Toward smart carbon capture with machine learning
Name
1-s2.0-S2666386421000862-main.pdf
Description
Published version
Size
1.67 MB
Format
Adobe PDF
Checksum (MD5)
97224cca39b2a2bef55c50c56190510b
Author(s)
Rahimi, Mohammad
Moosavi, Seyed Mohamad
Smit, Berend
Hatton, T Alan
Date Issued
April 21, 2021
Journal
Cell Reports Physical Science
Publisher
Elsevier BV
Citation
Rahimi, Mohammad, Moosavi, Seyed Mohamad, Smit, Berend and Hatton, T Alan. 2021. "Toward smart carbon capture with machine learning." Cell Reports Physical Science, 2 (4).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.xcrp.2021.100396
