Uncertainties are present in many energy-related process design (e.g., how should a process be configured?) and operational (e.g., what is the best production schedule for a day/week?) optimisation problems of current industrial interest. The efficiency of an energy-intensive hydrogen production plant can be greatly improved by optimising the steam-methane reformer, but design decisions regarding the reformer are subject to uncertain catalyst performance. Likewise, an electricity-intensive air separation unit can derive economic savings and reduce peak power demand by engaging in demand-response; however, deciding optimal production schedules relies on uncertain forecasts of electricity supply and product demand. Regrettably, state-of-the-art software is not suitable for decision-making under these uncertain conditions, severely limiting the benefits of industrial demand-side management (DSM) towards national energy efficiency. Here, DSM refers to measures of improving the energy system at the side of consumption, ranging from reducing overall demand by increasing process efficiencies to smarter consumption patterns through demand response operation. Demand response (DR) operation aims to increase the systemic integration of volatile renewable energy sources by matching consumption to the short-term and long-term (daily to seasonal) fluctuations in supply. Motivated by the above, this interdisciplinary project will introduce Algorithms for Industrial Demand-Side Management Under Uncertainty. The potential of curtailing carbon emissions through improving the efficiency of energy-intensive process industries is massive, with industrial entities comprising 17% of total energy consumption in the United Kingdom in 2017. DR operation in the electricity-intensive process industries further reduces carbon emissions by synchronising demand with renewable-based generation. Therefore, a complete DSM decision-making toolkit must consider uncertainty in both design and operational decisions of process systems. In modern environments, these tools must also be computationally scalable, synergise with the abundant available data, and accompany decisions with rationale. The proposed scientific advances have numerous immediate applications: optimising energy efficiency in manufacturing, balancing the power grid through DR, and mitigating negative effects of disturbances. The primary observation of the proposed research is that modern markets and environments dictate a deviation from the accepted paradigm of deterministic (i.e., no uncertainty is modeled), local (i.e., risks sub-optimal decision-making) optimisation. The process industries require a new generation of decision-making algorithms that can solve, and re-solve, large-scale optimisation problems to global optimality, often in an online or recurring fashion. The proposed research introduces DSM technologies that: (1) automatically decompose process models for global optimisation, (2) exploit historical operating data for planning and scheduling, and (3) produce explainable results for user-friendly re-optimisation. The fellowship will be held at the Department of Computing at Imperial College, which has an outstanding reputation and provides an ideal environment for the proposed software advances. Imperial is also the birthplace of the field of process systems engineering (PSE) and thus is a premier forum for applied PSE research. By providing freely available software tools, we will contribute to the forefront of PSE, as well as relevant related domains of optimisation theory, data science, and artificial intelligence. Finally, promoting the algorithmic advancements by releasing and contributing to open-source software will spur new academic and industrial applications in computational decision-making for energy efficiency.

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.

The electrode, and the electrolyte-electrode interface, plays a critical role in the performance of all cells. In Solid Oxide Fuel Cells (SOFCs) the microstructures of the porous composite anode and cathode are particularly critical as they determine the electrochemical, electrical, mechanical and transport properties of the electrode, and of current distribution to/from the electrode/electrolyte interface. Current state of the art SOFC electrodes rely on a largely empirical understanding to establish the electrode microstructure, and its influence on key performance characteristics, including long term durability. But recent work by the proposers has established a new suite of tools and techniques that offer the prospect of moving towards a design led approach to manufacture of improved electrodes, based on our ability to image, model, simulate and fabricate new electrode structures with controlled properties. This proposal seeks to develop and demonstrate this, further improving and validating our analysis and modelling tools, using these design optimum structures, fabricating these using three novel processing techniques established by the proposers, and then measuring device performance to feedback into the design process.

