Adaptive Neurocontrol and Minimization of Energy Consumption of a Heat Exchanger Test Facility

Abstract

It has been shown that artificial neural networks (ANNs) can be used to simulate and control thermal systems such as heat exchangers. It is known that the characteristics of thermal components such as heat exchangers vary with respect to time mainly due to fouling effects. There is a need of a model that can adapt to the new characteristics of the thermal system. In this work adaptive artificial neural networks are used to control the outlet air temperature of a heat exchanger test facility. The neurocontrollers are adapted on-line on the basis of different criteria. The parameters of the ANNs are modified considering target error and stability conditions of the closed loop system analyzed as a nonlinear iterative map. We also implement a minimization of a performance index that quantifies the energy consumption. It is shown numerically and experimentally that the neural network is able to control the thermal facility, and is also able to adapt to different disturbances applied to the system, while minimizing the amount of energy used.