The 'DecomRegHub' will provide a safe, collaborative environment where industry can engage with regulators and together explore the technical and regulatory requirements of decommissioning and share/manage the associated risks. It will facilitate early engagement between industry, key stakeholders and regulators to explore collaboratively the technical, environmental and safety requirements of decommissioning, as well as identify opportunities to develop and test new techniques, products and regulatory tools that will help ensure the success of the global decommissioning market. 'DecomRegHub' will also provide a customer-focused digital hub bringing together data, advice, guidance, information, best practice, and case studies across the entire regulatory landscape. This collaborative approach will enable knowledge sharing and access to robust evidence, drawn from multiple sources of information that, in turn, will inform policy and regulatory development, operational assessments and decisions. The digital hub will be designed with users to ensure it contains the right information in the right format and is structured to make it easy for users to find and use what they are looking for as easily as possible. The ''DecomRegHub'' is made up of UK regulators and will be supported by the Offshore Petroleum Regulator for Environment and Decommissioning (OPRED), the Health and Safety Executive (HSE), the Environment Agency (EA), the Scottish Environment Protection Agency (SEPA) and Zero Waste Scotland (ZWS). It will: • Provide a safe, collaborative environment that supports industry in the development and testing of innovative new techniques, products and services in support of decommissioning. • Bring together operating companies and multiple regulators (from the oil and gas industry and the waste supply chain). • Understand (holistically) the environmental and safety regulatory requirements and identify opportunities to manage the associated risks together. • Address cross-cutting areas, share best practices and create innovative solutions. • Drive the potential to reduce, re-use and recycle materials, moving towards a circular economy. • Develop knowledge and experience that will grow the industry. • Position the UK as a global leader in late-life oil and gas asset management and decommissioning.

Our proposal will develop and utilise smart sensors, test new infrastructure and approaches for data cleaning, as well as developing predictive analytics and a visualisation platform, to improve the next generation of environmental regulations for water resources. Our tools will allow businesses (eg the whisky and agricultural sectors) to individually assess and control their environmental interactions and ultimately enable regulators to remove the need for traditional environmental inspection and monitoring. Partners in the multi-disciplinary proposal are the Scottish Environment Protection Agency (SEPA) and the Innovation Centre for Sensor and Imaging Systems (CENSIS). Our project will scope out existing and new technology for sensing water resources in remote environments, and then in a demonstrator project, explore the practical implementation of a network of sensors across a catchment integrating data from the national river flow archive, the SEPA managed network of gauging stations, and rainfall information. The results will allow us to assess the potential of this technology to disrupt traditional approaches to environmental regulation by providing a framework for enhanced and superior information gathering while removing the extensive cost and regulatory burden associated with field officers conducting inspections and sampling. A key aspect of this proposal is the promotion and deployment of sensors and communication and analytical methods to extend a previous small scale sensor pilot into a prototype digital predictive and visualisation framework testing the communications infrastructure and integration of data streams to enhance the ability of the UK to better manage water resources (quality and availability) in the context of remote, rural environments. This links into existing networks including the national river flow archive and the SEPA supported network of river gauging stations. In this larger demonstrator project, further sensors will be deployed providing additional spatial coverage of water level sensors, while adding additional types of sensor (rainfall and soil moisture), as well as scoping using a satellite based communications solution. This study will evaluate the potential of reliable and easily deployable sensor communication infrastructure based on the low power wide area network LoRaWAN standard monitoring rural environmental areas. As well as data transmission and communication challenges we will also be attempting to address off-grid powering challenges by making use of low power devices and active duty cycle management as well as renewable energy sources (e.g. solar/wind) in a low cost sustainable format. We will use new infrastructure extending the range of environmental variables to be measured, and test different data communication technologies including satellite (IoT), daisy chaining LoRaWAN and using battery operated LoRaWAN and LoRaWAN hybrid repeater nodes. These are very leading edge and we will be working with the industry leader Semtech in not only new lower power silicon (Q319) but a roll-out of a new meshing standard (TBC). The Hybrid repeater nodes will be custom and bespoke to this project. Our proposal could lead ultimately to many new remote networks that are independent of any infrastructure requirements.

Plastic waste is one of the most serious environmental challenges across the world. Currently, around 381 million tonnes of plastic waste are generated every year in the worldwide. However, the current recycling rates are thought to be 14%-18% at the global level. A key issue causing the low recycling rate is the uncertainty of the quality of recycled plastics. It is contaminated with a wide range of non-plastic and non-recycling materials. The current method for inspection of baled materials is core sampling. However, this method is slow to determine results and not accurate if samples extracted are not representative. Therefore, an automatic and non-destructive measurement technology is highly desirable for accurate identification and quantification of materiel type and composition in waste plastic bales and further assessment of the quality of bales. This project proposes a new measurement methodology for automatic quality assessment of waste plastic bales through hybrid sensing and data driven modelling. The project will start with design and construction of a hybrid sensing unit including a hyperspectral scanner and a 3D capacitive sensor. Meanwhile, considerations about the optimal design of the capacitive sensor with multiple electrodes will be made through finite element modelling. Identification and quantification algorithms based on machine learning and 3D reconstruction techniques will be developed to visualise material distribution and identify material compositions. Quality assessment algorithms based on expert knowledge and artificial intelligence will be developed to classify the plastic waste bale under test into a quality category. At the end of the project, the effectiveness of the proposed measurement methodology will be evaluated through laboratory tests and demonstration trials. The proposed methodology will provide a new way for inspection of baled materials from both surface and interior of the bale. Meanwhile, this measurement methodology makes the assessment automatic, which will significantly improve the efficiency of recycling processes. In addition, the established assessment criteria will ensure a clearer and more accurate definition of the quality of materials, which will result in more transparent pricing and greater clarity in global trade of waste plastics.

