Real-Time Implementation of Model Predictive Neural Controller for Heat Exchanger

N. Pappa, G. Kaliraj, and J. Shanmugam


Neural networks, heat exchanger, localized PID control, real time


The control of heat exchanger is complex due to its nonlinear dynamics, and particularly the variable steady-state gain and time constant with the process fluid. Linear controllers designed based on linear models will be effective only in a small region around the operating point. The artificial neural network (ANN) technique is used to control the temperature of hot fluid flowing in the inner tube of a physical heat exchanger setup. A methodology is proposed for training and prediction of dynamic behaviour of heat exchanger using feed-forward neural network with external recurrent connections. Then a nonlinear predictive control strategy based on identified model is proposed for heat exchanger control. The performances of neural controller are evaluated in real time and results are compared with a localized PID controller.

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