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 < 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 (% Vmic), 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.

Automated summary

The issue of CO2 emission has led to the promotion of carbon capture and storage (CCS), particularly bio-route CCS. Machine learning modeling was used to study the physicochemical features of N-doped biomass-based activated carbon (BAC) for CO2 capture. The radial basis function neural network (RBF-NN) was able to estimate the efficiency of microstructural and N-functionality groups under different pressure conditions. Multiple features such as specific surface area, micropore volume, and nitrogen content were found to be important for CO2 capture.