A Novel Integrated Framework for Safe Nuclear Waste Management: Synergizing Deep Geological Disposal with Advanced Material Immobilization and AI-Driven Monitoring

The safe and sustainable management of high-level nuclear waste (HLW) remains a critical global challenge, demanding innovative strategies that transcend conventional geological disposal. This paper introduces a novel, multi-barrier framework integrating next-generation immobilization matrices, autonomous robotic handling, and a deep-learning-enhanced Internet of Things (AIoT) network for perpetual monitoring. We critically review the limitations of current borosilicate glass vitrification and propose a shift towards synthetic rock (SYNROC) and single-phase ceramic matrices, such as Zirconolite and Pyrochlore, for superior actinide containment and radiation stability. The core innovation lies in a symbiotic system where advanced waste forms are encapsulated in additively manufactured, corrosion-resistant alloy canisters (e.g., Inconel ۷۱۸) and emplaced in a deep geological repository (DGR) equipped with a distributed fiber optic and acoustic sensor network. A novel physics-informed neural network (PINN) processes real-time multi-physics data to predict long-term canister corrosion and radionuclide migration, enabling proactive risk mitigation. Our multi-scale simulation and experimental validation demonstrate a potential ۴۰% improvement in long-term isolation capacity compared to conventional methods, establishing a new paradigm for proactive, data-driven nuclear waste stewardship.

کلیدواژه ها:

High-Level Nuclear Waste ، Deep Geological Repository ، Advanced Immobilization ، SYNROC ، AIoT Monitoring ، Physics-Informed Neural Networks