Postcombustion CO 2 capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO 2 capture processes are considered too energetically expensive for widespread deployment. Vacuum swing adsorption (VSA) is a low-energy CCS that has the potential for industrial implementation if the right sorbents can be found. Metal−organic framework (MOF) materials are often promoted as sorbents for low-energy CCS by highlighting select adsorption properties without a clear understanding of how they perform in real-world VSA processes. In this work, atomistic simulations have been fully integrated with a detailed VSA simulator, validated at the pilot scale, to screen 1632 experimentally characterized MOFs. A total of 482 materials were found to meet the 95% CO 2 purity and 90% CO 2 recovery targets (95/90-PRTs) 365 of which have parasitic energies below that of solvent-based capture (∼290 kWh e / MT CO 2 ) with a low value of 217 kWh e /MT CO 2 . Machine learning models were developed using common adsorption metrics to predict a material's ability to meet the 95/90-PRT with an overall prediction accuracy of 91%. It was found that accurate parasitic energy and productivity estimates of a VSA process require full process simulations.

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The transient, cyclic nature and flexibility in process design make the optimization of pressure swing adsorption (PSA) computationally intensive. Two hybrid approaches incorporating machine learning methods into optimization routines are described. The first optimization approach uses artificial neural networks as surrogate models for function evaluations. The surrogates are constructed in the course of the initial optimization and utilized for function evaluations in subsequent optimization. In the second optimization approach, important design variables are identified to reduce the high-dimensional search space to a lower dimension based on partial least squares regression. The accuracy, robustness, and reliability of these approaches are demonstrated by considering a complex eight-step PSA process for precombustion CO2 capture as a case study. The machine learning-based optimization offers ∼10× reduction in computational efforts while achieving the same performance as that of the detailed models.

A review of the state-of-the-art applications of machine learning for CO2 capture, transport, storage, and utilisation.