Artificial Intelligence for Nuclear Waste Management: Opportunities, Challenges, and Future Prospects

Abstract

This study explores the transformative potential of artificial intelligence (AI) in revolutionizing nuclear waste management (NWM) by optimizing key processes, including waste classification, treatment, storage, and disposal. Leveraging advanced machine learning algorithms and data analytics, AI significantly enhances the precision and efficiency of waste categorization, facilitating more informed and systematic decision-making. Furthermore, AI-driven optimization techniques refine treatment methodologies, mitigate operational risks, and ensure stringent compliance with regulatory frameworks, thereby contributing to the safer and more sustainable handling of radioactive materials. These advancements not only enhance overall operational efficiency but also strengthen predictive modeling capabilities, enabling more accurate risk assessments and strategic planning. The integration of AI into NWM empowers stakeholders to navigate complex regulatory landscapes more effectively while minimizing ecological impacts and reinforcing public safety. This study identifies key research and development priorities for advancing AI-augmented NWM, including the refinement of AI algorithms for real-time monitoring, predictive analytics, and early anomaly detection, facilitating a proactive approach to risk mitigation. Additionally, emerging technologies such as robotic automation and autonomous systems present unprecedented opportunities to reduce human exposure to hazardous environments by streamlining waste handling operations. The continuous evolution of AI underscores its transformative potential in addressing the critical challenges associated with NWM, ensuring the secure, responsible, and long-term stewardship of radioactive materials for future generations.