Beatrice Ceccanti
From PINNs to DeepONets: Neural Network-based Approaches for Direct Air Capture Optimization.
Rel. Daniele Marchisio, Mattia Galanti, Martin Van Sint Annaland. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Chimica E Dei Processi Sostenibili, 2025
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Abstract
The awareness of the risks of climate change laid the foundation for the Paris Agreement, a legally binding treaty adopted at the 2015 UN Climate Change Conference (COP21) in Paris. At the European level, the Green Deal establishes clear time targets for GHG emissions and defines carbon capture, storage, and utilization (CCSU) technologies as priority areas. Furthermore, future projections of global emissions show that the utilization of negative emission technologies (NETs) is crucial for achieving Green Deal targets. Among carbon capture and storage technologies, Direct Air Capture plays an active role as an emerging Negative Emission Technology due to its ability to extract CO2 directly from the atmosphere, independently of the source.
DAC technologies are peculiar in their low land requirements compared to other CCS approaches, such as BECCS and AR
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