The global hydrogen generation market is valued at $115.25 billion in 2017 and is projected to grow to $154.74 billion by 2022 [Global Outlook & Trends for Hydrogen, IEA, 2017]. We are witnessing significant market opportunities emerging for hydrogen technologies today. New and existing hydrogen technology developments and market activities are projected to intensify over the coming decade. Sustainable hydrogen solutions are a key pathway for decarbonising transport, heat and power generation sectors. Common challenges to sustainable hydrogen being adopted across these sectors are: - Cost reduction - Safety - Systems level and multisectoral innovations - Managing change Over the next decade innovative solutions are needed to tackle the above challenges, but it will be impossible without a dedicated mechanism to train doctoral Energy Innovation Leaders. These leaders should have a firm grasp of the technology from scientific fundamentals through to applied engineering and a solid understanding of the techno-economic barriers and an appreciation of the societal issues that will impact on the translation of disruptive technologies from research labs through to market. This goes beyond being multidisciplinary, but is a transdisciplinary training, reflecting the translation steps from understanding market driven needs, planning and conducting appropriate basic and applied research to products/solutions/system development through to successful market penetration. This is delivered by a cohort training approach through the cross fertilisation of ideas of a cohort with a diverse background, peer-demonstration of the value of research across a diverse range of stakeholder-led projects, thus facilitating a peer-to-peer transdisciplinary learning culture. The SusHy Consortium, led by Gavin Walker, continues a long running and highly successful collaboration in hydrogen research between the Universities of Nottingham, Loughborough, and Birmingham (UoN, LU, UoB) which started over a decade ago with the Midlands Energy Consortium. The Midlands Energy Graduate School spawned two successful CDTs (Hydrogen, Fuel Cells and their Applications and the current Fuel Cells and their Fuels). The current proposal for a CDT in Sustainable Hydrogen brings together the world leading expertise in hydrogen generation, purification, sensors/monitoring, and storage, along with whole systems issues (resilience engineering, business economic models and life cycle analysis) which exist across the three Universities. A gap in the consortium expertise is in the research field of hydrogen safety and we identified the internationally-renowned Hydrogen Safety Engineering and Research Centre (HySAFER) at Ulster University (UU) as the right partner to deliver on this key aspect. This is the first broad collaboration in the world seeking to investigate, train researchers and produce leaders in Sustainable Hydrogen. Stakeholder Partnerships. A key strength of this CDT is the active involvement of the Stakeholders in co-creation of the training programme which is reciprocated in the value with which the Stakeholders view of the CDT. This shared vision of a training partnership between the Universities and Stakeholders will lead to the smooth function of the CDT with not just a high-quality training programme, but a programme that is tailored to the sector needs for high-quality, industry-ready doctoral Energy Innovation Leaders. The valued CDT-stakeholder partnership will also be a significant appeal to candidates interested in energy-related PhDs and will be used to help market the CDT programme to a diverse talent pool.

The Made Smarter review identified that the UK is lagging behind in worker productivity and could benefit from the advent of new industrial digital tools (IDTs) such as novel intelligent technologies, connected devices, robotics and artificial Intelligence. It is estimated that IDTs could contribute an additional £630bn to the UK economy by 2035 and increase the manufacturing sector growth by between 1.5 and 3% per annum [1]. For example, there is a high demand for bespoke and personalised goods in high volume [2]. In order to meet this demand, manufacturing systems need to be highly flexible, adaptable and highly automated. Since most manufacturing SMEs make use of jobshops and contribute up to 15% of the UK economy [3], equipping them with robots that can learn a task rapidly and flexibly (similar to how a human can be rapidly trained to assemble new product lines) will enable SMEs to meet high order demands thereby improving UK PLC's export opportunities and UK's GDP. This proposal aims to investigate cognitive architectures that equips robots with the capability to rapidly learn new skills by passive observation of a human demonstrating a task to the robot and applying previously learnt skills to new task scenarios, thereby achieving task flexibility on the manufacturing floor. This opens up exciting possibilities. For one, it means that robots can be taught to do various tasks with no intensive programming required by a human. It also means that robots can be flexibly used to perform a wide variety of tasks thereby reducing the need for capital intensive, rigid and time-consuming manufacturing set ups. There is a gap in literature of applying digital mental models on robots for building in flexible and creative robots that can be flexibly and rapidly re-tasked for various tasks. Nevertheless, there is a growing realisation that creativity is needed in industrial robots of the future and that this could be achieved through providing them with mental models [4]. For the first time ever, this proposal investigates a cognitive architecture that embeds the human cognitive capabilities of mental simulation for creative problem solving on manufacturing robots and task structure mapping in a unified framework for the purposes of achieving rapid re-tasking (task flexibility) of industrial robots via passive human demonstrations. State of the art architectures (such as SOAR and ART-R) often make use of a prior task informed rigid procedural rules that make them less amenable for exploring rapid re-tasking on robots while techniques that use machine learning paradigms (e.g deep neural networks or reinforcement learning) that require lots of data and result in task specific applications. Furthermore, these techniques are yet to be successfully combined with the creation of digital mental models through envisioning and applied to varying tasks in manufacturing environments similar to those to be investigated in this proposal. In summary, the novelty of this proposal is in the application of robot envisioned digital mental models to support them in creativity and imagination of morphological informed solutions to problems encountered in manufacturing (and other sectors outside manufacturing) as well as to support the application of previously learnt skills to new similar tasks. This will lead to rapid re-tasking and task flexibility in robots. References: [1] J. Maier, "Made Smarter Review," 2017. [2] D. Brown, A. Swift, and E. Smart, "Data analytics and decision making," Inst. Ind. Res. Univ. Portsmouth, pp. 1-20, 2019, doi: 10.4324/9781315743011-9. [3] C. Rhodes, "Business Statistics," 2019. [4] J. B. Hamrick, "Analogues of mental simulation and imagination in deep learning," Current Opinion Behavioral Science, vol. 29, pp. 8-16, 2019.