A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR

Miltiadis Alamaniotis, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Monitoring of Boiling Water Rectors (BWRs) is a complex process that requires the use of a numerous sensors and systems. Acquisition of data and the subsequent processing of it accommodate inference making with regard to the state of the reactor system. System identification promotes decision making with regard to operation action taking. In this paper, we present a new method for serially integrating two machine learning tools and more specifically a neural network and a set of algorithms for learning Gaussian processes. Both sets of tools exhibit learning capabilities, and their integration in the current work offers a two-stage learning schema applied to identification of transient states in BWR. In particular, the proposed methodology utilizes the synergism of a set of Gaussian processes with a feedforward neural network for recognizing the type of loss of coolant accident (LOCA) that occurs in the reactor. The methodology is tested on a set of real-world datasets taken from the FIX-II facility. Results demonstrate efficacy of the method to accurately identify the occurring LOCA among three possible states.

Original languageEnglish (US)
Title of host publication11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
PublisherAmerican Nuclear Society
Pages431-439
Number of pages9
ISBN (Electronic)9780894487835
StatePublished - Jan 1 2019
Event11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019 - Orlando, United States
Duration: Feb 9 2019Feb 14 2019

Publication series

Name11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019

Conference

Conference11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
Country/TerritoryUnited States
CityOrlando
Period2/9/192/14/19

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering
  • Energy Engineering and Power Technology
  • Human-Computer Interaction
  • Control and Systems Engineering

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