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Dr Solomon F. BrownI am a Professor of Process and Energy Systems in the Department of Chemical and Biological Engineering at The University of Sheffield.
I'm the Director of the EPSRC's CDT in Energy Storage and its Applications and lead the Brown Group at Sheffield with ongoing projects funded by the EPSRC, BEIS, DASA, RAEng, the EC and Industry. My research focusses on mathematical modelling, process analysis and optimisation with a particular focus on clean energy processes, energy storage and energy systems. Key areas include:
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Jude Ejeh joined us as part of the Energy Open Piazza – Power Forward Challenge project, he works on developing optimisation models for electrical energy systems. This involves and extends to models for optimal scheduling of electrical energy storage devices for efficient integration of renewable energy sources – solar and wind, energy arbitrage, balancing mechanism services and other energy services for behind-the-meter and front-of-meter applications. It further covers the use of static electrical energy storage devices as well as the optimal scheduling of electric vehicle charging/discharging for vehicle-to-home (V2H) and vehicle-to-grid (V2G) applications. He also has experience in developing techno-economic optimisation models for process systems, with emphasis on multi-floor process plant layout configurations; in research relating to obtaining fuels from plastic wastes, as an intern in a Petroleum refining company and a Nuclear research laboratory. His current research interests lie in operations research (OR) applications to energy systems and supply chains.
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Peter Bugryniec's research focuses on developing a better understanding of the safety of Li-ion batteries. Specifically, my work investigates the hazard of thermal runaway with the aim of determining the governing processes and influencing factors that affect thermal runaway severity. This involves employing experimental methods to study the thermal runaway process under different environmental conditions and battery states. It further involves computational techniques for the development of an advanced abuse model of Lithium-ion phosphate cells.
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Mathew Wilkes is a Research Fellow in Clean Energy at the University of Sheffield. He uses process models developed in gPROMS, ASPEN, and Fluent, to simulate and analyse and a wide range of emerging energy technologies. He initially joined the Brown Group as an Engineering Doctorate student in the EPSRC CDT in Carbon Capture and Storage and Cleaner Fossil Energy. His EngD looked at techno-economically comparing post-combustion capture (PCC) technologies for CO2 capture on small quick-response fossil power generators, focusing on existing technologies and evaluating their performance under transient operation. He has produced peer reviewed papers on gas turbine operation, flexible CO2 capture, and linking CO2 sources to transportation options.
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Diarmid Roberts: The goal of my project is to perform a techno-economic analysis of Redox Flow Batteries in grid-connected applications. The work has two major components:
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Robert Milton is a mathematician, with a diverse background in finance, polyglot software development and applied mathematics. His interest is in creating novel mathematical and computational methods, which he has contributed to a variety of fields including McCabe-Thiele iteration and hydrological flow duration curves. A novel combination of techno-economic, process and supply-chain modelling was a major software project. The centrepiece of his research is building the rom-comma python package, combining original and existing machine learning techniques in a novel way. This tool uses Gaussian Processes to perform Global Sensitivity Analysis and locate an Active Subspace, dramatically reducing the problem dimension. It is already being applied to carbon capture, thermal runaway in batteries, wet granulation, and syntheses from Metal-Organic Frameworks to personalised medicines to mRNA vaccines. His short term goal is to integrate these techniques into a new class of device called an Agile Soft Sensor.
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Tim Hutty's project seeks to assess the efficacy and cost of reversible solid oxide fuel cells (ReSOFC) as a tool for implementing a small scale, low-carbon distributed energy generation/storage system when interacting with a larger grid. To this end a microgrid simulation will be constructed using AnyLogic software.
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Thomas Cowley is a PhD student researching district heat network modelling, design and operation, focusing on utilising low-grade renewable heat sources. His current research employs agent-based modelling techniques to inform policymakers on how the UK can decarbonise the heating sector. He started within the group during his MEng degree, which looked at generating value from wind turbine and electrolyser systems using multi-integer linear programming to schedule the dispatch of hydrogen to storage and electricity via a grid connection. He then received the Hossein Farmy scholarship that now funds his PhD.
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Min Tao is a Research Associate in Soft Sensors and Digital Twins for RNA Vaccine Manufacture funded via the Welcome Leap R3 Program. His project focuses on developing advanced digital tools for enhancing the development and operation of RNA vaccine and therapeutics production processes. Previously, He got his PhD degree at The University of Manchester.
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Oludayo Asuni is a Ph.D. Student working on the techno-economic analysis of supercritical water gasification of biomass integrated with physical absorption-based CCS using modelling and simulation for the production of Hydrogen. Her past research projects include CCS integration with bio-power plants and bio-ethanol production with CHP integration using lignocellulosic biomass.
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Joseph Hammond is a PhD student researching hydrogen network modelling, design and integration with small industrial clusters. His research uses a whole system approach encompassing local hydrogen generation, the design of infrastructure, and its end-use, primarily in foundational industries. His involvement with the group started during his MEng degree, where he helped develop an agent-based tool for district heat networks.
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Veysel Yildiz's research involves the application of numerical modelling, control theory, and optimization methods to contribute to scientific and technical advances in hydropower plant design. He developed a MATLAB toolbox (called HYPER) of run-of-river (RoR) power plants, which produce hydroelectricity using river flow instead of water stored in a reservoir. He has developed the HYPER-MORDM approach (HYPER - Many-objective robust decision making ) to help optimize the design and operation of power plants. The results of his research significantly enhance our ability to determine which design is most robust and reliable for given site conditions under deep uncertainty.
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Alumni Members
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Rachel Lee’s research is aimed at combining technical analysis of vehicle-to-grid (V2G) systems with a human behaviour model in order to move from current ‘technically feasible’ to more realistic “practically achievable” volumes of V2G response. The work employs an agent-based simulation using the ‘consumat’ behaviour model to first establish the growth in electric vehicle deployment and then the uptake of V2G contracts. Data from the National Travel Survey is used to model journeys and locations of vehicles so that the availability of V2G connection points at different locations can be considered within the analysis.
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Emma Chandler's PhD focussed on applying the McCabe Thiele method to liquid-liquid biological separations. Her current interests are on purification techniques suitable for biological separations.
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Aaron Yeardley's research interests include applying data analytics and machine learning to help engineering issues. During his PhD, Aaron used Robert Milton's novel ROMCOMMA Python Package to apply Gaussian Processes as a surrogate model to help other researchers with sensitivity analysis and forecasting capabilities to build accurate computational models. Currently, Aaron uses his solid mathematical skills as a Senior Carbon Reduction Engineer, working with many businesses to calculate their baseline emissions. Then, data analytics is used to present the carbon hotspots and promote reduction opportunities to the client. In the future, Aaron would love to combine his work in applying machine learning models to help the environment. In particular, the future could apply digital twin optimisation to create a circular economy and energy synergies between partnering manufacturing facilities.
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Flora Biggins' research looks at how energy storage can be utilised to generate value in different integration scenarios. Case studies have explored locational effects of co-locating battery storage with solar, optimising bidding strategies in competitive energy markets and community storage partially accessed by a value-maximising aggregator. Ongoing work includes developing a model for a wind-based hydrogen electrolysis and applying a real-options approach to energy storage projects.
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