Modeling of the Operational Parameters on the Performance of an Air Conditioning System

Heat and moisture are removed from occupied spaces by air conditioning systems. Air conditioners regulate moisture and temperature, and improve air quality. Human efficiency is dramatically boosted and worker's efficiency is affected by their work environment. Human being is more comfortable and productive with air conditioning. After a few years of excellent operation, the system began to fail, resulting in numerous resident complaints. The goal of this study is to establish operational characteristic models for an air conditioning system's performance in order to fulfill future client requirements. The inlet and output temperatures as a function of local time were measured for the LSBLG 1200/MCF model central water chiller air conditioning system. The COP and EER curves were examined. MiniTAB 16.0 was used to establish the characteristic curves and models, according to the findings. The regression models are statistically significant at 97.7 percent and 91.1 percent for p-value less than 0.05 and R2 , respectively. The COP and EER values were calculated, and it was determined that the best cooling occurred between 1 and 3 p.m. The chiller and cooling tower achieve maximum coefficients of performance (COP) of 52 and 20, respectively. The chiller and cooling tower have EER ratings of 177 and 68, respectively. Regression analysis is used to develop models that characterize the statistical relationship between operational parameters such as input and outlet temperatures as a function of local time and response variables such as COP and EER. The performance of the system can be monitored using existing models. Keywords: Modeling, operational parameters, performance, and air conditioning system.