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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.

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that a Neural Network Output Error (NNOE) model can be accurate using the right size of the regression vector; the learning database consisting of the data obtained during a half day. Finally, it is shown that a Multiple Inputs Single Output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that varies with the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model (SISO—Single Input Single Output) or MISO) is less than 0.5%.