Linear regression and R-squared correlation analysis on major nuclear online plant cooling system

• Published in: International Journal of Electrical and Computer Engineering
• Main Author: Mohamad Nor A.A.; Minhat M.S.; Ya'acob N.; Kassim M.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151855287&doi=10.11591%2fijece.v13i4.pp3998-4008&partnerID=40&md5=a082960f898ec9a8431582a8d857e73a

The primary cooling system is an integral part of a nuclear reactor that maintains reactor operational safety. It is essential to investigate the effects of the cooling system parameter before implementing predictive maintenance techniques in the reactor monitoring system. This paper presents a linear regression and R-squared correlation analysis of the nuclear plant cooling system parameter in the TRIGA PUSPATI Reactor in Malaysia. This research examines the primary cooling system's temperature, conductivity, and flow rate in maintaining the nuclear reactor. Data collection on the primary coolant system has been analyzed, and correlation analysis has been derived using linear regression and R-squared analysis. The result displays the correlation matrix for all sensors in the primary cooling system. The R-squared value for TT5 versus TT2 is 89%, TT5 versus TT3 is 94%, and TT5 against TT4 is 66% which shows an excellent correlation to the linear regression. However, the conductivity sensor CT1 does not correlate with other sensors in the system. The flow rate sensor FT1 positively correlates with the temperature sensor but does not correlate with the conductivity sensor. This finding can help to better develop the predictive maintenance strategy for the reactor monitoring program. © 2023 Institute of Advanced Engineering and Science. All rights reserved.