Artificial neural network control of a heat exchanger in a closed flow air circuit

This paper experimentally investigates the control of a heat exchanger in a closed flow air circuit. The temperature inside the test section of the test facility has been maintained at a set value by variation of air flow rate over the heat exchanger tube surface and the water flow inside the heat exchanger tubes. The neural network based control has been implemented in a Labview platform and compared with the PID control. The performance of the controller has been investigated for multiple changes in set points and under externally imposed disturbance. The neural network based control has higher speed of response and the steady-state error for the neural network control has a smaller average value than that of the PID control. The control action based on the neural network technique shows less oscillation in comparison to that of the PID based control. Dual actuations, i.e. both air flow and water flow control, have better performance than that with single actuation, i.e. either air flow or water flow control. Both the ANN and PID based control are equally robust in the presence of externally imposed disturbance.