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Machine learning (ML) is emerging as a powerful approach that has recently shown potential to affect various frontiers of carbon capture, a key interim technology to assist in the mitigation of climate change. In this perspective, we reveal how ML implementations have improved this process in many aspects, for both absorption-and adsorption-based approaches, ranging from the molecular to process level. We discuss the role of ML in predicting the thermody namic properties of absorbents and in improving the absorption process. For adsorption processes, we discuss the promises of ML techniques for exploring many options to find the most cost-effective process scheme, which involves choosing a solid adsorbent and designing a process configuration We also highlight the advantages of ML and the associated risks, elaborate on the importance of the features needed to train ML models, and identify promising future opportunities for ML in carbon capture processes.
Jan Van Herle, Jürg Alexander Schiffmann, Victoria Xu Hong He, Michele Gaffuri
François Maréchal, Luc Girardin, Daniel Alexander Florez Orrego, Ivan Daniel Kantor, Shivom Sharma, Meire Ellen Gorete Ribeiro Domingos, Rafael Amorim Leandro De Castro Amoedo, Julia Granacher, Yi Zhao