With increasing opposition to building new transmission lines, transfer of bulk energy is going to be a major challenge in the UK and in many parts of Europe. Examples include the transmission link from the north of the UK to the load centres in the south and the corridor importing hydro power from north of Norway to the load centres near Oslo. It is therefore, absolutely critical that the existing power transmission assets are fully utilised by loading them much closer to their capacity. To ensure secure operation under such heavy loading, the dynamic performance of the system needs to be improved through appropriate control of voltage and power flow using the flexible ac transmission systems (FACTS) devices. It is often difficult to obtain accurate information about all the components (e.g. loads) of a power system which poses fundamental limitation on conventional model based control design. In the above context, this project aims at designing and validating a self-tuning control scheme for FACTS devices that rely solely on the measured signals and thereby, obviate the need for accurate system information. Such controllers are designed independent of the system operating condition and therefore, need no retuning with changes in system configuration. Use of more than one feedback signals from strategic locations, available though wide-area measurement systems (WAMS), can potentially improve the effectiveness of the FACTS controller. Hence, the control design needs to be formulated in a multi-variable framework. The performance of the controller would be validated in real-time through hardware-in-loop (HIL) simulation employing a test bench, emulating the behaviour of large power systems, and a commercial control simulator. The proposed project essentially integrates FACTS with WAMS and could potentially provide the developers and user of both these technologies a new edge.

This project focuses on a radical change to chemical manufacturing with a view to effective step changes in environmental sustainability and in circularity of materials. We shall focus on the emerging electrochemical sector which is expected to grow strongly and within which there are many opportunities for the deployment of digital technologies to underpin system design and operation. In response to this call, we have united a cross-disciplinary team of leading researchers from three UK universities (Imperial College, Loughborough, and Heriot-Watt) to create a digital circular electrochemical economy. The chemical sector is a "hard to decarbonise" sector. Its high embedded carbon comes from two aspects: (1) the intensive energy use; and (2) the use of fossil feedstock. Therefore, the decarbonisation requires the substitution of both two with renewable energy (electrifying the chemical processes) and feedstock (e.g., H2O, CO2). We foresee a closer integration of the electrical energy system with the industrial chemistry system, with the former providing reducing energy formerly available in fossil fuels and which enables the processing of highly oxidised but abundant feedstocks. The intermittency of renewable electricity supply and the economic benefits of flexible processing and closer integration between these two sectors will give rise to opportunities for new digital technologies. These will enable improved design and operation of emerging electrochemical processing technologies and provide new pathways to chemical building blocks (e.g. olefins) and fuels. The integration of the sectors also provides opportunities for cost savings in the electrical system through improved flexibility and demand management. We propose three work packages (WP) to look at the challenges at different levels, and finally integrate as a whole solution: - WP1 Digital twins of key electrochemical operation units and processes. - WP2 Digitalisation of the value chain encompassing the integration between the chemical and electrical systems - WP3 Policy, Society and Finance, including business models to capture value generation opportunities from industrial integration

The importance of international collaborations in research is recognised both by individual researchers and by institutions and government, with studies showing that the average impact of publications resulting from these collaborations is significantly higher than that of papers with national co-authorship. This collaborative project between leading academic groups in the UK and India addresses the purification operations used to manufacture biopharmaceuticals e.g. antibodies and hormones such as insulin. They are supported in this activity by four industrial partners selected to provide support to the analytical and manufacturing aspects (being leading companies in their respective areas) as well as to provide a route to transfer the findings of the research to practice. Many of the latest drugs are based upon proteins rather than traditional small molecules (e.g. antibiotics). These protein drugs are produced for the treatment of diseases such as cancer. Antibodies such as Herceptin dominate this market. The research collaboration described here is focused on the study of the performance of the core purification method used for the manufacture of biopharmaceuticals - chromatography. Specifically we seek understand the mechanisms which determine the manufacturing lifetime of this operation and can lead to changes in performance. This issue presents a major hurdle to manufacturers. They must establish a robust purification process with acceptable costs for production before seeking approval for such medicines from the regulatory agencies. Clearly problems leading to delays can lengthen the times before medicines can made available to patients. This can affect both manufacturers of new products and those seeking to compete at reduced costs and widen the availability of this class of medicines (products often termed biosimilars). In comparison to other areas of manufacturing, bioprocessing is unusual in several respects. Typical product quantities are small (~250 kg/year), but are manufactured to extremely high purity and quality specifications (impurities < 0.001%). The variability typically seen in these processes has led to extremely regulated manufacturing, whose dictum is that "the process is the product". No significant change can be made to a licensed manufacturing process without detailed and time-consuming review by the international regulatory authorities. Developing and validating a bioprocess for manufacture takes ~10 years at a cost of £800M. Development is often empirical, with little use of modelling compared to other manufacturing sectors. These unusual features emphasise the need for a more fundamental understanding of the bioprocess. This research programme is structured towards building mechanistic understanding of the events that lead to changes in chromatographic performance in the manufacturing setting. There is evidence for several mechanisms the first stage is to structure these into a series of proposed mechanisms. Following consultation and study of historical data from our industrial partners we will embark upon experimental studies. Here detailed analytical measurements are required to identify specific critical species that are associated with the root cause of the mechanism. The project is to be led by UCL in London and IIT in Delhi in collaboration with IIT Bombay and the University of Kent. These academic groups are supported by industrial partners; ABB, Dr Reddy's Labs, GE Healthcare, Genzyme, PerkinElmer and Regeneron.

