Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter

Abstract

This study deals with predicting the performance characteristics of a reversibly used cooling tower (RUCT) under cross flow conditions for heat pump heating system in winter using artificial neural network (ANN) technique. For this aim, extensive field experimental work has been carried out in order to gather enough data for training and prediction. After back-propagation (BP) training combined with principal component analysis, the three-layer ANN model with a tangent sigmoid transfer function at hidden layer with 11 neurons and a linear transfer function at output layer was obtained. The predictions agreed well with the experimental values with a satisfactory correlation coefficient in the range of 0.9249–0.9988, the absolute fraction of variance in the range of 0.8753–0.9976, and the mean relative error in the range of 0.0008–0.54%, moreover, the root mean square error values for the ANN training and predictions were very low relative to the range of the experiments. The results reveal that ANN model can be used effectively for predicting the performance characteristics of RUCT under cross flow conditions, then providing the theoretical basis on the research of heat and mass transfer inside RUCT, which is important for design and running control of the RUCT system.