Machine learning implemented exploration of the adsorption mechanism of carbon dioxide onto porous carbons

Sarvesh Namdeo; Vimal Chandra Srivastava; Paritosh Mohanty

doi:10.1016/j.jcis.2023.05.052

Machine learning implemented exploration of the adsorption mechanism of carbon dioxide onto porous carbons

J Colloid Interface Sci. 2023 Oct:647:174-187. doi: 10.1016/j.jcis.2023.05.052. Epub 2023 May 18.

Authors

Sarvesh Namdeo¹, Vimal Chandra Srivastava², Paritosh Mohanty³

Affiliations

¹ Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India. Electronic address: snamdeo@ch.iitr.ac.in.
² Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India. Electronic address: vimalcsr@yahoo.co.in.
³ Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India. Electronic address: pm@cy.iitr.ac.in.

PMID: 37247481
DOI: 10.1016/j.jcis.2023.05.052

Abstract

Adsorption of CO₂ on porous carbons has been identified as one of the promising methods for carbon capture, which is essential for meeting the sustainable developmental goal (SDG) with respect to climate action, i.e., SDG 13. This research implemented six supervised machine learning (ML) models (gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), light boost gradient machine (LBGM), random forest (RF), categorical boosting (Catboost), and adaptive boosting (Adaboost)) to understand and predict the CO₂ adsorption mechanism and adsorption uptake, respectively. The results recommended that the GBDT outperformed the remaining five ML models for CO₂ adsorption. However, XGB, LBGM, RF, and Catboost also represented the prediction in the acceptable range. The GBDT model indicated the accurate prediction of CO₂ uptake onto the porous carbons considering adsorbent properties and adsorption conditions as model input parameters. Next, two-factor partial dependence plots revealed a lucid explanation of how the combinations of two input features affect the model prediction. Furthermore, SHapley Additive exPlainations (SHAP), a novel explication approach based on ML models, were employed to understand and elucidate the CO₂ adsorption and model prediction. The SHAP explanations, implemented on the GBDT model, revealed the rigorous relationships among the input features and output variables based on the GBDT prediction. Additionally, SHAP provided clear-cut feature importance analysis and individual feature impact on the prediction. SHAP also explained two instances of CO₂ adsorption. Along with the data-driven insightful explanation of CO₂ adsorption onto porous carbons, this study also provides a promising method to predict the clear-cut performance of porous carbons for CO₂ adsorption without performing any experiments and open new avenues for researchers to implement this study in the field of adsorption because a lot of data is being generated.

Keywords: Adsorption; Machine learning; Porous carbons; SDG13; SHAP explanations.