SuperAIRE aims to establish a world-leading network connecting academia, industries, and policymakers across the spectrum of artificial intelligence (AI) for renewable energy (RE), particularly wind, solar, marine, and bio energy. This includes generation, storage, transmission/distribution and demand side management. These represent most of the research areas in the UKRI's Energy and Decarbonisation theme. With SuperAIRE, we aim to create the conditions in which AI for RE can be promoted much more rapidly than at present to boost the development and deployment of RE. We will not only exploit the transformative power of AI in different RE subsectors but also address common challenges and optimise performance across the RE ecosystem. Supported by a broad partnership currently with 30 partners across industry (23), leading R&I organisations (5), and policymakers (2), we will incubate a Supergen AI+RE research community seizing the opportunity to enhance the UK's role as a global leader in the intelligent and digital transformation of the RE sector. Despite the recent growth in all subsectors, progress in essential technologies supporting the lifecycles of RE systems lags behind. AI offers strategic advantages in overcoming the limitations of traditional methods which struggle to process the increasing complexity and big data in RE systems. It will enable decision-supporting digitalisation, operational efficiency optimisation, cost-effective integration, multi-scenario adaptability, and technological cross-applicability. Though there are some current critical masses in AI for RE, the communities are facing many challenges, e.g., the fragmented nature of the landscape, subsystem isolation, and limited scope. SuperAIRE will address these challenges by enabling shared learning on common research challenges in different RE subsectors through promoting novel generic approaches complemented with refinements tailored to subsector's unique needs; forging a holistic view to facilitate system-wide AI applications; and fostering comprehensive solutions that go beyond single-task focuses to exploit the full potential of AI in enhancing the RE ecosystem. SuperAIRE will carry out diverse activities to engage with stakeholders, facilitate knowledge exchanges, catalyse community coherence, identify cross-sector opportunities, address skill gaps, support nurturing high-skill professionals with multidisciplinary expertise, and disseminate project outcomes. These activities include four key challenge workshops, bimonthly seminars, flexible funds, outreach activities, an international conference, etc. SuperAIRE will support early career researchers (ECRs) from both academia and industry via a dedicated ECR Forum, a mentoring scheme, secondment opportunities, and ECR grants. We will emphasise Equality, Diversity and Inclusion in all activities. Based on the current critical mass and emerging gaps and opportunities, we have also proposed six pre-defined research themes (RTs) to steer our Network+ activities, especially in guiding discussions, identifying challenges and opportunities, streamlining research coordination efforts, shaping a research landscape report, and developing a whitepaper. This includes RT1 Robust and trustworthy AI; RT2 Prediction and forecasting across scales; RT3 AI-powered digital twins; RT4 Intelligent control and management; RT5 Smart integration; and RT6 Intelligent robotics and autonomous systems in resource assessments, operations, and maintenance. Bolstered by strong support from project partners, we will consolidate core achievements and pursue the establishment of a new Supergen Hub in AI for RE at the end of SuperAIRE. Through these endeavours, we aim to enhance the efficiency, resilience, and affordability of RE, ultimately transforming the RE sector and addressing environmental challenges via AI.

The UK is leading the development and installation of offshore renewable energy technologies. With over 13GW of installed offshore wind capacity and another 3GW under construction, two operational and one awarded floating offshore demonstration projects as well as Contracts for Difference awards for four tidal energy projects, offshore renewable energy will provide the backbone of the Net Zero energy system, giving energy security, green growth and jobs in the UK. The revised UK targets that underpin the Energy Security Strategy seek to grow offshore wind capacity to 50 GW, with up to 5 GW floating offshore wind by 2030. Further acceleration is envisaged beyond 2030 with targets of around 150 GW anticipated for 2050. To achieve these levels of deployment, ORE developments need to move beyond current sites to more challenging locations in deeper water, further from shore, while the increasing pace of deployment introduces major challenges in consenting, manufacture and installation. These are ambitious targets that will require strategic innovation and research to achieve the necessary technology acceleration while ensuring environmental sustainability and societal acceptance. The role of the Supergen ORE Hub 2023 builds on the academic and scientific networks, traction with industry and policymakers and the reputation for research leadership established in the Supergen ORE Hub 2018. The new hub will utilise existing and planned research outcomes to accelerate the technology development, collaboration and industry uptake for commercial ORE developments. The Supergen ORE Hub strategy will focus on delivering impact and knowledge transfer, underpinned by excellent research, for the benefit of the wider