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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