Toward smart carbon capture with machine learning

Abstract

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 thermodynamic 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. Machine learning (ML), which is rapidly emerging in a number of fields, is also having an effect on carbon-capture processes on many fronts, with applications ranging from predictions of thermodynamic properties to an accelerated discovery of materials. In this perspective, Rahimi et al. discuss how ML methods can pave the way toward smart carbon-capture processes.