Wider Research Landscape
A consortium of nine research projects were funded through the same EPSRC funding call ‘Research for a digitally enabled circular economy and sustainable digital technologies’ which funded the DICE Network+.
All exploring different areas digital technologies and different circular interventions, these projects are led by academic experts in their fields, from UK universities and collaborating with a wider group of academic, industry and community partners.
Our objective through the network is to help amplify the ground-breaking research of these projects, connecting with wider stakeholder groups to raise awareness and application of the research. DICE Network+ activities will include partners from this research community and you can either connect directly or contact us for an introduction.
DECHI: Digitally Enabled Circular Healthcare Innovation
This programme researches new digital approaches to expedite the adoption of circular innovation within the MedTech sector. Uniting leading interdisciplinary research expertise from the Universities of Exeter, Sheffield, and Cambridge, the programme will research new digital approaches to present a pioneering pathway to expedite the adoption of circular innovation within the MedTech sector.
Work packages with industry partners will investigate how digital technologies can accelerate the transition to a more sustainable and resilient healthcare system through data analytics, simulation and modelling techniques, and other technological advancements such as sensorisation, enhanced sterilisation and asset tracking.
Reimagining digital research infrastructure in environmental science for a sustainable future
Innovations in ‘Digital Research Infrastructure’ (DRI) are revolutionising data-driven approaches to environmental science. Models, AI, digital twins and IoT have transformative potential for monitoring, computational data science, and tools for sharing and visualising scientific insights. At the same time, DRI also creates significant and growing impacts for the environment – emitting carbon emissions in its creation, use and disposal, generating significant e-waste, and causing issues of equity and social justice.
A cross-disciplinary team from the UK Centre for Ecology & Hydrology, Lancaster University and Small World Consulting will explore this tension between the gains and impacts of DRI in environmental science. Working across disciplines and closely with stakeholders through case studies focusing on ‘land use for net zero’, the project will co-design and reimagine a future of sustainable digital research, with the aim of creating new tools, data science techniques and innovation processes that support sustainable digital research in environmental science.
Towards a more sustainable High Performance Computing sector: a hardware/software co-design proof-of-concept
The research project focuses on developing a more sustainable High-Performance Computing (HPC) sector through a hardware/software co-design approach. Led by an interdisciplinary team from Imperial College London, the University of Cambridge, and the University of Edinburgh, the research integrates expertise in HPC, computer science, computational fluid dynamics, innovation, and sustainability.
Given the rising energy consumption of HPC due to artificial intelligence and big data, the project explores the use of configurable and customizable processing technologies to optimize energy efficiency. We will demonstrate that it is possible to use energy efficient hardware to carry out research for a more sustainable society, with a focus on the optimisation of the performance of large-scale wind farms, creating a virtuous sustainability cycle by applying our energy-efficient computing innovations to wind farm optimisation.
In this project, we will also undertake a comprehensive assessment for a more sustainable HPC sector, by carrying out manufacturing and supply chain analyses, energy and resource audits, as well as determine HPC users’ awareness of and sentiment towards environmental sustainability.
We will aim to deliver practical and timely actionable insights to key stakeholders and enable beneficiaries to realise real-world impacts on the pathway to a more sustainable HPC sector within and beyond the lifetime of the project. In particular, we will transfer our findings, achievements and scientific outcomes to policy-making with a dedicated work package. The anticipated outcomes include a roadmap for sustainable HPC practices and contributions to government policy on energy-efficient computing.
RoboTriage: Robotic Triage for Value Retention in a Circular Economy
Current Circular Economy practices often underutilise the potential of end-of-life products, with many being inefficiently recycled. This research addresses the critical need for scalable methods to separate heterogeneous used products for optimal valorisation (reuse, repair, remanufacturing, repurposing). Existing evaluation relies on subjective human judgment, leading to inefficiencies.
This research introduces “circularity triage,” a concept inspired by healthcare triage, involving the rapid, systematic assessment of products to determine their highest-value CE pathway. We aim to develop robotic systems capable of performing this triage by capturing product health data, enabling swift condition evaluation, segregation of similar items, and recommendations for optimal CE options.
The project’s objectives include creating robotic triage systems, developing intelligent planning for these systems, identifying correlations between product history and condition via large-scale robotic analysis, exploring value opportunities and circular business models based on these insights, and demonstrating industrial uptake through case studies.
This research promises significant academic impact across ICT, AI, and data science, with broader economic, societal, and environmental benefits. RoboTriage technologies offer the potential to scale CE practices, enhance productivity, and drastically reduce primary material use and energy consumption, exemplified by our industrial partnerships and international collaborations with the UN agencies.
Circular Robot 5.0: Industry-Wide Data-Driven Circular Economy of Industrial Robots
Circular Robot 5.0 is a research project led by the Royal College of Art, in collaboration with Loughborough University, King’s College London, UCL, and the Manufacturing Technology Centre (MTC), alongside industry partners including NVIDIA, OMRON, ASTM International, Katlas Technology, Wootzano, and Inovo Robotics. The project aims to extend the useful life of industrial robots and reduce waste by embedding circular economy principles into robot design, operation, and end-of-life processes. By integrating AI, blockchain, and intelligent lifecycle planning, and viewing challenges through a design-led lens, Circular Robot 5.0 is developing new frameworks and demonstrators to promote remanufacturing, reuse, and recycling of robotic systems and embedded critical raw materials.
