This 5 year project aims to address the challenges in understanding, creating and scaling up manufacturing processes for formulated products with particular reference to those in fast moving consumer goods (home/personal care and food products). The main objective is to develop a new modelling approach which will be combined with experimental measurements of the liquid properties to describe these complex fluids and enable a significant reduction in the conventional physical experimentation required to develop new formulations. The methodologies developed will provide a detailed understanding of the behaviour of these liquids under process conditions which will facilitate the decision making process and accelerate the introduction of new, innovative products to the market optimised to deliver benefits to consumers, e.g. shiny hair or mouthfeel for ice cream. The project brings together a multi-disciplinary team from the University of Manchester, the University of Cambridge and Unilever who are a global leader in the research, process design and manufacture of formulated products. The project is also supported by Process Systems Enterprise Ltd.. A major outcome of the project will be a demonstrator of the Industry 4.0 concept which will enable smart factories to be realised in the process sector and thus allow the UK to remain at the forefront of manufacturing in this field.

The proposed project deals with liquefaction of CO2 and its transportation by ship either for storage or enhanced oil recovery, and the impact of impurities on loading and unloading scenarios. There is little experience of handling liquid CO2 under shipping conditions. The levels, for which impurities (including water) need to be removed from CO2 so as to mitigate handling issues, and the associated impact on energy efficiency of compression and liquefaction, are not understood, especially for CO2 from fossil-fired power plants. The study aims to evaluate the propensity for leakages, freezing, blockages, and safety issues for liquid CO2 suitable for transportation by ship (near to the triple point). The project aims to modify the existing PACT facility at Cranfield to allow the multi operational facility to test CO2 in either dense phase or semi refrigerated fluid state. Two key technology gaps will be addressed: How to prepare an energy efficient triple point CO2 under controlled manner and what operational issues would encounter as a function of key impurities?

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.

At the 2015 Paris climate conference, 195 countries agreed that global greenhouse gases should peak as soon as possible and that countries should thereafter rapidly reduce their emissions. The process industries must therefore reduce their energy consumption and increase efficiency while maintaining consumer services. Next generation decision-making software at the interface of engineering, computer science, and mathematics is critical for these efficient systems of the future. Already, state-of-the-art computational packages are routine in the process industries; practically every major company uses simulation and optimisation to model production in different modes including: continuous, batch, and semi-continuous production systems. But more efficient industrial systems require simultaneously considering many tightly integrated subsystems which exponentially increase complexity and necessitate many temporal/spatial scales; the resulting decision making problems may not be solvable with current techniques. Increasing efficiency may also jeopardise safety: the process integration required for efficiency implies interchanging heat between processes and may damage safety precautions by transferring disturbances across a plant. During this fellowship, we propose to develop GALINI, new decision-making software constructing and deploying next generation process optimisation tools dealing with combinatorial complexity, disparate temporal/spatial scales, and safety considerations. The GALINI project proposes step-changes in optimisation algorithms that are immediately applicable to efficiency challenges in process systems engineering (PSE): safely operating batch reactors, retrofitting heat-exchanger networks, intermediate blending, and integrating planning and scheduling. We will freely release our software on open-source platform Pyomo and build an international user community. The primary GALINI research aim is to develop optimisation software that pushes the boundary of computational tractability for PSE energy efficiency applications. Effective optimisation software in the process industries answers: How can we best achieve a definite engineering objective? Given constraints such as an existing plant layout or a contractual obligation to produce specific products, the software supports novel engineering by quantitatively comparing the implications of different options and identifying the best decision. GALINI is particularly interested in design: How should we build new facilities or modify existing ones to achieve our design goals with maximum efficiency? The state-of-the-art in decision making for the process industries is represented by commercial modelling software such as AspenTech and gPROMS. Practically every major company in the process industries uses these software tools since the outputs of the simulation or optimisation can be implemented with minimal day-to-day operational disruption and savings can be realised with a payback time as short as 6-12 months. GALINI will develop deterministic global optimisation software for mixed-integer nonlinear programs, a type of optimisation problem highly relevant to energy efficiency and process systems engineering. Energy efficiency instances may exhibit the mathematical property of nonconvexity, i.e. have many locally optimal solutions; global optimisation mathematically guarantees the best process engineering solution. GALINI proposes transformational shifts in algorithms that creatively reimagine the core divide-and-conquer algorithm typically applied to this type of optimisation problem. Our approach is to freely release GALINI to users including those in the process industries, publicise the software, demonstrate its utility, and build a user community that will feed back into software development.

The complex, interconnected and fast-changing nature of today's society presents a growing challenge for decision-makers. Increased competition in the process industries (oil and gas, chemicals, personal care products, food, pharmaceuticals and agrochemicals) means that agility must be built into process design and operation. Furthermore, the need to ensure reliability across the supply chain, minimise resource use and environmental impact, and maximise energy efficiency combine to make investment and operational decisions especially difficult. Such multifaceted decision-making has long been aided by detailed mathematical models of physical and engineered processes, which enable digital twins and constitute a cornerstone of smart manufacturing technologies and the future Industry 4.0. But the full benefits afforded by these models have so far been hampered by the lack of tools for exploiting them beyond "what if?" scenario analysis. In particular, the uptake of optimisation-based decision-making has been hindered by the large-scale, nonlinear and uncertain nature of these problems that often leads to suboptimal or even unphysical solutions. In the ADOPT collaboration between the Sargent Centre for Process Systems Engineering (CPSE) and the JARA Center for Simulation and Data Science (JARA-CSD), we propose to address some of these shortcomings by developing improved methods for deterministic global optimisation, a class of optimisation methods that rely on complete search techniques and offer a rigorous conceptual framework to overcome the caveats of local optimisation. Our key research hypothesis is that the integration of deterministic global optimisation with surrogate (simplified) models and machine learning will enable transformational changes in our capability to tackle complex decision-making problems, leading to more tractable solutions with global optimality certificates and improved resilience to uncertainty. This nascent area brings about the following specific research challenges that we shall tackle within ADOPT: - identifying best-in-class theoretical / algorithmic global optimisation frameworks and surrogate modelling paradigms to empower surrogate-based optimisation; - handling uncertainty within the chain linking physical/simulated data to surrogate models and to optimisation results; and - developing bespoke deterministic global optimisation approaches for more challenging classes of problems beyond mixed-integer nonlinear programming. The ADOPT collaboration brings together two world-class teams of researchers in the field of deterministic global optimisation as well as team members who are specialists in handling uncertainty, in solving large-scale combinatorial problems, and in applying optimisation to real-world engineering problems. Furthermore, our assembled team partners with prominent optimisation software and process modelling companies in order to increase the accessibility of the research outputs and facilitate their dissemination. The ADOPT collaboration creates added-value through the combined strength of scientific expertise of the two centres, the breadth of the software infrastructure that can be brought together, the wealth of its human capital, the reach of its industrial relationships and the exceptional potential to establish a long-term partnership. It will lead to scientific advances that can be tested on practical problems quickly, ensuring maximum impact from the research.