Application of automated and robotically deployed in situ X-ray fluorescence analysis for nuclear waste management

Sam R White*, Peter George Martin, David Megson-Smith, Thomas Bligh Scott

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

Laboratory and synchrotron X-ray fluorescence (XRF) analysis has both served as mainstay rapid and quantitative elemental analysis techniques for decades, attaining parts per million sensitivities for the majority of elements. Formerly, XRF was the reserve of large X-ray generating systems and national facilities. More recently, developments in miniaturized X-ray generators and detectors have allowed for this nondestructive technique to be utilized for portable and in situ elemental characterization of materials, away from the confines of the laboratory. When combined with a robotic manipulator, these usually handheld systems present a powerful method for autonomous assessments of material composition for a wide range of nuclear characterization and decommissioning scenarios. In this study, we present a proof-of-concept XRF system integrated with a robotic manipulator to autonomously identify a suite of nuclear relevant materials. Such remotely deployable noncontact tools are crucial for use within hazardous environments where it may not be possible, for physical and safety reasons, for a human operator to manually undertake characterization tasks. It is envisaged that this robotically deployed XRF system will comprise part of the wider autonomous characterization “toolkit”; capable of extensive large-area mapping alongside targeted compositional “point analysis.” The system was demonstrated to rapidly and repeatably derive accurate and precise compositional information of different test materials, autonomously on both flat and complex, object-rich surfaces.
Original languageEnglish
Pages (from-to)1205-1217
Number of pages13
JournalJournal of Field Robotics
Volume39
Issue number8
Early online date28 Jun 2022
DOIs
Publication statusPublished - 26 Oct 2022

Bibliographical note

Funding Information:
We acknowledge the support of the University of Bristol National Nuclear User Facility (NNUF) for Hot Robotics, funded by BEIS and EPSRC. In particular Sabrina Shirazi for her assistance with hiring the KUKA LBR and Olympus Vanta systems. We would also like to thank James Parker at Olympus Corporation, for his support with the XRF system. Bartosz Dworzanski for his valuable technical support. Jim Brooke, DRPS for the School of Physics, for his assistance in ensuring the XRF system was robotically deployed safely during this study. The UK Research and Innovation with funding from the Engineering and Physical Sciences Research Council (EPSRC) towards the Robotics and AI in Nuclear (RAIN) research hub (EP/R026084/1), the National Centre for Nuclear Robotics (NCNR) (EP/R02572X/1), and the National Nuclear User Facility for Hot Robotics (EP/T011491/1).

Funding Information:
We acknowledge the support of the University of Bristol National Nuclear User Facility (NNUF) for Hot Robotics, funded by BEIS and EPSRC. In particular Sabrina Shirazi for her assistance with hiring the KUKA LBR and Olympus Vanta systems. We would also like to thank James Parker at Olympus Corporation, for his support with the XRF system. Bartosz Dworzanski for his valuable technical support. Jim Brooke, DRPS for the School of Physics, for his assistance in ensuring the XRF system was robotically deployed safely during this study. The UK Research and Innovation with funding from the Engineering and Physical Sciences Research Council (EPSRC) towards the Robotics and AI in Nuclear (RAIN) research hub (EP/R026084/1), the National Centre for Nuclear Robotics (NCNR) (EP/R02572X/1), and the National Nuclear User Facility for Hot Robotics (EP/T011491/1).

Publisher Copyright:
© 2022 The Authors. Journal of Field Robotics published by Wiley Periodicals LLC.

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