Process planning and scheduling problems are becoming increasingly complex due to the expanding production and customer base around the globe. A decision maker is continuously faced with the challenge to optimise the production plans and reduce costs under uncertainty. The uncertainty can be attributed to factors including volatile customer demands, variations in the process performance, fluctuations in socio-economics around the locations of the production plants, etc. Another complicating issue is the time-scale at which the decisions have to be taken and implemented. Not being able to effectively take these issues into account can lead to increased costs, customer dissatisfaction, loss of competitive edge and eventually shutting down of the manufacturing bases. This project aims to develop planning and scheduling tools for optimal decision-making under uncertainty while taking into account the multiple time-scales. Each process planning and scheduling problem is unique and hence one modelling and model solution tool cannot address the peculiarities of each problem. A framework where uncertainties are classified into specific categories is the key to providing cutting-edge optimal solutions. So, a problem will have a number of uncertainties which will be classified based upon our proposed framework and then for each classification the appropriate solution methodology will be invoked. A hybrid uncertainty modelling and optimisation tool that exploits the synergies of the solution techniques for various classes of uncertainty will also be developed. The novel planning and scheduling tools developed in this project will be tested on real-life case studies from process industries from a wide variety of sectors including energy systems, agrochemicals, pharmaceuticals, consumer goods, oil & gas, and industrial gases. Optimal planning and scheduling solutions based upon personalised uncertainty will be obtained.

Development of geological models of the sub-surface relies on the interpretation of largely remotely sensed data. We propose a program of knowledge exchange that shares existing information and trials new methods for determining the impact of human biases, anchoring and confidence, on the interpretation of data used to build geological models. From this knowledge exchange and creation we will create and promote optimal workflows for interpretation that minimize risk in the oil and gas industry from interpretational uncertainty. The geological exploration and production of hydrocarbons and the storage of CO2 in geological reservoirs requires a 3D picture to be built of the sub-surface. This picture is made up of remotely sensed information like seismic reflection data with poor resolution, and 1D point sources such as well bores which sample a relatively small amount of the sub-surface volume of interest. Work on improving interpretation of these datasets has mainly focused on technological improvements to refine the imaging and processing of the remotely sensed data to better illuminate the sub-surface architecture. But even with improved techniques interpretations of the data, and the subsequent models created are uncertain. This uncertainty equates to exploration and production risk. The risk results from the lack of constraint from the data to create a 'certain' predictive model, and is amplified by known biases that are applied during interpretation of limited datasets. This knowledge exchange proposal aims to: quantify the effect of known biases on interpretation of seismic reflection datasets and to build a workflow that minimizes biases in interpretation that industry can deploy. We will work with industry, and on industry datasets, to exchange knowledge of industry workflows and the effects of human bias between the academics and partner companies involved, as well as with MSc and PhD students. Building on this exchange we will create new knowledge through a series of experiments to investigate and quantify the influence of anchoring on interpretation. By building into the experiment release of additional data we will test how individual's deal with new information that either confirms, or is contrary, to their original interpretation; and for how long individuals remain anchored to an original prediction in the face of contradictory evidence. We will compare cohorts of individuals with staged access to different data against those with all the data at the outset. Throughout the process we will gauge an individual's perception of confidence in their interpretation through an expert elicitation process. Using this new knowledge we will quantify the impact of human biases on interpretational uncertainty and determine an optimal workflow for seismic interpretation. From our combined existing and co-generated knowledge we will create a series of products to promote this workflow, and the associated knowledge, as well as the NERC science on which they are based. These will include an online resource of digital video footage deployed through the existing Virtual Seismic Atlas, accessed by 8,000-10,000 users monthly, and a series of training packages for industry and early career scientists undertaking PhDs as part of the NERC Oil and Gas Centre for Doctoral Training.

