Characterization of a triple concentric-tube heat exchanger with corrugated tubes using Artificial Neural Networks (ANN)

Abstract This work presents a model of Artificial Neural Networks (ANNs) capable of accurately predicting the heat transfer rate and pressure drop in a triple concentric-tube heat exchanger (TTHX) with corrugated and non-corrugated inner tubes and involving a fluid typically used in the food industry. Pitch and depth are varied in the case of corrugated tubes. The ANN model was developed and validated using a huge databank including 181 experimental datasets. The best training algorithm is the Bayesian regulation. A back-propagation algorithm, which is considered to be the most common learning method for ANNs, is used in the training and testing of the network. Different network configurations were tested, and the optimum ANN configuration consisted of a network with two hidden layers with 15 and 21 nodes in the first and second layer, respectively. The ANNs results were found to be in good agreement with the experimental data, with the absolute average relative deviation (AARD) being below 1.91% for the heat transfer coefficient and below 3.82% for the pressure drop, respectively. The simplicity of the developed ANN model and its low levels of error for a huge experimental databank are some of the key features of the model.