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

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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.