The proposed EPSRC CDT in the Science and Applications of Graphene and Related Nanomaterials will respond to the UK need to train specialists with the skills to manipulate new strictly two-dimensional (2D) materials, in particular graphene, and work effectively across the necessary interdisciplinary boundaries. Graphene has been dubbed a miracle material due to the unique combination of superior electronic, mechanical, optical, chemical and biocompatible properties suitable for a large number of realistic applications. The potential of other 2D materials (e.g. boron nitride, transition metal and gallium dichalcogenides) has become clear more recently and already led to developing 'materials on demand'. The proposed CDT will build on the world-leading research in graphene and other 2D nanomaterials at the Universities of Manchester (UoM) and Lancaster (LU). In the last few years this research has undergone huge expansion from fundamental physics into chemistry, materials science, characterization, engineering, and life sciences. The importance of developing graphene-based technology has been recognized by recent large-scale investments from UK and European governments, including the establishment of the National Graphene Institute (NGI) at UoM and the award of 'Graphene Flagship' funding by the European Commission within the framework of the Future and Emerging Technologies (Euro1 billion over the next 10 years), aiming to support UK and European industries.Tailored training of young researchers in these areas has now become urgent as numerous companies and spin-offs specializing in electronics, energy storage, composites, sensors, displays, packaging and separation techniques have joined the race and are investing heavily in development of graphene-based technologies. Given these developments, it is of national importance that we establish a CDT that will train the next generation of scientists and engineers who will able to realise the huge potential of graphene and related 2D materials, driving innovation in the UK, Europe and beyond. The CDT will work with industrial partners to translate the results of academic research into real-world applications in the framework of the NGI and support the highly successful research base at UoM and LU. The new CDT will build directly on the structures and training framework developed for the highly successful North-West Nanoscience DTC (NOWNANO). The central achievement of NOWNANO has been creating a wide ranging interdisciplinary PhD programme, educating a new type of specialist capable of thinking and working across traditional discipline boundaries. The close involvement of the medical/life sciences with the physical sciences was another prominent and successful feature of NOWNANO and one we will continue in the new CDT. In addition to interdisciplinarity, an important feature of the new CDT will be the engagement with a broad network of users in industry and society, nationally and internationally. The students will start their 4-year PhD with a rigorous, bespoke 6-month programme of taught and assessed courses covering a broad range of nanoscience and nanotechnology, extending beyond graphene to other nanomaterials and their applications. This will be followed by challenging, interdisciplinary research projects and a programme of CDT-wide events (annual conferences, regular seminars, training in transferable skills, commercialization training, outreach activities). International experience will be provided by visiting academics and secondments to overseas partners. Training in knowledge transfer will be a prominent feature of the proposed programme, including a bespoke course 'Innovation and Commercialisation of Research' to which our many industrial partners will contribute, and industrial experience in the form of 3 to 6 months secondments that each CDT student will undertake in the course of their PhD.

Robots will revolutionise the world's economy and society over the next twenty years, working for us, beside us and interacting with us. The UK urgently needs graduates with the technical skills and industry awareness to create an innovation pipeline from academic research to global markets. Key application areas include manufacturing, assistive and medical robots, offshore energy, environmental monitoring, search and rescue, defence, and support for the aging population. The robotics and autonomous systems area has been highlighted by the UK Government in 2013 as one the 8 Great Technologies that underpin the UK's Industrial Strategy for jobs and growth. The essential challenge can be characterised as how to obtain successful INTERACTIONS. Robots must interact physically with environments, requiring compliant manipulation, active sensing, world modelling and planning. Robots must interact with each other, making collaborative decisions between multiple, decentralised, heterogeneous robotic systems to achieve complex tasks. Robots must interact with people in smart spaces, taking into account human perception mechanisms, shared control, affective computing and natural multi-modal interfaces.Robots must introspect for condition monitoring, prognostics and health management, and long term persistent autonomy including validation and verification. Finally, success in all these interactions depend on engineering enablers, including architectural system design, novel embodiment, micro and nano-sensors, and embedded multi-core computing. The Edinburgh alliance in Robotics and Autonomous Systems (EDU-RAS) provides an ideal environment for a Centre for Doctoral Training (CDT) to meet these needs. Heriot Watt University and the University of Edinburgh combine internationally leading science with an outstanding track record of exploitation, and world class infrastructure enhanced by a recent £7.2M EPSRC plus industry capital equipment award (ROBOTARIUM). A critical mass of experienced supervisors cover the underpinning disciplines crucial to autonomous interaction, including robot learning, field robotics, anthropomorphic & bio-inspired designs, human robot interaction, embedded control and sensing systems, multi-agent decision making and planning, and multimodal interaction. The CDT will enable student-centred collaboration across topic boundaries, seeking new research synergies as well as developing and fielding complete robotic or autonomous systems. A CDT will create cohort of students able to support each other in making novel connections between problems and methods; with sufficient shared understanding to communicate easily, but able to draw on each other's different, developing, areas of cutting-edge expertise. The CDT will draw on a well-established program in postgraduate training to create an innovative four year PhD, with taught courses on the underpinning theory and state of the art and research training closely linked to career relevant skills in creativity, ethics and innovation. The proposed centre will have a strong participative industrial presence; thirty two user partners have committed to £9M (£2.4M direct, £6.6M in kind) support; and to involvement including Membership of External Advisory Board to direct and govern the program, scoping particular projects around specific interests, co-funding of PhD studentships, access to equipment and software, co-supervision of students, student placements, contribution to MSc taught programs, support for student robot competition entries including prize money, and industry lead training on business skills. Our vision for the Centre is as a major international force that can make a generational leap in the training of innovation-ready postgraduates who are experienced in deployment of robotic and autonomous systems in the real world.