sector, providing research and development for the economic and social benefit of the UK. Four mechanisms for leverage are envisaged to accelerate the ORE expansion: Streamlining ORE projects, by accelerating planning, consenting and build out timescales; upscaling the ORE workforce, increasing the scale and efficiency of ORE devices and system; enhanced competitiveness, maximising ORE local content and ORE economic viability in the energy portfolio; whilst ensuring sustainability, yielding positive environmental and social benefits from ORE. The research programme is built around five strategic workstreams, i) ORE expansion - policy and scenarios , ii) Data for ORE design and decision-making, iii) ORE modelling, iv) ORE design methods and v) Future ORE systems and concepts, which will be delivered through a combination of core research to tackle sector wide challenges in a holistic and synergistic manner, strategic projects to address emerging sector challenges and flexible funding to deliver targeted projects addressing focussed opportunities. Supergen Representative Systems will be established as a vehicle for academic and industry community engagement to provide comparative reference cases for assessing applicability of modelling tools and approaches, emerging technology and data processing techniques. The Supergen ORE Hub outputs, research findings and sector progress will be communicated through directed networking, engagement and dissemination activities for the range of academic, industry and policy and governmental stakeholders, as well as the wider public. Industry leverage will be achieved through new co-funding mechanisms, including industry-funded flexible funding calls, direct investment into research activities and the industry-funded secondment of researchers, with >53% industry plus >23% HEI leverage on the EPSRC investment at proposal stage. The Hub will continue and expand its role in developing and sustaining the pipeline of talent flowing into research and industry by integrating its ECR programme with Early Career Industrialists and by enhancing its programme of EDI activities to help deliver greater diversity within the sector and to promote ORE as a rewarding and accessible career for all.

This Centre for Doctoral training in Industrially Focused Mathematical Modelling will train the next generation of applied mathematicians to fill critical roles in industry and academia. Complex industrial problems can often be addressed, understood, and mitigated by applying modern quantitative methods. To effectively and efficiently apply these techniques requires talented mathematicians with well-practised problem-solving skills. They need to have a very strong grasp of the mathematical approaches that might need to be brought to bear, have a breadth of understanding of how to convert complex practical problems into relevant abstract mathematical forms, have knowledge and skills to solve the resulting mathematical problems efficiently and accurately, and have a wide experience of how to communicate and interact in a multidisciplinary environment. This CDT has been designed by academics in close collaboration with industrialists from many different sectors. Our 35 current CDT industrial partners cover the sectors of: consumer products (Sharp), defence (Selex, Thales), communications (BT, Vodafone), energy (Amec, BP, Camlin, Culham, DuPont, GE Energy, Infineum, Schlumberger x2, VerdErg), filtration (Pall Corp), finance (HSBC, Lloyds TSB), food and beverage (Nestle, Mondelez), healthcare (e-therapeutics, Lein Applied Diagnostics, Oxford Instruments, Siemens, Solitonik), manufacturing (Elkem, Saint Gobain), retail (dunnhumby), and software (Amazon, cd-adapco, IBM, NAG, NVIDIA), along with two consultancy companies (PA Consulting, Tessella) and we are in active discussion with other companies to grow our partner base. Our partners have five key roles: (i) they help guide and steer the centre by participating in an Industrial Engagement Committee, (ii) they deliver a substantial elements of the training and provide a broad exposure for the cohorts, (iii) they provide current challenges for our students to tackle for their doctoral research, iv) they give a very wide experience and perspective of possible applications and sectors thereby making the students highly flexible and extremely attractive to employers, and v) they provide significant funding for the CDT activities. Each cohort will learn how to apply appropriate mathematical techniques to a wide range of industrial problems in a highly interactive environment. In year one, the students will be trained in mathematical skills spanning continuum and discrete modelling, and scientific computing, closely integrated with practical applications and problem solving. The experience of addressing industrial problems and understanding their context will be further enhanced by periods where our partners will deliver a broad range of relevant material. Students will undertake two industrially focused mini-projects, one from an academic perspective and the other immersed in a partner organisation. Each student will then embark on their doctoral research project which will allow them to hone their skills and techniques while tackling a practical industrial challenge. The resulting doctoral students will be highly sought after; by industry for their flexible and quantitative abilities that will help them gain a competitive edge, and by universities to allow cutting-edge mathematical research to be motivated by practical problems and be readily exploitable.

