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

ABSTRACT: 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) base

SUBMITTER: Fathalian F 

PROVIDER: S-EPMC9747901 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature