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

开发用于低放废物表征和分类的机器学习工具

• 批准号: 571324-2021
• 负责人: Li, ZoeZ
• 金额: $ 3.46万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749234
• 关键词: Development; machine; learning; tools; characterization

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.

由于低放废物（LLW）体积庞大，因此核电运营中低放废物（LLW）的储存和处置是一项艰巨的挑战。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来满足低放废物快速增长的数量是不可持续的，也是不经济的。作为替代方案，可以通过LLW分类和隔离来优化现有的存储容量。然而，在商业实践中，LLW材料的表征和分选只能以非常粗糙、低技术和劳动密集型的方式手动完成。没有用于商业应用的现有自动LLW分选工具。本研究的目标是利用先进的机器学习的力量，并开发自动工具的表征和排序的低放废物从安大略的核舰队的运作。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。该研究的预期成果是：1）对核废物处理、表征、分选和分离技术进行全面的文献综述; 2）开发一个交互式LLW数据库，用于跟踪LLW和有效管理LLW数据; 3）开发基于机器学习的LLW表征和分选工具，用于处理LLW图像和预测LLW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LLW表征和优化核废物分类。它将为日益严重的低放废物问题提供长期解决方案，并有助于提高加拿大核能工业作为世界领导者的能力。