PLASTIC: Plastics Analysis, Sorting & Recycling Technologies Through Intelligent Classification
The PLASTIC project aims to revolutionize plastic recycling by leveraging advanced machine learning (ML) and artificial intelligence (AI) technologies. PLASTIC addresses the critical issue of variability in plastic waste quality within the circular economy. By developing intelligent sorting and mechanical recycling systems, PLASTIC seeks to maximize throbe use of post-consumer and post-industrial recyclate in high-value products, minimizing reliance on virgin polymers. The project integrates real-time process monitoring and rheological analysis to predict the properties and processability of recycled plastics, ensuring efficient upgrading and remanufacturing. Led by a team of experts at WMG, University of Warwick, PLASTIC is poised to contribute significantly to sustainable manufacturing practices and environmental conservation, paving the way for a zero-waste future.
DECIDE: Co-creating equitable circular food systems through a digital Hub: Digital Equitable CIrcular FooD systEms
VISION
DECIDE brings together an interdisciplinary team working with industry experts, SMEs and third-sector organisations from the food sector to co-create a digitally enabled resilient and equitable food system in line with the circular economy (CE).
DECIDE has a clear focus on driving a Circular Food System (CFS) in the UK apple supply chain (SC) by leveraging digital capabilities and involving farmers and actors involved in, processing, storage and preservation, transportation, retail and redistribution, waste and surplus use/disposal.
The project will contribute to food security, enhanced economic sustainability and reduce environmental impact.
This sector is an ideal context to conduct this research as apples are a cornerstone of fruit production in the UK, hold significant heritage value as well as support a local and national economic infrastructure. Outputs and outcomes from the project will provide a blueprint for CFS for other product SCs.
AIMS AND OBJECTIVES
DECIDE’s aim is to co-create a digital Hub solution to enable a UK CFS in the context of the apple sector. This
overarching aim translates into the following objectives:
O1. Leverage interdisciplinary and participatory approaches from computer, SC management, agricultural, environmental, and social sciences to offer novel and equitable ways to drive the UK’s transition to CFS.
O2. Co-design digital services grounded in the needs, preferences, values, capabilities, and constraints of food system actors to enable the transition to CFS and create value for local actors and for nature.
O3. Support circular decision-making in the food system through mapping, integration and evaluation of economic, social, and environmental flows and data as well as by optimising the SC.
O4. Prototype and deploy a digital Hub solution pilot centred on apples and provide new scalable and adaptable learnings for other contexts.
O5. Disseminate findings and learnings from the work widely through innovative physical and virtual means to inform local and national policy and practice.
SUMER: Digitally enabled sustainable metals recycling for circular economies
SUMER’s vision is to establish sustainable digital technologies for a circular economy based on advanced separation and recycling technologies of electronic waste materials.
Objectives:
- Explore sources and make up of electronic waste and probe public perspectives
- Develop modelling framework to identify optimal sustainable flowsheets for circularity in electronic waste treatment
- Develop intensified separation processes for waste recycling with non-organic solvents
- Digitisation of separation processes with integrating sensing and control (Digital Twin Development)
- Lead technology translation and dissemination. Modelling to be used for public outreach on e-waste recycling.
SUMER specific objectives:
Create a multi-criteria optimization-based methodology for the design of instrumented, continuous, controllable modular small channel processes, taking into account model uncertainty and variable feeds for robust performance (PROCESS LEVEL)
- Develop predictive dynamic models of process steps for use in optimization, with uncertainty quantification for robust operation, using model-based experimental design of experiments (MBDoE) techniques (UNIT LEVEL)
- Establish experimental layouts of intensified processing channels with modular scale out and embedded sensors for data generation and model validation, for a range of conditions (EXPERIMENTAL LEVEL)
- Develop prototype digital twin units to aid technology translation and dissemination (INTEGRATION LEVEL)
IDEAL: Reducing Carbon Footprints of IoT Devices through Extension of Active Lifespans
The IDEAL project aims to significantly extend the lifespan of Internet of Things (IoT) devices—from a few years to several decades—to reduce their carbon footprint, particularly the embodied carbon from manufacturing. With IoT devices projected to reach 30 billion by 2030, this initiative combines innovative hardware design, software co-design, machine learning, formal methods, and circular economy principles. The project also explores sustainable business models that support long-lived devices. A key feature is embedding ultra-low power monitoring and self-healing capabilities directly into integrated circuits. These systems will use edge-based machine learning to detect and address early signs of degradation, enabling devices to be repurposed for new tasks. Secure and correct communication is ensured through formal methods like session types. This subsystem integrates into existing IoT architectures—edge, fog, and cloud—to reduce lifecycle emissions and support IoT-as-a-service. The approach fosters new business opportunities in device repurposing and adaptive monitoring services.