Analysing the Swabbing Process to Improve the Accuracy of Characterisation in the Nuclear Industry

Abstract

The nuclear industry requires routine monitoring and the characterisation of environments before they can be decommissioned. Part of the characterisation of potentially contaminated assets is done through analysing swabs that are taken by operators and analysed in laboratories to identify chemical, biological and radiological hazards. The use of human operators in potentially hazardous environments has safety, cost and time implications; and this is accompanied by an understanding that human operators have limited reliability in swabbing scenarios. For these reasons, there is a desire to develop robots that can replace human operators in accessing and characterising radioactive facilities. Swabbing is a sample retrieval process which involves the removal of contamination from a surface using a porous substrate (swab). There is a lack of understanding of the cause of uncertainties in the swabbing process and this hinders the applicability and usefulness of swabbing. This project provides an analysis of swabbing which can inform best practice and aid the development of a new generation of swabbing robots. This thesis makes major contributions to our understanding of swabbing, presenting a systematic evaluation of swabbing inputs, as well as providing the first study which measures human performance in swabbing tasks. In addition to this, a feasibility study which focuses on the use of Chrysoidine G as a chemosensor used for the detection of common radionuclides is presented in this thesis. In addition to the contributions from these experiments, the development of equipment and a number of novel methodologies are detailed. The effect of swabbing force, force application area, contaminant mass and the number of swab passes on the pick-up factor of loose contamination are all studied, with these experiments providing a foundation to better understand the role of different swabbing inputs on the swabbing process. It was found that altering swabbing force did affect swab efficacy, with swabbing force causing a total variation of approximately 16% in mean pick-up factor performance; with a 6% minimum and 22% maximum mean performance. Force application area was found to influence pick-up factor to a greater extent, with an approximate 20% range in mean pick-up factors observed. These results also highlight the importance of leading-edge accumulation, with this mechanism greatly impacting overall swabbing efficacy. Human operators are widely thought to be a large source of swabbing uncertainty, and studies have been undertaken to quantify this uncertainty. It was found that swabbing area recreation had a coefficient of variation (C.O.V.) of approximately 18% and swabbing force recreation was subject to far greater variation with a C.O.V. of 61%. These results provide clear confirmation that human performance in swabbing tasks has a low repeatability, and that human operators do contribute significantly to overall swabbing uncertainty. The repeatability achieved with automated robotic systems in these swabbing tasks is far greater than that achieved by the participants in this study, with these results confirming that robotic platforms could be used to reduce uncertainty in swabbing tasks. Further to increasing sample retrieval capability, the development of in situ sample analysis techniques is a crucial step in removing humans from harm in the characterisation of radioactive facilities. A feasibility study for one such in situ characterisation technique was performed. Chrysoidine G (CG) is seen as a promising candidate for colorimetric detection of the abundant fission products, Sr-90 and Cs-137, which are known to be prevalent across many nuclear sites. This sensor's performance in likely conditions for its use is assessed. Ultimately the response of this sensor was found to be incompatible for use on an in situ characterisation robot. The colorimetric response of CG was found to be greatly affected by pH, necessitating careful pH ad

Date of Award	31 Dec 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Simon Watson (Supervisor) & Clint Sharrad (Supervisor)