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to develop intensified processes for CO2 conversion into sustainable synthetic fuels via cobalt-catalysed Fischer-Tropsch Synthesis (FTS), based on advanced catalysis and reactor engineering concepts
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: Optimize network design: Develop a robust, scalable thermodynamic–hydraulic framework to size and configure prosumer-based heating and cooling networks under conditions of price volatility, CO2 taxation
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commodity chemical, responsible for 1-2% of energy consumption and CO2 emissions worldwide. The PhD project will support the efforts for creating an innovative electrochemical membrane reactor
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Climate Act established a target of = 49% reduction in CO2 emissions by 2030 and 95% by 2050 (compared to 1990 emission levels). Realizing this transition will require continued expansion of energy from
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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pollution, accounting for 7% of national CO2 emissions, 4% of waste, and 13% of raw material use-figures that place it above the international average. Much of this environmental impact results from
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such as carbon capture and storage (CCS) and negative emissions play in effectively decarbonizing industry? This project aims to give an economic answer to these—and other—questions using a combination