Modeling the interactions between CO 2 uptake and different parameters of activated carbon (AC) adsorbent synthesis in biomass types can be a way to create efficient adsorbents for CO 2 capture. In the present work, several AC syntheses experiments, from 35 publications, have been used for the development of operability simulations, based on an artificial neural network (ANN). Four ANN structures were developed by multilayer perceptron (MLP) and radial-based function (RBF) algorithms to predict the specific surface area (BET) of the adsorbents and their CO 2 adsorption capacity. The precursors, activators, pyrolysis temperatures, pour volumes, adsorption pressure, adsorption temperature, BET, and CO 2 adsorption capacity have been considered as input and output variables. The Bayesian Regularization backpropagation algorithm has been chosen for the two hidden layers from the MLP and compared with the RBF algorithms. The number of neurons in the MLP and RBF algorithms was 35 and 45 for BET prediction, and 130 and 240 for CO 2 adsorption capacity prediction, respectively, after an optimization process. MLP and RBF networks with high accuracy have the greatest MSE validation results (R 2 > 0.99). The ANN approach has been found to be a promising tool to accurately predict specific adsorbents of AC biomass types for CO 2 capture.

Green porous carbon was synthesized by self-activation methodology with facile one-step carbonization from a walnut-shell precursor for air separation. The adsorption process behavior was surveyed using isotherm, kinetic and thermodynamic models.

Oxygen and nitrogen enriched micro–meso porous carbon powders have been prepared from pectin and melamine as oxygen and nitrogen containing organic precursors, respectively.

Biomass-derived porous carbons have been considered one of the most effective adsorbents for CO2 capture, due to their porous structure and high specific surface area. In this study, we successfully synthesized porous carbon from celery biomass and examined the effect of external adsorption parameters including time, temperature, and pressure on CO2 uptake in experimental and molecular dynamics (MD) simulations. Furthermore, the influence of carbon’s surface chemistry (carboxyl and hydroxyl functionalities) and nitrogen type on CO2 capture were investigated utilizing MD simulations. The results showed that pyridinic nitrogen has a greater tendency to adsorb CO2 than graphitic. It was found that the simultaneous presence of these two types of nitrogen has a greater effect on the CO2 sorption than the individual presence of each in the structure. It was also revealed that the addition of carboxyl groups (O=C–OH) to the carbon matrix enhances CO2 capture by about 10%. Additionally, by increasing the simulation time and the size of the simulation box, the average absolute relative error for simulation results of optimal structure declined to 16%, which is an acceptable value and makes the simulation process reliable to predict adsorption capacity under various conditions.

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In this research, activated carbon (AC)-based absorbents modified with NiO and MgO were prepared by wet impregnation method for adsorption of carbon dioxide (CO 2 ). The effect of adding (Ni(NO 3 ) 2 6(H 2 O)) and (Mg(NO 3 ) 2 6(H 2 O)) in 1, 3, 5, and 7 wt% to AC was studied. Raw AC and modified AC were characterized by ultimate analysis, scanning electron microscopy, X-ray diffraction, and surface area. In addition, response surface methodology method was used to optimize the adsorption operation condition. The five-level central composite design was applied to design the experiments for three types of adsorbents (AC, AC/NiO-3, and AC/MgO-3) in the temperature and pressure ranges of 25-80 °C and 2-10 bar, respectively. The results indicated that the adsorption capacity of activated carbon was modified after NiO and MgO loading, especially at higher temperatures, and the optimal concentrations were obtained 3 wt% for both of them. For better evaluation of the adsorbents behavior, experimental data were investigated by isotherm, kinetic, and thermodynamic models. The optimum adsorption capacities were obtained 121.35, 105.17 mg/g for AC/NiO-3 and AC/MgO-3, respectively.

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