A team led by Nuvia Limited is testing a suite of new technologies—including robotics, artificial intelligence, and virtual reality—to dismantle the UK’s most hazardous nuclear waste facilities faster, more safely, and at lower cost. The problem is urgent. Sellafield’s ageing process plants contain highly radioactive materials that must be handled remotely. Current decommissioning methods are slow, expensive, and expose workers to risk. The project aims to replace manual operations with teleoperable robots, 3D modelling, and AI-guided expert systems that can plan and supervise each step—from initial characterisation through to waste packaging and export. If successful, the technologies could cut the time and cost of cleaning up the UK’s nuclear legacy while reducing human exposure to radiation. The consortium includes industrial partners with global reach, meaning proven systems could be exported to other countries facing similar decommissioning challenges. The project also integrates virtual and augmented reality tools for operator training and safety case development, which could improve preparedness before any physical work begins. This is applied engineering, not fundamental science. The goal is to demonstrate feasibility in a real-world setting, with Phase 1 assessing which technologies are ready for inactive and then active trials.
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Nuvia Limited, a UK based, international nuclear engineering, project management and services contractor has teamed up with a number of the UK's top innovative technology providers in response to Innovate UK's SBRI IIND funding competition. The title of the project is "Nu-Decom" and that reflects the aim of the project which is to demonstrate a range of new decommissioning technologies that will deliver the safer, cheaper and quicker decommissioning of the UK's nuclear legacy. Nuvia have assembled a comprehensive team suited for the complex and wide ranging challenges associated with the decommissioning of the active process plants on the Sellafield site. The team includes expertise from both industry and academia, and the fields of expertise include: (1) Teleoperable systems, advanced robotics & semi-autonomous systems for challenging environments. (2) Remote radiometrics and geometric characterisation. (3) The post processing of geometric data into 3D modelling and visualisation software packages. (4) The use of games technology, virtual (VR) and augmented reality (AR) technologies to provide immersive environments for training and communications purposes. (5) The application of Artificial Intelligence (AI) to provide a knowledge base and expert systems that can be used to support and guide remote operations across the UK's decommissioning program. (6) Application of Human Factors principles. These innovative new technologies and techniques will be integrated with Nuvia's own tried and tested nuclear decommissioning expertise and knowhow which has been successfully deployed throughout the UK and on the global decommissioning market thus providing an established route to a global market for UK Technologies. The Project Team's industrial partners include:- Nuvia (Project Lead), Clicks & Links (C&L), Hu-Tech, MOOG, PaR Systems, Tacit-Connections (TC), UKAEA RACE and Imitec. The Project Team's academic partners include: University of Manchester (UoM) and the University of Bristol (UoB). As part of Phase 1 the project team will assess each step in a typical decommissioning process including:- initial characterisation, decontamination, the design & manufacture process, operator training, HAZOP and safety case processes, site preparation, installation & commissioning, Safe Systems of Work (SSoW), the Human Machine Interface (HMI), remote operations, size reduction, consumable management, materials handling, routine maintenance, remote intervention & repair, waste export routes, ex-situ size reduction, assay monitoring, waste sorting, waste sentencing & segregation, waste packaging, waste container handling and finally export from the facility. Phase 1 will investigate the feasibility of applying the range of innovative technology platforms to each of the above steps and rank and rate each of the applications and potential benefits against the required criteria i.e. improved productivity, minimising human intervention, transferability, scalability and optimising waste treatment packing and routing into the most cost efficient waste streams. The project will also consider the Technology Readiness Levels (TRL) and the cost to develop technologies to a level suitable for (a) Inactive Demonstration (Phase 2) and (b) active demonstration. The project will prioritise and make recommendations for which platforms go forward to the Phase 2 inactive demonstration.
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