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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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potential large-scale climate repercussions. Even more so since the AMOC brings CO2 from the surface to the deep ocean during deepwater formation (physical pump), and variations in the AMOC strength will
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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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steel industry. A world without steel is unimaginable, yet we need to produce this steel sustainably. The Groeien met Groen Staal programme (GGS) aims at making the Dutch steel sector CO2 -neutral by 2050
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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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initiatives in real-life settings and how they stimulate capacity building, joint learning and fundamental change of practices within these settings. The novel concept of learning infrastructures captures flows
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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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, different designs of AI systems should be assessed during the design phase. Data flow diagrams used at NOLAI capture the processing, storage and transmission of data: key elements in assessing
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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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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