Heat Transfer Coefficient Prediction and Increase the Effectiveness in Fin-and-Tube Heat Exchangers Using Machine Learning Approaches

Abstract

The heat transfer coefficient (HTC) plays a crucial role in the efficiency and performance of heat exchangers, which are essential in numerous industrial applications. However, obtaining sufficient high-quality data for machine learning models in complex systems like heat exchangers can be challenging. This research aims to optimize the prediction of HTC in fin-and-tube heat exchangers by applying advanced machine learning models. By incorporating smooth wavy fins and combining Louvred fins with rectangular wing vortex generators, the study seeks to enhance heat transfer, reduce pressure drop, and minimize pumping power. The adaptive neuro-fuzzy inference system (ANFIS) has been used to predict the flow boiling heat transfer coefficient, outperforming traditional methods with a maximum coefficient of 14.2. Utilizing tools like matlab for HTC prediction can improve the effectiveness of these heat exchangers. Future research will focus on integrating advanced computational and experimental techniques to develop more accurate models, optimizing heat exchanger designs, and improving energy efficiency while minimizing environmental impact.