Digitalization as an aggregate performance in the energy transition for nuclear industry

Florencia de los Angeles Renteria del Toro; Chen Hao; Akira Tokuhiro; Mario Gomez-Fernandez; Armando Gomez-Torres

Nuclear Engineering and Technology (Apr 2024)

Digitalization as an aggregate performance in the energy transition for nuclear industry

Florencia de los Angeles Renteria del Toro,
Chen Hao,
Akira Tokuhiro,
Mario Gomez-Fernandez,
Armando Gomez-Torres

Affiliations

Florencia de los Angeles Renteria del Toro: Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, 150001, China
Chen Hao: Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, 150001, China; Corresponding author.
Akira Tokuhiro: Energy Systems and Nuclear Science Research Centre, University of Ontario Institute of Technology, 2000 Simcoe Street North Oshawa, ON L1H 7K4, Canada
Mario Gomez-Fernandez: School of Nuclear Science and Engineering, Oregon State University, 100 Radiation Center, Corvallis, OR, 97330, United States
Armando Gomez-Torres: Department of Nuclear Systems, National Institute for Nuclear Research, Ocoyoacac, 52705, Mexico

Journal volume & issue: Vol. 56, no. 4
pp. 1267 – 1276

Abstract

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The emerging technologies at the industrial level have deployed rapidly within the energy transition process innovations. The nuclear industry incorporates several technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, High-Performance-Computing (HPC) and Quantum Computing (QC), among others. Factors identifications are explained to set up a regulatory framework in the digitalization era, providing new capabilities paths for nuclear technologies in the forthcoming years. The Analytical Network Process (ANP) integrates the quantitative-qualitative decision-making analysis to assess the implementation of different aspects in the digital transformation for the New-Energy Transition Era (NETE) with a Nuclear Power Infrastructure Development (NPID).2023 Elsevier Ltd. All rights reserved.

Published in Nuclear Engineering and Technology

ISSN: 1738-5733 (Print)
Publisher: Elsevier
Country of publisher: Korea, Republic of
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Nuclear engineering. Atomic power
Website: http://www.journals.elsevier.com/nuclear-engineering-and-technology

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