Abstract
This work focused on a new developed and experimentally tested non-linear control approach to control a double tube heat exchanger's output cold water temperature. The control approach uses an inverse artificial neural network (ANNi) and an integral control law. The ANNi objective is to obtain the cold water flow considering as a reference the desidered output cold water temperature. The ANNi was built from a feedforward ANN with four inputs (input cold water temperature, input hot water temperature, cold water flow, hot water flow), a hidden layer with a neuron, and two outputs (output cold water and output hot water temperature). The integral control law is implemented to reduce the error between the setpoint and the ANNi estimate. As a result, the output cold water temperature regulation is achieved through a simple controller that does not need a complex ANN model and does not depend on the process parameters' exact value. The proposed control scheme (Inverse Artificial Neural Network with Integral Control (ICANNi)) showed good reference tracking, obtaining a root mean square error (RMSE) of 0.2025, standard deviation (SD) of 0.0410, establishment time of 23 s, and overshoots less than 2.7 °C. In addition, the ICANNi control has a settling time that is 1.91 times faster than the PID control and 1.5 times faster than the ANNi control.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.