Dynamic prediction and control of heat exchangers using artificial neural networks

We extend the artificial neural network (ANN) technique to the simulation of the time-dependent behavior of a heat exchanger (HX) and use it to control the temperature of air passing over it. The experiments are carried out in a open loop test facility. First a methodology is proposed for the training and prediction of the dynamic behavior of thermal systems with heat exchangers. Then an internal model scheme is developed for the control of the over-tube air temperature with two artificial neural networks, one to simulate the heat exchanger and another as controller. An integral control is implemented in parallel with the filter of the neural network controller to eliminate a steady-state offset. The results are compared with those of standard PI and PID controller. There is less oscillatory behavior with the neural network controller, which allows the system to reach steady-state operating conditions in regions where the PI and PID controllers are not able to perform as well. (C) 2001 Elsevier Science Ltd. All rights reserved.