Venue & metadata

• Journal/Proceedings: Procedia Computer Science
• Volume: 225
• Pages: 3133 – 3142
• Note: Cited by: 4; All Open Access, Gold Open Access
• Author keywords: Digital Twins; Model-driven risk assessment; Nuclear Waste Monitoring; Physical Protection Systems

Abstract

In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Keywords

Digital storage GS Radioactive waste storage GS Radioactive wastes GS Computer science and engineerings GS Cutting edges GS Information and Communication Technologies GS Model-driven GS Model-driven risk assessment GS Monitoring system GS Nuclear waste monitoring GS Physical protection systems GS Risks assessments GS Temporary storage GS Risk assessment GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Di Giovanni, M., Campanile, L., D’Onofrio, A., Marrone, S., Marulli, F., Romoli, M., Sabbarese, C., & Verde, L. (2023). Supporting the Development of Digital Twins in Nuclear Waste Monitoring Systems [Conference paper]. Procedia Computer Science, 225, 3133–3142. https://doi.org/10.1016/j.procs.2023.10.307