CFP last date
15 January 2025
Call for Paper
February Edition
IJAIS solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 15 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Event Identification in Prototype Fast Breeder Reactor Subsystem using Artificial Neural Network

by Subhra Rani Patra, R. Jehadeesan, H. Seetha, T. Jayanthi, S. Rajeswari, S.A.V Satya Murty, M. Sai Baba
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 1
Year of Publication: 2012
Authors: Subhra Rani Patra, R. Jehadeesan, H. Seetha, T. Jayanthi, S. Rajeswari, S.A.V Satya Murty, M. Sai Baba
10.5120/ijais12-450605

Subhra Rani Patra, R. Jehadeesan, H. Seetha, T. Jayanthi, S. Rajeswari, S.A.V Satya Murty, M. Sai Baba . Event Identification in Prototype Fast Breeder Reactor Subsystem using Artificial Neural Network. International Journal of Applied Information Systems. 4, 1 ( September 2012), 1-9. DOI=10.5120/ijais12-450605

@article{ 10.5120/ijais12-450605,
author = { Subhra Rani Patra, R. Jehadeesan, H. Seetha, T. Jayanthi, S. Rajeswari, S.A.V Satya Murty, M. Sai Baba },
title = { Event Identification in Prototype Fast Breeder Reactor Subsystem using Artificial Neural Network },
journal = { International Journal of Applied Information Systems },
issue_date = { September 2012 },
volume = { 4 },
number = { 1 },
month = { September },
year = { 2012 },
issn = { 2249-0868 },
pages = { 1-9 },
numpages = {9},
url = { https://www.ijais.org/archives/volume4/number1/262-0605/ },
doi = { 10.5120/ijais12-450605 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:46:46.329635+05:30
%A Subhra Rani Patra
%A R. Jehadeesan
%A H. Seetha
%A T. Jayanthi
%A S. Rajeswari
%A S.A.V Satya Murty
%A M. Sai Baba
%T Event Identification in Prototype Fast Breeder Reactor Subsystem using Artificial Neural Network
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 4
%N 1
%P 1-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification of events in nuclear power plant is a very challenging task because each event has a unique set of patterns based on the dynamic behavior of the plant. Accordingly the effective set of parameters has to be chosen to identify an event having a particular pattern. This paper describes the development of artificial neural network (ANN) model for event identification of Primary Sodium System of Prototype Fast Breeder Reactor. In reactor under normal operating condition, the Primary Sodium Pump takes the sodium from the cold pool towards the core. Due to some mechanical and electrical problems the Primary Sodium Pump trip and Primary Sodium Pump Seizure may occur which can be identified manually after analyzing the process parameters. The work involves implementation of two ANN models to identify the occurrence of these two events. The effective parameters considered for these two events are SCRAM (Safety Control Rod Accelerated Movement) parameters. The training data for modeling the neural network is prepared using the thermo hydraulics simulation code of PFBR simulator. Multilayer neural network using back propagation algorithm has been widely used for transient identification. The proposed ANN model was able to identify the events correctly and the results obtained are satisfactory.

