Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach

Abstract

This work applied Artificial Neural Network (ANN) for heat transfer analysis of shell-and-tube heat exchangers with segmental baffles or continuous helical baffles. Three heat exchangers were experimentally investigated. Limited experimental data was obtained for training and testing neural network configurations. The commonly used Back Propagation (BP) algorithm was used to train and test networks. Prediction of the outlet temperature differences in each side and overall heat transfer rates were performed. Different network configurations were also studied by the aid of searching a relatively better network for prediction. The maximum deviation between the predicted results and experimental data was less than 2%. Comparison with correlation for prediction shows superiority of ANN. It is recommended that ANN can be used to predict the performances of thermal systems in engineering applications, such as modeling heat exchangers for heat transfer analysis.