Hybrid artificial neural network-genetic algorithm technique for condensing temperature control of air-cooled chillers

Abstract

Air-cooled chillers are commonly used in commercial buildings in the subtropical climate, which are considered inefficient due to operating under traditional head pressure control. This study presents a hybrid intelligent control technique, including neural networks and genetic algorithms, for the optimal control of the set points of the condensing temperature to improve the coefficient of performance (COP) of air-cooled chillers under various operating conditions. The neural network is used to model the air-cooled chillers, and genetic algorithm is adopted in searching optimal set points of condensing temperature based on the predicted fitness values. Results show that this control technique allows optimal set point of the condensing temperature to be successfully determined, and the chiller performance can be improved considerably.