NEURAL NETWORK ANALYSIS OF FIN TUBE REFRIGERATING HEAT EXCHANGER WITH LIMITED EXPERIMENTAL DATA

We consider the problem of accuracy in heat rate estimations from articial neural network (ANN) models of

heat exchangers used for refrigeration applications. Limited experimental measurements from a manufacturer are

used to show the capability of the neural network technique in modeling the heat transfer phenomena in these

systems. A well-trained network correlates the data with errors of the same order as the uncertainty of the

measurements. It is also shown that the number and distribution of the training data are linked to the performance

of the network when estimating the heat rates under di€erent operating conditions, and that networks trained from

few tests may give large errors. A methodology based on the cross-validation technique is presented to nd regions

where not enough data are available to construct a reliable neural network. The results from three tests show that

the proposed methodology gives an upper bound of the estimated error in the heat rates. The procedure outlined

here can also help the manufacturer to nd where new measurements are needed.