ICONIC aims to develop innovative physical and digital tools to achieve fundamental breakthroughs for the integrated control of wind farms, considering the whole physical system at farm, turbine, and component levels, in particular the complex aerodynamic interactions among turbines. ICONIC aims to increase farm-wide power production by 15-20% under optimal wind speeds and directions for typical wind farms suffering from wake effects, with a 3%-5% increase in annual energy production (AEP) considering all working conditions over the long term. It targets an LCOE reduction of at least 6% compared with the state-of-the-art control tools deployed in the current wind industry by improving farm-wide AEP and reducing operation & maintenance costs via leveraging the latest AI and digital technologies. Extensive validations for the integrated wind farm control solutions will be conducted via high-fidelity simulation models, experiments at a national-level wind tunnel, historical operational data at BP’s and C-Power’s wind farms, a unique collection of test rigs for critical turbine components at respective companies, and real-world wind farm field tests at C-Power. ICONIC’s integrated wind farm control system will contain (1) novel AI-based wind farm control system to unlock wind farms’ full potential; (2) novel data enhanced wind turbine controllers to fulfil farm-level commands while balancing power generation and load mitigation; (3) an integration with digital twins (DTs) as extra support to improve control and reduce costs, which contains a first-ever farm-level DT for wind farm flow systems replicating detailed physical flow fields and an innovative turbine-level DT with critical component models for loading and lifetime estimations; (4) extensions of the solutions to future 20MW turbines. ICONIC will establish new knowledge and industrial leadership in key digital, enabling and emerging technologies, and deliver next-generation tools for wind farm operation.

bp is committed to Backing Britain and we intend to invest up to £18 billion in the UK's energy system by 2030\. A key element of this will be in low carbon hubs where renewable energy, CCUS and hydrogen congregate to put UK industrial hubs at the forefront of technological development. Through Net Zero Teesside Power (NZT), a first-of-a-kind fully integrated gas-fired power generation and carbon capture project, bp is expected to provide flexible low carbon electricity sufficient for around 1.3 million homes - progressing the deployment of carbon capture technology in line with the UK Government's ten point plan. The UK's Department for Business, Energy and Industry Strategy (BEIS) has also selected bp's H2Teesside hydrogen project as one of its shortlisted East Coast Cluster (ECC) projects. This is a world leading initiative to decarbonize the industrial heartlands of the Humber and Teesside. Through APACE Net Zero, we will explore the use of advanced technology to monitor, report and validate the emissions from industrial and consumer activity in the Tees Valley region. The aim is to develop a highly differentiated approach to identify the sources and concentration of air emissions including CO2, NOX , SOX and particulates. This advancement will facilitate decarbonisation initiatives and also help compliance with Clean Air Act, Environment Act 2021, Environmental Permitting regulation and other relevant directives.

bp is aiming to be a very different company by 2030, and our ambition is to be a net zero company by 2050 or sooner and to help the world get to net zero. A key component of becoming an integrated energy company surrounds low carbon electricity and energy, and within that, creating a distinct position in hydrogen, including aiming for a 10% market share in core markets. The **HYDRI** project, led by bp, aims to develop stand-off gas sensing devices critical to the safe roll-out of hydrogen as a widely used energy source in domestic, industrial, and transportation sectors. It harnesses the UK's world-leading expertise in single-photon detector arrays and quantum-sensor technology products. The HYDRI consortium comprises internationally recognised UK organisations at the forefront of the innovative and high technology sectors they serve, who are extremely well placed to deliver the state-of-the-art modules required for these devices. The consortium is led by a globally recognised end-user of the technology who will steer the performance of the project and carry out extensive testing in a range of high-value application scenarios. Finally, the project benefits from the expertise of the UK's leading academic and research technology organisation, who are performing critical system modelling, design, and integration activities throughout this exciting project.

Thousands of Oil & Gas industry structures in the sea are approaching the end of their lives. At this time, they typically need to be removed and the environment returned to a safe state. This process is known as decommissioning. As many of these sites are old (typically 20+ years) and originally were drilled before the current environmental regulations existed, there has often been some contamination of the seabed around these sites. To ensure that no harmful effects will occur, decommissioning operations need to be supported by an environmental assessment and subsequent monitoring. Monitoring may be required over many years after decommissioning, especially if some structures are left in place. Monitoring surveys in the offshore environment are expensive and time-consuming, requiring ships and many specialist seagoing personnel. This requirement, although vital, will have a considerable cost for industry and the public. Ocean robots, which use computer systems to carry out survey missions by themselves, are regularly used in detailed scientific assessments of the environment. As they collect very high-quality data quickly, such robots have recently been adopted for some tasks by industry but these still require an expensive support ship as they are not capable of long-range missions. Recent technological developments have cut the cost and expanded the range of these robots to thousands of kilometres, making it possible for long-range assessments of multiple sites to be undertaken with a robot launched from the shore. This would have many advantages, improving the quality and quantity of environmental information while cutting the costly requirement for a survey ship and crew. We will carry out the first fully autonomous environmental assessment of multiple decommissioning sites. The Autosub long-range ocean robot submarine ("Boaty McBoatface") will be launched from the shore in Shetland, visit and carry out an environmental assessment at three decommissioning sites in the northern North Sea, before returning around 10 days later with the detailed survey information onboard. The robot will take photographs of the seabed, and these will be automatically stitched together to make a map of the seafloor, structures present, and the animals that live there. Established sensor systems will measure a range of properties of the water, including the presence of oil and gas. As well as the decommissioned sites, the robot will visit a special marine protected area where we know there are natural leaks of gas, to check the robot can reliably detect a leak if it did occur. On return to shore, the project will examine all the data obtained and compare it to that gathered using standard survey ship methods. We will test if the same environmental trends can be identified from both datasets to determine if the automated approach would be a suitable replacement for standard survey ship operations. The project will also produce a fully documented case study, which includes detailed information on the costs and benefits, practical information on deployments and approaches to reduce the risks and improve the efficiency of operations. This will be used by industry, scientists and government regulators, to demonstrate the techniques and will provide the necessary information to potential users to aid in their adoption. The overall goal of the project is to improve the environmental protection of the North Sea at a reduced cost and to demonstrate how this leading UK robotic technology could be used worldwide.

Advanced materials lie at the heart of a huge number of key modern technologies, from aerospace and automotive industries, to semiconductors through to surgical implants. The transmission electron microscope (TEM) is a key enabling technology for advanced material research because it offers two important pieces of atomic information: firstly the location of atoms can be determined from studies of elastic scattering of electrons by the sample, and secondly the chemical composition of atomic sites within the materials structure can be recovered from spectroscopic studies of the inelastic transfer of energy to the sample (either from direct energy loss or by the detection of characteristic X-rays). These two pieces of information underpin a huge research area exploring the relationship between materials microscopic structure and the macroscopic properties it exhibits. With the drive towards nanotechnologies and quantum devices the ability to discover the most precise understanding of individual atoms is an essential capability for facilities supporting research of advanced materials. The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution. To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges: i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction. ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few. iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features. Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.