Modelling a non-stationary single tube heat exchanger using multiple coupled local neural networks

This paper presents the application of an online identification neural technique to a single tube heat exchanger with a constant outer surface temperature. To show the feasibility of such an identification, the response to a sequence of random temperatures at the inlet of the inner fluid is studied. In the first part, the numerical solution is given, showing that the model cannot be a first order model. Then the principles of the neural technique are presented. The standard neural architecture, which normally calculates the output of the system directly from the input, is modified. A large number of local identical networks are used, each of them modelling an elementary module. It is shown that the neural model determined from the study of the first local network is representative of all the local networks (using the actual input data). At last it is shown that, when the networks are coupled, the output of the last network is in good agreement with the values obtained by the numerical model, but in a greatly reduced time.