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.