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Maria M. Papathanasiou
Imperial College London
This research focuses on capturing carbon dioxide from industrial emissions for a sustainable energy system, aiming to improve the flexibility and effectiveness of CO2 capture processes. Traditional methods use pressure-vacuum swing adsorption (PVSA), but they lack flexibility and are constrained to operate around a single optimal point. The study introduces a machine learning-aided design space identification framework to quantify process flexibility and design flexible and controllable PVSA processes. It significantly reduces the number of simulations needed to find good operating points, while maintaining cost efficiency. This framework identifies that cost optimal operation is highly non-flexible and highlights the need for new design approaches to design more efficient and controllable PVSA processes.