We are increasingly dependent on complex "smart" systems: cities, houses, vehicles, electricity grids and a myriad of connected 'things' gathering information and performing automated decision-making with or without a human in the loop. This is in part possible because of technological advances in sensing, actuation, computer hardware, networking and communication, which enable the harnessing, processing and analysis of vast volumes of data. Major advances in Automatic Control Engineering have provided the underpinning theory, methodology and practice needed to design and implement highly complex control and decision-making systems. Automatic control engineering continues to play a vital role in realising the government's long-term industrial strategy of raising productivity and earning power within the UK. Specifically, automatic control is a key enabling technology for all four major societal challenge themes identified in the 2017 UK Industrial Strategy: AI and Data, Clean Growth, Future Mobility and Aging Society and the specific challenge areas within each theme. Automatic control not only dramatically improves the productivity, efficiency, reliability and safety of a wide range of processes across all sectors, but also provides fundamental theory, methodologies and tools to further the understanding and enable discovery in other disciplines such as biology, medicine and social sciences. Whilst the UK led the First Industrial Revolution through the adoption of new technologies, including automation and control, today it lags behind its international competitors. This is evidenced in part by the slow productivity growth over the past decade, which is in sharp contrast to other economic indicators. It is argued that if the UK does not make a concerted effort to transition towards automation, it will miss a pivotal opportunity for growth, estimated to be worth more than £200 billion to the UK economy by 2030. For the UK to become a global leader in intelligent automation and leapfrog international competitors, it is vital that it consolidates its research leadership in automatic control engineering. The UK has a strong control engineering community of well over 1000 active researchers, and engineering practitioners spanning all career stages, which are represented at an international level by the UK Automatic Control Council (UKACC), the United Kingdom's National Member Organisation (NMO) of the International Federation of Automatic Control (IFAC), acting as an effective link between the UK and the international control communities. At the time of dramatic advances in automation, AI, sensing and computation technologies, in order to engage effectively with the UK Grand Challenge research agenda, avoid fragmentation of effort and to ensure control engineers are engaged from the outset with end-users or initiatives, there is a need for the UK control community to connect effectively with other academic and industry stakeholders, to develop a common research vision and strategy and to start addressing these challenges through ambitious pilot studies, paving the way for full-scale, high-impact grant proposals, novel groundbreaking research and knowledge transfer projects. The Automatic Control Engineering Network aims to drive forward the UK's research and international leadership in next-generation automation and control, by bringing together and connecting the country's expertise in automation, the internet-of-things, cybersecurity, machine learning and robotics, with industry stakeholders and the wider research communities working towards addressing the same pressing societal challenges. Through the creation of a Virtual Centre of Excellence in Automation and Control, the Network will ensure that the coordination of research efforts, industry engagement, training activities and resource sharing needed to address Grand Challenges, will continue beyond the end of the funding period.

Sensors are everywhere, facilitating real-time decision making and actuation, and informing policy choices. But extracting information from sensor data is far from straightforward: sensors are noisy, prone to decalibrate, and may be misplaced, moved, compromised, and generally degraded over time. We understand very little about the issues of programming in the face of pervasive uncertainty, yet sensor-driven systems essentially present the designer with uncertainty that cannot be engineered away. Moreover uncertainty is a multi-level phenomenon in which errors in deployment can propagate through to incorrectly-positioned readings and then to poor decisions; system layering breaks down when exposed to uncertainty. How can we be assured a sensor system does what we intend, in a range of dynamic environments, and how can we make a system ``smarter'' ? Currently we cannot answer these questions because we are missing a science of sensor system software. We will develop the missing science that will allow us to engineer for the uncertainty inherent in real-world systems. We will deliver new principles and techniques for the development and deployment of verifiable, reliable, autonomous sensor systems that operate in uncertain, multiple and multi-scale environments. The science will be driven and validated by end-user and experimental applications.