Development of machine learning tools for the characterization and sorting of low level waste

核电作业产生的低放废物的储存和处置是一项艰巨的挑战，因为低放废物的数量很大。低放废物通常储存在指定的储存设施中；然而，继续建造新的储存设施以满足快速增长的低放废物数量是不可持续的，也不经济。作为另一种选择，现有的存储容量可以通过LLW分类和分离来优化。然而，商业实践中对低放废物材料的表征和分类只能通过手工完成，以一种非常粗糙、低技术和劳动密集型的方式。目前还没有用于商业应用的LLW自动分类工具。这项研究的目标是利用先进的机器学习的力量，开发自动工具来表征和分类安大略省核舰队作业的低生活垃圾。这项研究将与核能行业的创新者和领导者Laurentis Energy Partners合作进行。这项研究的预期成果是：1)关于核废物处理、表征、分类和分离技术的全面文献综述；2)开发用于跟踪低生活垃圾和有效管理低生活垃圾数据的交互式低生活垃圾数据库；以及3)开发基于机器学习的低生活垃圾表征和分类工具，用于处理低生活垃圾图像和预测低生活垃圾含量。这项工作将导致开发最先进的机器学习工具，用于自动表征低放废物和优化核废料分类。它将为日益严重的低放废物产量问题提供长期解决方案，并有助于增强加拿大核能行业作为世界领先者的能力。

The storage and disposal of Low Level Waste (LLW) from nuclear power operations is a daunting challenge due to the large LLW volumes. LLW are typically stored in designated storage facilities; however, continuing to build new storage facilities to meet the fast growing volumes of LLW is not sustainable or economic. As an alternative, the existing storage capacity can be optimized by LLW sorting and segregating. However, the characterization and sorting of LLW materials in commercial practices can only be done manually, in a very crude, low-technology, and labor-intensive way. There is no existing automatic LLW sorting tools for commercial applications. The goal of this research is to leverage the power of advanced machine learning and develop automatic tools for the characterization and sorting of LLW from Ontario's nuclear fleet operation. This research will be conducted in collaboration with Laurentis Energy Partners, which is an innovator and leader in the nuclear energy industry. The anticipated outcomes of this research are: 1) a comprehensive literature review on nuclear waste handling, characterization, sorting, and segregation technologies; 2) development of an interactive LLW database for tracking LLW and effectively managing LLW data; and 3) development of machine-learning-based LLW characterization and sorting tools for processing LLW images and predicting LLW content. This work will lead to the development of state-of-the-art machine learning tools for automatic LLW characterization and optimized nuclear waste sorting. It will provide a long-term solution to the growing issue of LLW volumes and help enhance the capacity of Canadian nuclear energy industry as a world leader.