considering influential factors in the model is crucial for producing models with the greatest accuracy, while the performance of intelligent methods is influenced by various factors.