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.

Designing a model to connect CO2 adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO2 adsorption application. In this work, the information of several GO-based solid sorbents were extracted from 17 articles aimed to develop a machine learning based model for CO2 adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO2 uptake capacity was defined as model response, alsoseven different models, including support vector machine, gradient boosting, random forest, artificial neural network (ANN) based on multilayer perceptron (MLP) and radial basis function (RBF), Extra trees regressor and extreme gradient boosting, were employed to estimate the CO2 adsorption capacity. The best performance was obtained for ANN based on MLP method (R2 > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β1 = 0.9, β2 = 0.999, epochs = 1971, and batch size = 32. To investigate CO2 uptake dependency on mentioned effective parameters, three dimensional diagrams were reported based on MLP network, also the MLP network characteristics including weight and bias matrices were reported for further application of CO2 adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO2 removal efficiency as the target based on adsorbent properties to pick more efficient adsorbents without increasing processing time. Current work employed statistical analysis and machine learning to support the logical design of porous GO for CO2 separation, aiding in screening adsorbents for cleaner manufacturing.