Prediction of heat transfer rates for shell-and-tube heat exchangers by artificial neural networks approach

Abstract

This work used artificial neural network (ANN) to predict the heat transfer rates of shell-and-tube heat exchangers with segmental baffles or continuous helical baffles, based on limited experimental data. The Back Propagation (BP) algorithm was used in training the networks. Different network configurations were also studied. The deviation between the predicted results and experimental data was less than 2%. Comparison with correlation for prediction shows ANN superiority. It is recommended that ANN can be easily used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis.