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| Funder | Innovate UK |
|---|---|
| Recipient Organization | Forth Engineering (Cumbria) Limited |
| Country | United Kingdom |
| Start Date | Mar 01, 2021 |
| End Date | May 30, 2021 |
| Duration | 90 days |
| Data Source | UKRI Gateway to Research |
| Grant ID | 10004562 |
On-going nuclear decommissioning activities in the UK currently cost £3 billion per year and it is estimated that nearly 5 million tonnes of new waste are still to be generated from future decommissioning work spanning the next 100-years. Over 90% of this waste will be categorised as low-level waste (LLW) and its efficient separation from waste with a higher radioactive content (intermediate level waste - ILW) will help to reduce future waste management costs and open up new opportunities for increased recycling of waste.
Utilising an innovative combination of robotics, sensor technology and advanced AI, SmartDecay has the potential to radically improve the efficiency with which different types of intermediate and low-level radioactive waste are sorted and segregated, significantly lowering nuclear waste processing costs. The deployment of (semi-)autonomous robotic systems will result in a reduction in the number of direct human interactions with waste and associated handling equipment.
This helps to minimise exposure to harmful radiation, lower the probability of work-related injuries and reduce the overall risk to operator health. Radiation sensors attached to the robot will measure and record the radioactive content of every piece of waste enabling classification as either ILW or LLW. Automated 3D scanners and X-ray fluorescence equipment will then be utilised to further analyse waste - determining its size/shape through generation of a digitised 3D model and classifying the waste by material type.
After detailed analysis, waste samples will be moved into a temporary segregation area pre-marked with RF-ID tags that will enable the robot to identify specific locations to place waste classified by radioactive content and material type. After segregation, machine learning algorithms will be developed, and high-performance edge computing resources will be deployed to establish optimal packing order for each classification of waste items into appropriate drums and pallet boxes.
Final packing of waste containers will be performed by a robot that will utilise state-of-the-art simultaneous localisation and mapping (SLAM) for autonomous navigation around the segregation area. Effective separation of decommissioning waste according to radioactive content and material type will also help to boost opportunities for increasing recycling activities.
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