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A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management

  • Thiagarajan Chenniappan and Yuvarajan Devarajan ORCID logo EMAIL logo
Published/Copyright: October 21, 2024
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Kerntechnik
From the journal Kerntechnik Volume 89 Issue 5

Abstract

Controlling and managing nuclear waste is a significant challenge due to the harmful effects of radioactive materials on human health. To address this, long-term storage solutions are essential. Artificial Intelligence (AI) and Machine Learning (ML) are being utilized to make nuclear waste management safer, more effective, and efficient. This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification. AI and ML enhance real-time monitoring of storage conditions and optimize waste handling procedures through advanced data processing capabilities. Implementing cutting-edge solutions is crucial to protect public health and the environment from radioactive waste. The purpose of this evaluation is to examine how AI and ML improve nuclear waste management processes. These technologies can reduce human exposure to harmful materials and increase the safety and efficiency of managing nuclear waste through advanced predictive capabilities. The introduction of AI and ML in nuclear waste management is driving significant changes and innovations, addressing current issues, and establishing new guidelines for future policies.

Keywords: nuclear waste; artificial intelligence; machine learning; waste generation; reduce waste generation; waste-to-value
Corresponding author: Yuvarajan Devarajan, Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, Tamil Nadu, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-07-25
Accepted: 2024-09-24
Published Online: 2024-10-21
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Comprehensive review of surface contamination in nuclear waste waters: identification, quantification, and mitigation strategies
  3. Methodology of probabilistic safety assessment for transportation of radioactive material
  4. A new approach to determine abnormality of radioactive discharges from pressurized water reactors and to derive abnormality indicators correlated with a specific causal event
  5. A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management
  6. Design study of gas-cooled fast reactor with natural uranium as fuel employing modified CANDLE shuffling strategy in the axial direction
  7. Synthesis, structural transformation and magnetic properties of the Nd(III)-doped Fe3−xNd x O4 (0 ≤ x ≤ 0.9): an analogue for actinicles immobilization
  8. Examination of the use of thorium-based fuel for burning minor actinides in European sodium cooled fast reactor
  9. Solitary wave form of reaction rate in graphite diffusive medium using different neutron absorbers
  10. Evaluation of the unavailability of the primary circuit of Triga SSR reactor, importance factors and risk criteria for its components
  11. Thermal-hydraulic simulation of loss of flow accident for WWR-S research reactor
  12. A quick parameter configuration tool for SCHISM’s ocean transport simulation of radioactive materials
  13. Main heat transport system configuration influence on steam drum level control and safety for a pressure tube type boiling water reactor with multiple interconnected loops
  14. Testing the thermal performance of water cooling towers
  15. Design a robust intelligent power controller for pressurized water reactor using particle swarm optimization algorithm
  16. Calendar of events
https://doi.org/10.1515/kern-2024-0085
Keywords for this article
nuclear waste; artificial intelligence; machine learning; waste generation; reduce waste generation; waste-to-value

Articles in the same Issue

  1. Frontmatter
  2. Comprehensive review of surface contamination in nuclear waste waters: identification, quantification, and mitigation strategies
  3. Methodology of probabilistic safety assessment for transportation of radioactive material
  4. A new approach to determine abnormality of radioactive discharges from pressurized water reactors and to derive abnormality indicators correlated with a specific causal event
  5. A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management
  6. Design study of gas-cooled fast reactor with natural uranium as fuel employing modified CANDLE shuffling strategy in the axial direction
  7. Synthesis, structural transformation and magnetic properties of the Nd(III)-doped Fe3−xNd x O4 (0 ≤ x ≤ 0.9): an analogue for actinicles immobilization
  8. Examination of the use of thorium-based fuel for burning minor actinides in European sodium cooled fast reactor
  9. Solitary wave form of reaction rate in graphite diffusive medium using different neutron absorbers
  10. Evaluation of the unavailability of the primary circuit of Triga SSR reactor, importance factors and risk criteria for its components
  11. Thermal-hydraulic simulation of loss of flow accident for WWR-S research reactor
  12. A quick parameter configuration tool for SCHISM’s ocean transport simulation of radioactive materials
  13. Main heat transport system configuration influence on steam drum level control and safety for a pressure tube type boiling water reactor with multiple interconnected loops
  14. Testing the thermal performance of water cooling towers
  15. Design a robust intelligent power controller for pressurized water reactor using particle swarm optimization algorithm
  16. Calendar of events
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