Classification Models Based on Association Rules for Estimation of Key Process Variables in Nuclear Power Plant
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.References
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[10] S. Narasimhan and Rajendran. Application of Data Mining Techniques for Sensor Drift Analysis to Optimize Nuclear Power Plant Performance. International Journal of Innovative Technology and Exploring Engineering, 9(1):3087–3095, nov 2019.
[11] V.Rajendran. S.Narasimhan. Optimization of a Process System in Nuclear Power Plant- A Data Mining Approach. Grenze International Journal of Engineering and Technology, Special Issue, Grenze ID:6.2.1, 2020.
[12] Jian Pei. Jiawei Han, Micheline Kamber. Data mining : concepts and techniques. Morgan Kaufmann Publishers, 2012.
[13] Wenmin Li, Jiawei Han, and Jian Pei. CMAR: accurate and efficient classification based on multiple class-association rules. In Proceedings 2001 IEEE International Conference on Data Mining. IEEE Comput. Soc, 2001.
[14] Xiaoxin Yin and Jiawei Han. CPAR: Classification based on Predictive Association Rules. In Proceedings of the 2003 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, may 2003.
[15] Coenen, F. LUCS-KDD implementations of CPR (Classification based on Predictive Association Rules).
[16] Hahsler M, Johnson I. Classification Based on
Association Rules [R package arulesCBA version 1.2.0].
[17] Max Kuhn. Classification and Regression Training [R package caret version 6.0-86]. Comprehensive R Archive Network (CRAN), 2020 web page = https://cran.r-project.org/package=caret.
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Lisacek Fr´ed´erique Sanchez Jean-charles Mu¨ller Markus Robin, Xavier Turck. pROC : an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics, Issue 12, 2011.
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: 26 dec. 2024.
Issue
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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