References
  1. Santosh T. V. , Srivastava A. , Sanyasi Rao V. V. S. , Ghosh A. K. , Kushwaha H. S. ,2009, Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks, Reliability Engineering and system Safety, 759-762.
  2. Varde P. V, Chowdhury R. , Vinod G, Babar A. K, Kushwaha H. S, Simulation of Pressurized Heavy Water Reactor Data Using Artificial Neural Network for Reactor Status/Transient Identification, International Conference on Quality, Reliability and Control(ICQRC-2001), http://www. barc. ernet. in/publications/nl/2002/200210-08. pdf
  3. Sorsa T. , Koivo H. N. , Koivisto H. , Neural Networks in process Fault Diagnosis, 21,1991, IEEE Transactions on Systems, Man and Cybernetics,815-824,2002, http://ieeexplore. ieee. org/xpl/freeabs_all. jsp?arnumber=108299&abstractAccess=no&userType=inst.
  4. Santosh T. V. , Kumar M. , Thangamani I. , Srivastava A. , Dutta A. , Verma V. , Mukhopadhyay D. , Ganju S. , Chatterjee B. , Rao V. V. S. S. , Lele H. G. , Ghosh A. K. , A diagnostics system for identifying accident conditions in a nuclear reactor, Nuclear engineering and design, 241, 177-184, 2011
  5. Parlos A. G. , Fernandez B. , Artificial neural networks based system identification and control of nuclear power plant components, Proceedings of IEEE 29th conference on decision and control, 1703-1706, Vol. 3. http://ieeexplore. ieee. org/Xplore/defdeny. jsp?url=http%3A%2F%2Fieeexplore. ieee. org%2Fstamp%2Fstamp. jsp%3Ftp%3D%26arnumber%3D203911%26userType%3D&denyReason=-133&arnumber=203911&productsMatched=null&userType=
  6. Attieh I. K. , Gribok A. V. , Hines J. W. , Uhrig R. E. , Pattern recognition techniques for transient detection to enhance nuclear reactor's operational safety, Nuclear engineering and design, 207, 207-221, 2001.
  7. Hadad K. , Pourahmadi M. , Majidi-Marahgi H. , Fault diagnosis and classification based wavelet transform and neural network, Progress in nuclear energy, 53, 41-47, 2011
  8. Roverso D. , Neural ensembles for event identification, Proceedings of safeprocess, IFAC symposium on fault detection, supervision for technical process, 2000
  9. Mo K. , Leo S. J. , Seong P. H. , A dynamic neural network aggregation model for transient diagnosis in nuclear power plants, Progress in nuclear technology, 49, 262-272, 2007
  10. Embrechts M. J. , Benedek S. , Hybrid identification of unlabelled nuclear power plant transients with artificial neural networks, Neural network proceedings, The 1998 IEEE International Joint Conference on IEEE World Congress on Computational Intelligence, 2, 1438 - 1443
  11. Zio E. , Maio F D. , Stasi M. , A data-driven approach for predicting failure scenarios in nuclear systems, Annals of nuclear technology, 37, 482-491, 2010.
  12. Ma J. , Jiang J. , Applications of fault detection and diagnosis methods in nuclear power plants: A review, Progress in nuclear energy, 53, 255-266, 2011
  13. Krose B. , Patrick V. D. S. , An introduction to neural network, page-15, 1996, http://www. cs. unibo. it/babaoglu/courses/cas/resources/tutorials/Neural_Nets. pdf
  14. Sivanandam S. N. , Sumathi S. , Deepa S. N. , Introduction to neural networks using MATLAB 6. 0, 20-21, 2009, Tata McGraw Hill Edu. Pvt. Ltd
  15. Yegnanarayana B, Artificial Neural Networks, 88-142, 2008, Prentice-Hall India pvt. Ltd.
  16. Raghupathy S. , Singh O. P. , Govindarajan S. , Chetal S. C. , Bhoje, Design of 500 MWe Prototype Fast Breeder Reactor, http://www. dae. gov. in/ni/nimar04/design. pdf, 2004
  17. Chetal S. C. , Bhoje S. B. etal, Conceptual design of Heat Transport Systems and components of PFBR-NSSS, http://www. iaea. or. at/inisnkm/nkm/aws/fnss/fulltext/28014320. pdf, 1995
  18. N Kasinathan, U Parthasarathy. , K Natesan. , P Selvaraj, P Chellapandi, S C Chetal and S B Bhoje, Internal Report on "Analysis of Design Basis Events for Prototype Fast Breeder Reactor", 1st National Conference on Nuclear Reactor Safety, Mumbai, Nov 2002.
  19. Sivanandam S. N. , Deepa S. N. , Principles of Soft Computing, 74-82, 2010, Tata McGraw Hill Edu. Pvt. Ltd
Index Terms

Computer Science
Information Sciences

Keywords

Back Propagation Algorithms Event Identification

Powered by PhDFocusTM