To be really useful, robots need to interact with objects in the world. The current inability of robots to grasp diverse objects with efficiency and reliability severely limits their range of application. Agriculture, mining and environmental clean-up arejust three examples where - unlike a factory - the items to be handled could have a huge variety of shapes and appearances, need to be identified amongst clutter, and need to be grasped firmly for transport while avoiding damage. Secure grasp of unknown objects amongst clutter remains an unsolved problem for robotics, despite improvements in 3Dsensing and reconstruction, in manipulator sophistication and the recent use of large-scale machine learning. This project proposes a new approach inspired by the high competence exhibited by ants when performing the closely equivalent task of collecting and manipulating diverse food items. Ants have relatvely simple, robot-like 'grippers' (their mouth-parts, called 'mandibles'), limited sensing (mostly tactile, using their antennae) and tiny brains. Yet they are able to pick up and carry a wide diversity of food items, from seeds to other insect prey, which can vary enormously in shape, size, rigidity and manouverability. They can quickly choose between multiple items and find an effective position to make their grasp, readjusting if necessary. Replicating even part of this competence on robots would be a significant advance. Grasping thus makes an ideal target for applying biorobotic methods that my group has previously used with substantial success to understand and mimic insect navigation behaviours on robots. How does an ant pick up an object? The first part of this project will be to set up the methods required to observe and analyse in detail the behaviour of ants interacting with objects. At the same time we will start to build both simulated and real robot systems that allow us to imitate the actions of an ant as it positions its body, head and mouth to make a grasp; using an omnidirectional robot base with an arm and gripper. We will also examine and imitate the sensory systems usedby the ant to determine the position, shape and size of the object before making a grasp. What happens in the ant's brain when it picks up an object? The second part will explore what algorithms insect brains need to compute to be able to make efficient and effective grasping decisions. Grasping is a task that contains in miniature many key issues in robot intelligence. It involves tight coupling of physical, perceptual and control systems. It involves a hierarchy of control decisions (whether to grasp, how to position the body and actuators, precise contact, dealing with uncertainty, detecting failure). It requires fusion of sensory information and transformation into the action state space, and involves prediction, planning and adaptation. We aim tounderstand how insects solve these problems as a route to efficient and effective solutions for robotics. Can a robot perform as well as an ant? The final part will test the systems we have developed in real world tasks. The first task will be to perform an object clearing task, which will also allow benchmarking of the developed system against existing research. The second task will be based ona pressing problem in environmental clean-up: detection and removal of small plastic items from amongst shoreline rocksand gravel. This novel area of research promises significant pay-off from translating biological understanding into technical advance because it addresses an important unsolved challenge for which the ant is an ideal animal model.

European and national legislation is increasingly being imposed on river management practice in the UK. Successful implementation requires detailed understanding of the interaction between flow and sediment transfer within river systems. Environmental organisations are calling for research aimed at accurately predicting the instant at which sediment motion begins and improving our understanding of the mechanics of sediment transport. This is because predictive sediment transport models are essential tools for engineers and scientists whose responsibility it is to plan and manage river regulation, restoration and re-alignment. However, existing models do not perform well. Improving the accuracy of these models is of immediate importance in predicting the sensitivity of sediment transport to changing flow patterns resulting from climate change and catchment modification. To date, it has been widely assumed that the stability of sediment comprising a river bed is only affected by high flows capable of sediment transport. Yet, there is clear evidence that the period of low flow between two flood events is actually a very important control on the point at which sediment is mobilised during a subsequent high flow event. Whilst, sediment transport occurs at lower shear stresses following short inter-flood periods, it is delayed until a higher shear stress when floods are separated by a longer inter-flood period. Accepting, quantifying and understanding the influence of antecedent low flows (termed the stress history ) on river bed stability are crucial if sediment transport predictions are to be improved. As such, detailed, systematic investigations aim to quantify the effect that stress history has on the stability of graded sediment beds, determine the mechanisms responsible and develop a practical method for integrating a stress history variable into sediment transport relationships. Whilst this new area of experimental research will significantly benefit the active international research community at a fundamental level, the motivation for this research is firmly rooted in its application to improving sediment transport models imperative to civil engineers, councils, consultants, water companies, reservoir managers, fisheries and the environment agencies directly involved in implementing the EC Water Framework Directive in areas such as river regulation, river restoration and flood risk.