Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks

Abstract

We consider the flow of humid air over fin-tube multi-row multi-column compact heat exchangers with possible condensation. Previously published experimental data are used to show that a regression analysis for the best-fit correlation of a prescribed form does not provide an unique answer, and that there are small but significant differences between the predictions of the different correlations thus obtained. It is also shown that it is more accurate to predict the heat rate directly rather than through intermediate quantities like the j-factors. The artificial neural network technique is offered as an alternative technique. It is trained with experimental values of the humid-air flow rates, dry-bulb and wet-bulb inlet temperatures, fin spacing, and heat transfer rates. The trained network is then used to make predictions of the heat transfer. Comparison of the results demonstrates that the neural network is more accurate than conventional correlations.