A 4-year multidisciplinary project aimed at minimising primary-energy use in UK industry is proposed, concerned with next-generation technological solutions, identifying the challenges, and assessing the opportunities and benefits (to different stakeholders) resulting from their optimal implementation. Around 20 companies from component manufacturers to industrial end-users have expressed an interest in supporting this project. With this industrial support, the team has the necessary access and is in a prime position to deliver real impact, culminating in the practical demonstration of these solutions. The proposed project is concerned with specific advancements to two selected energy-conversion technologies with integrated energy-storage capabilities, one for each of: 1) heat-to-power with organic Rankine cycle (ORC) devices; and 2) heat-to-cooling with absorption refrigeration (AR) devices. These technological solutions are capable of recovering and utilising thermal energy from a diverse range of sources in industrial applications. The heat input can come from highly efficient distributed combined heat & power (CHP) units, conventional or renewable sources (solar, geothermal, biomass/gas), or be wasted from industrial processes. With regards to the latter, at least 17% of all UK industrial energy-use is estimated as being wasted as heat, of which only 17% is considered economically recoverable with currently available technology. The successful implementation of these technologies would increase the potential for waste-heat utilisation by a factor of 3.5, from 17% with current technologies to close to 60%. The in-built, by design, capacity for low-cost thermal storage acts to buffer energy or temperature fluctuations inherent to most real heat sources, allowing smaller conversion devices (for the same average input) and more efficient operation of those devices closer to their design points for longer periods. This will greatly improve the economic proposition of implementing these conversion solutions by simultaneously reducing capital and maintenance costs, and improving performance. The technologies of interest are promising but are not economically viable currently in the vast majority of applications with >5-20 year paybacks at best. The project involves targeting and resolving pre-identified 'bottleneck' aspects of each technology that can enable step-improvements in maximising performance per unit capital cost. The goal is to enable the widespread uptake of these technologies and their optimal integration with existing energy systems and energy-efficiency strategies, leading to drastic increases performance while lowering costs, thus reducing payback to 3-5 years. It is intended that technological step-changes will be attained by unlocking the synergistic potential of optimised, application-tailored fluids for high efficiency and power, and of innovative components including advanced heat-exchanger configurations and architectures in order to increase thermal transport while simultaneously reducing component size and cost. Important system-level components are included in the project, whose objective is to assess the impact of incorporating these systems in targeted industrial settings, examine technoeconomic feasibility, and identify opportunities relating to optimal integration, control and operation to maximise in-use performance. A dynamic, interactive whole-energy-integration design and assessment platform will be developed to accelerate the implementation of the technological advances, feeding into specific case-studies and facilitating direct recommendations to industry. Only two international research teams are capable of developing the necessary tools that combine multiscale state-of-the-art molecular thermodynamic theories for fluids, detailed energy-conversion ORC and AR models, and incorporating these into whole-energy-system optimisation platforms. This is truly a world-leading development.

The development of new materials and new devices / products based upon these materials is absolutely critical to the economic development of our society. One critical aspect of the development of new materials is the ability to analyse the materials and thus determine their properties. Indeed at the very heart of the philosophy of the materials discipline is the relationship between the microstructure and the properties of the materials. The core idea is that through processing one can control the microstructure and thus the properties. Materials characterisation tells us how succesful we have been at changing the microstructure and so is essential in process development. It also tells us what has gone wrong when materials or devices based upon them fail, i.e. it is used in troubleshooting. There are a vast array of advanced materials characterisation techniques available these days and it is very challenging to know the best technique or combination of techniques to use to answer specific research problems. There is a need, therefore, to train research scientists who are expert in the use of certain techniques but also have a broader in-depth understanding of the plethora of techniques that potentially could be used. At the moment there is a skills gap in this area and we will plug that gap with this CDT in advanced characterisation of materials that brings together experts in advanced materials characterisation from two of the worlds top universities. The students will also spend some time (at least 12 weeks) in industry or at an overseas univeristy receiving context specific training. The unique vision brought by this research training programme, therefore, is that our students will have a knowledge of materials characterisation that goes beyond narrow expertise in one or two experimental techniques, or a general overview of many, and instead cuts to the heart of what it means to be a leading experimentalist; with an inherent understanding of the nature of a scientific problem, the fundamental principles and intellectual tools required to address the problem, the technical knowledge and craft to apply the most appropriate experimental technique to obtain the necessary information and the critical and analytical skill to extract the solution from the data. The vision will be realised by exploiting the unique experimental infrastructure provided by UCL and ICL. The first year will be an MRes structure with the entire cohort receiving laboratory based practical training in techniques ubiquitous to modern day materials characterisation such as vacuum technology, scanning probe microscopy, optical characterisation techniques and clean-room processing. Key analytical skills will be taught such as data handling, manipulation and interpretation, practiced on real data, exploiting facilities such as Imperials ToF-SIMS analysis suite and UCL chemistry's material modelling user interface. We will engage with industry to generate genuine problem-based characterisation case studies so that elements of the course will be founded on problem based learning. Visiting professors such as Mark Dowsett (Warwick University) and Hidde Brongersma(Calipso BV) will contribute to the training experience and some external courses will be used for specialist training, for example at ISIS. Traditional lectures will be limited in number with every sub-topic leading into an interactive problem class run by one of our extensive number of industry partners. In our CDT ACM the thrill of solving class problems together and of competing in team-based experimental challenges will produce a highly engaged, critically minded, close-knit team of students.