Open AccessPrediction of Thermal Performance of Cooling Tower of a Chiller Plant Using Machine Learning
Author(s) -
K Karunamurthy,
Debdutta Chatterjee,
Shivam Pattanashetti,
Aditya Chhetri,
P. Aswin Sevugan,
G. Suganya
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/573/1/012029
Subject(s) - chiller , chilled water , condenser (optics) , water chiller , evaporator , cooling tower , shower , refrigerant , water cooling , refrigeration , environmental science , cooling capacity , tower , engineering , waste management , mechanical engineering , heat exchanger , civil engineering , thermodynamics , light source , optics , nozzle , physics
Chiller plants (CP) are accountable for regulating the comfort levels of most indoor environments. The CP uses water as the working medium and acts as a centralized cooling system for the controlled cooling of products, in the different production environments, machine tool industries, 3D printing, packaging, heat exchanging systems and to preserve agricultural produce, dairy products, and other edible items. The CP under study is used for providing cooling to sixteen storeyed hostel building at VIT Chennai. The refrigerant used in this system is Tetra fluoro ethane (R134a). The chilled water (ChW) from the evaporator is circulated to the rooms in the hostel through the secondary circuit and the air handling unit exchanges chillness from the chilled water and finally supplies to the hostel rooms. The chilled water from the hostel returns to the evaporator in a closed loop. The cooling water (CW) from the condenser of the refrigeration system rejects heat to the cooling tower (CT). Thus the performance of the CT is directly linked to the performance of the cooling provided to the hostel rooms. The objective of this research is to predict the temperature at the outlet of the CT integrated with the CP using machine learning algorithms. The predicted values are compared with the measured values and with the values calculated theoretically. The results are analyzed using the standard metrics and are observed to be appreciable.