Adaptive neurocontrol of heat exchangers

This paper investigates the use of adaptive artificial neural networks (ANN s) to control the exit air temperature of a compact heat exchanger. The con trollers, based on an internal model control scheme, can be adapted on-one on the basis of different performance criteria. By numerical simulation a m ethodology by which the weights and biases of the neural network are modifi ed according to these criteria was developed. An ANN controller for an air- water compact heat exchanger in an experimental facility is then implemente d. The parameters of the neural net are modified using three criteria: mini mization of target error, stabilization of the closed-loop performance of t he controller, and minimization of a performance index that we have taken t o be the energy consumption. It is show that the neural network is able to control the air exit temperature in the heat exchanger. The neurocontroller is able to adapt to major structural changes in the system as well as to s imultaneously minimize the amount of energy used.