OPTIMIZATION AND PREDICTION OF CENTRAL AC SYSTEM PERFORMANCE WITH RESPONSE SURFACE METHODOLOGY (RSM) MODELING

Optimizing AC system performance is important to optimize energy consumption, especially at partial load. The aim of this research is to obtain a performance model of the central AC system based on its operational conditions which include environmental air conditions, heat load conditions and other operational parameters, so that the performance of the AC system can be optimized which includes cooling capacity, power consumption and energy efficiency ratio. Data is obtained by experiment, then analyzed by Response Surface Methodology (RSM) to obtain optimal system performance. This research resulted in models of AC performance with coefficient of correlation (R2) of 0.9745, 0.2041 and 0.8965 for cooling capacity, power consumption and EER respectively. By analysis of varians for models, it is obtained that Model F-values are 9920.83, 313.45, and 2245.59 for cooling capacity, power and EER respectively, and implied that the models are significant. The Adequate precision ratios were 1078.33, 93.08, and 344.32 for those parameters respectively, and indicated the adequate signals. The optimum results obtained were a cooling capacity of 46.7 kW, compressor power consumption of 4.48 kW, and an energy efficiency ratio of 8.5. Keywords: Performance; Central air conditioning; Prediction; Optimization; RSM.