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 <3.5%. RBF-NN
estimates the CO2 capture with an R
2 range of 0.97–0.99 of BACs as solid adsorbents. Also,
the generalization assessment of RBF-NN observed errors, tolerating
0.5–6% of MAPE in 50–80% of total data sets. An alternative
survey sensitivity analysis discloses the importance of multiple features
such as specific surface area (SSA), micropore volume (%V
mic), average pore diameter (AVD), and nitrogen content
(N%), oxidized-N, and graphitic-N as nitrogen functional groups. A
genetic algorithm (GA) optimized the physiochemical properties of
N-doped ACs. It proposed the optimal CO2 capture with a
value of 9.2 mmol g–1 at 1 bar and 273 K. The GA
coupled with density functional theory (DFT) to optimize the geometries
of exemplified BACs and adsorption energies with CO2 molecules.