Artifi cial neural network for predicting nuclear power plant dynamic behaviors

M. El-Sefy; A. Yosri; W. El-Dakhakhni; S. Nagasaki; L. Wiebe

doi:https://doi.org/10.1016/j.net.2021.05.003

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자료유형: 학술저널

저자정보: M. El-Sefy (McMaster University) A. Yosri (McMaster University) W. El-Dakhakhni (McMaster University) S. Nagasaki (McMaster University) L. Wiebe (McMaster University)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제53권 제10호

발행연도: 2021.10

수록면: 3,275 - 3,285 (11page)

DOI: https://doi.org/10.1016/j.net.2021.05.003

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A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usuallymonitored continuously, resulting in very large amounts of data. This situation makes it possible tointegrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPPoperation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based onpatterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. Inthis study, a feed-forward backpropagation artificial neural network (ANN) model was trained tosimulate the interaction between the reactor core and the primary and secondary coolant systems in apressurized water reactor. The transients used for model training included perturbations in reactivity,steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in thesetransients. Eight training functions were adopted during the training stage to develop the most efficientnetwork. The developed ANN model predictions were subsequently tested successfully consideringdifferent new transients. Overall, through prompt prediction of NPP behavior under different transients,the study aims at demonstrating the potential of artificial intelligence to empower rapid emergencyresponse planning and risk mitigation strategies.

#Data-driven models #Artificial intelligence #Artificial neural network #Nuclear power plant #Back-propagation algorithm

참고문헌 (68)

참고문헌 신청

IAEA / 2015 / Accident Monitoring Systems for Nuclear Power Plants, vol. 16 / IAEA

J. H. Min / 2019 / Demonstration of the validity of the early warning in online monitoring system for nuclear power plants / Nucl. Eng. Des 349:56~62

N. Tamimi / An Artificial Intelligence Decision Support System for Unconventional Field Development Design

I.S. Korovin / 2015 / Modern decision support systems in oil industry:types, approaches and applications / Int. Conf. Test, Meas. Comput. Method :141~144

Sajjad Ahmad / 2006 / An Intelligent Decision Support System for Management of Floods / Water Resources Management 20(3):391~410

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