Classification Models Based on Association Rules for Estimation of Key Process Variables in Nuclear Power Plant

  • Narasimhan S Bharatiya Nabhikiya Vidyut Nigam Limited(BHAVINI) ,Kalpakkam,INDIA & VELS Institute of Science,Technology and Advanced Studies(VISTAS),Chennai, India http://orcid.org/0000-0002-6456-6201
  • Rajendran Velayudham VELS Institute of Science, Technology and Advanced Studies (VISTAS) Chennai, India

Abstract

Nuclear power plant process systems have developed greatly over the years. As a large amount of data is generated from Distributed Control Systems(DCS) with fast computational speed and large storage facilities, smart systems have taken over analysis of the process. These systems are built using data mining concepts to understand the various stable operating regimes of the processes, identify key performance factors, makes estimates and suggest operators to optimize the process. Association rule mining is a frequently used data-mining concept in e-commerce for suggesting closely related and frequently bought products to customers. It also has a very wide application in industries such as bioinformatics, nuclear sciences, trading and marketing. This paper deals with application of these techniques for identification and estimation of key performance variables of  a lubrication system designed for a 2.7 MW centrifugal pump used for reactor cooling in a typical 500MWe nuclear power plant . This paper dwells in detail on predictive model building using three models based on association rules for steady state estimation of key performance indicators (KPIs) of the process. The paper also dwells on evaluation of prediction models with various metrics and selection of best model.

Author Biography

Narasimhan S, Bharatiya Nabhikiya Vidyut Nigam Limited(BHAVINI) ,Kalpakkam,INDIA & VELS Institute of Science,Technology and Advanced Studies(VISTAS),Chennai, India
Scientific Officer/HIT&Instrumentationresearch Scholor in VISTAS

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Published
2020-12-24
How to Cite
S, Narasimhan; VELAYUDHAM, Rajendran. Classification Models Based on Association Rules for Estimation of Key Process Variables in Nuclear Power Plant. Journal of Power Technologies, [S.l.], v. 100, n. 4, p. 315-330, dec. 2020. ISSN 2083-4195. Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1691>. Date accessed: 01 dec. 2024.
Section
Nuclear Power

Keywords

Association Rule Mining, Classification Based on Association, Classification Based on Multiple Association Rules, Classification based on Predictive Association Rules, Data Mining, Data modelling, Data Transformation, Inter Correlation, k-Means Clustering

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