Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents

Designing a model to connect CO 2 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 CO 2 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 CO 2 adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO 2 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 CO 2 adsorption capacity. The best performance was obtained for ANN based on MLP method ( R 2 > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β 1 = 0.9, β 2 = 0.999, epochs = 1971, and batch size = 32. To investigate CO 2 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 CO 2 adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO 2 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 CO 2 separation, aiding in screening adsorbents for cleaner manufacturing.