South Korean and Singaporean researchers use AI to boost CO2 absorption in BWDPCs

In South Korea, biomass waste can be used to produce porous carbons capable of sequestering CO₂ gas emitted from large point sources (e.g., power plants, cement industries). However, there are no general guidelines on how such high-quality porous carbons should be synthesized or their optimal operational conditions. In a recent study, scientists employed machine learning-based method to determine which core factors should be prioritized in biomass waste-derived porous carbons to achieve the best CO₂ adsorption performance, paving the way to a circular economy. 

In a recent study published in Environmental Science and Technology, a collaborative research team from Korea University and the National University of Singapore employed a machine learning-based approach that may guide the development of future porous carbon synthesis strategies. The scientists noted that there are three core factors influencing the CO2 adsorption properties in biomass waste-derived porous carbons (BWDPCs): the elemental composition of the porous solid, its textural properties, and the adsorption parameters at which it operates, such as temperature and pressure. However, how these core factors should be prioritized when developing BWDPCs has remained unclear, until now.