Abstract

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.