Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches

The CO2 emission issue has triggered the promotion of carbon capture and storage (CCS), particularly bio-route CCS as a sustainable procedure to capture CO2 using biomass-based activated carbon (BAC). The well-known multi-nitrogen functional groups and microstructure features of N-doped BAC adsorbents can synergistically promote CO2 physisorption. Here, machine learning (ML) modeling was applied to the various physicochemical features of N-doped BAC as a challenge to figure out the unrevealed mechanism of CO2 capture. A radial basis function neural network (RBF-NN) was employed to estimate the in operando efficiency of microstructural and N-functionality groups at six conditions of pressures ranging from 0.15 to 1 bar at room and cryogenic temperatures. A diverse training algorithm was applied, in which trainbr illustrated the lowest mean absolute percent error (MAPE) of