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
    CountryUnited States
    CityOrlando
    Period2/9/192/14/19

    Fingerprint

    Loss of coolant accidents
    Boiling liquids
    Learning systems
    Neural networks
    Feedforward neural networks
    Water
    Identification (control systems)
    Decision making
    Monitoring
    Sensors
    Processing

    All Science Journal Classification (ASJC) codes

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

    Cite this

    Alamaniotis, M., & Ray, A. (2019). A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR. In 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019 (pp. 431-439). (11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019). American Nuclear Society.
    Alamaniotis, Miltiadis ; Ray, Asok. / A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR. 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019. American Nuclear Society, 2019. pp. 431-439 (11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019).
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    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.",
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    Alamaniotis, M & Ray, A 2019, A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR. in 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019. 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019, American Nuclear Society, pp. 431-439, 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019, Orlando, United States, 2/9/19.

    A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR. / Alamaniotis, Miltiadis; Ray, Asok.

    11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019. American Nuclear Society, 2019. p. 431-439 (11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019).

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

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    Alamaniotis M, Ray A. A machine learning method integrating neural networks and Gaussian processes for LOCA identification in BWR. In 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019. American Nuclear Society. 2019. p. 431-439. (11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019).