In situ characterisation of liquids in decommissioning environments by LIBS

利用 LIBS 对退役环境中的液体进行原位表征

• 批准号: 2860401
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2860401
• 关键词: situ; characterisation; liquids; decommissioning; environments

Liquid waste streams and disposal ponds are key parts of the nuclear decommissioning challenge. Consequently, characterisation of these environments is an important aspect with sampling and off-line analysis being the conventional approach. This project aims to investigate the in situ characterisation of these environments using laser induced breakdown spectroscopy (LIBS) which can provide rapid elemental analysis. We have demonstrated success in preliminary measurements on molten salts in the AFPC-pyroprocessing project by measuring from the liquid surface but there are further questions to answer regarding whether the sampling is representative of the bulk as well as by applying multivariate data analytics and machine learning to the derived spectra. We plan to explore representative sampling by comparing 'dip-stick' sampling and direct LIBS measurement on the dip-stick as well as the development of a LIBS probe to directly sample the liquid bulk. The latter would be initially developed and trialled for ambient temperature environments. Comparisons will be made with extracted samples measured off-line by ICP. LIBS underwater or molten salt probe measurements are particularly challenging and we will build on the experience of the bespoke LIBS and Raman systems that we have built in our laboratory for robotic application on the TORONE project (www.torone-project.com). The project would make use of the NNL LIBS instrument and be based at NNL Workington in the CINDe laboratory.

液体废物流和处置池是核退役挑战的关键部分。因此，这些环境的表征是一个重要方面，采样和离线分析是传统方法。该项目旨在利用可提供快速元素分析的激光诱导击穿光谱 (LIBS) 研究这些环境的原位表征。通过从液体表面进行测量，我们已经在 AFPC 高温处理项目中的熔盐初步测量中取得了成功，但还有更多问题需要回答，即采样是否代表整体以及通过应用多元数据分析和机器学习来得出的光谱。我们计划通过比较“量油尺”采样和量油尺上的直接 LIBS 测量以及开发 LIBS 探针来直接对液体本体进行采样来探索代表性采样。后者最初将在环境温度环境下进行开发和试验。将与通过 ICP 离线测量的提取样品进行比较。 LIBS 水下或熔盐探头测量尤其具有挑战性，我们将借鉴我们在实验室中为 TORONE 项目 (www.torone-project.com) 的机器人应用构建的定制 LIBS 和拉曼系统的经验。该项目将利用 NNL LIBS 仪器，并以 NNL 沃金顿的 CINDe 实验室为基地。