A thermodynamic evaluation of chilled water central air conditioning systems using artificial intelligence tools

Abstract

An analysis of a chilled water central air conditioning system is presented. The object was to calculate main cycle component irreversibility, as well as evaluating this indicator's sensitivity to operational variations. Artificial neural networks (ANN), genetic algorithms (GA) and Matlab tools were used to calculate refrigerant thermodynamic properties during each cycle stage. These tools interacted with equations describing the system's thermodynamic behaviour. Refrigerant temperature, when released from the compressor, was determined by a hybrid model combining the neural model with a simple genetic algorithm used as optimisation tool; the cycle's components which were most sensitive to changes in working conditions were identified. It was concluded that the compressor, evaporator and expansion mechanism (in that order) represented significant exergy losses reaching 85.62% of total system irreversibility. A very useful tool was thus developed for evaluating these systems.