Efficient and effective manufacturing supply networks (MSN) are essential to the functioning of the global economy. In line with the EPSRC call, this proposal is premised on the strong belief that appropriate mathematical theory and methods can provide fundamentally new understanding on the behaviour of MSNs and provide an effective investigative toolset for MSN analysis, design and management. In particular we argue that the power of network science can be harnessed to underpin new thinking in MSNs for resilience and robustness. The work will be strongly embedded in real MSNs in three domains - producer-driven inbound MSNs and outbound distribution channels for industrial companies; global MSNs for critical products used in high-valued manufacturing (e.g. titanium or composite pre-preg materials); and evolving MSNs for emerging UK industries such as renewable energy. The project will develop and apply existing and new mathematics specifically in the theory of complex adaptive networks, drawing on techniques from game theory, dynamical systems and Bayesian informatics. It will also learn from related modelling approaches in ecology, metabolism modelling and utility grids. This grant will represent the first attempt to develop an integrated mathematical modelling suite to support effective decision making in MSNs in the context of risk and uncertainty. The work will build on disparate recent developments in network science and complex adaptive dynamical systems, Bayesian statistics and operational research to develop new models and measures to better understand and analyse MSN behaviour and performance. Multiple perspectives and a multi-level view of risks and vulnerabilities in MSNs will be taken, including physical, financial, informational, relational, and governance perspectives at the strategic MSN design and policy levels, and risk mitigating strategies at both strategic and operational levels to support MSN management. This is an adventurous and challenging proposal due to the following reasons: (1) The PIs based in have various domains of expertise, from theory of complex networks and nonlinear dynamics, to applied statistics in domains such as reliability and risk assessment, and development and application of operational research and operations management methods to MSN management and control problems. However, our expertise is complementary and will add a substantial body of new knowledge and bring novelties to the theory of complex networks, network dynamics and Bayesian networks, but also, applications of these new models to real-world MSN problems will ultimately lead to better understanding of complex MSN behaviour and will improve MSN management and control in the presence of risks and uncertainties. (2) This proposal will bring together PIs and PDRAs from 4 universities. The management of the resources involved is a challenge on its own. However, we believe that a very carefully designed project management plan can lead this research collaboration to its success. Furthermore, if funded, this research project can potentially secure the continuation of the collaboration among the four universities. (3) The project will involve a wide array of industrial partners from manufacturing primes (e.g. in Aerospace and Defence) to manufacturing trade organisations and consultants, to representatives of a brand new industry (offshore renewable energy) for which the in-bound MSNn does not yet exist.

Air quality in most East African cities has declined dramatically over the last decades and it air pollution is now the leading environmental risk factor for human health. There is a critical lack of data to assess air quality in East Africa, and therefore to quantify its effect upon human health. Air quality networks in East Africa are still in their early days, with the long term and systematic measurement of air pollutants only available at less than a handful of sites. Large spatial and temporal gaps in data exist. From a historical perspective, very little is known of air pollution concentrations before 2010. The lack of historical data makes it extremely difficult to assess the deleterious effects of air pollution upon human health. It also poses challenges for assessing the efficacy of air quality interventions. Hence informed decisions about infrastructure, which take air quality into account are difficult to make. This proposal forms a new network to co-create strategy and protocols to bring together data that relate to air pollution in East African Urban areas. It targets the capitals of Ethiopia (Addis Ababa), Kenya (Nairobi) and Uganda (Kampala). New data science techniques will be developed to synthesize disparate data streams into spatially and temporally coherent outputs, which can be used to understand historic, contemporary and future air quality. The proposal will provide a road map to harness the power of new data analytics and big data technologies. To design this roadmap, three high intensity workshops and interspersed virtual meetings will be undertaken in Stage 1. Each workshop will tackle a key knowledge gap or development challenge: - Workshop 1: Parameterizing the data problem in East Africa for assessing the causes and effects of air pollution (Kampala) - Workshop 2: Big data approaches to improve East Africa air quality prediction (Addis Ababa) - Workshop 3: Creating greater capacity and capability in analytic air quality science (Nairobi) The Stage 1 research outcomes will enable the development of tailored mitigation strategies for improving air quality. The methodologies developed in the proposal will be translatable and scalable throughout urban East Africa. Hence, the proposal will help realise multiple sustainable development goals (SDGs), including SDG3: Good health and well-being, SDG11: Sustainable cities and communities, and SDG17: Partnerships for the goals. To ensure the project reaches its maximum potential, it includes an extensive array of research translation activities: workshops with academic and non-academic stakeholders; a professionally designed website, which will hold both academic and non-academic outputs including open source academic papers and presentations; briefing notes directed at a range of external stakeholders, including top down governance and bottom up grassroots organizations. Project partners from business, academia, governance and public engagement with science are involved and will attend the workshops. They are Uber, Amazon Web Services, PA Consulting, Kampala Capital City Authority, African Population Health Research Centre, Birmingham Open Media, GCRF Multi-Hazard Urban Disaster Risk Transitions Hub, and the Alan Turing Institute. They offer an additional £102,951 of in-kind contributions to the project. Their incorporation widens the available skillsets and will help deliver long-term impact in the East African region.