Details

Autor(en) / Beteiligte

• Guo, Lisheng
• Xu, Xin
• Niu, Cencen
• Wang, Qing
• Park, Junboum
• Zhou, Lu
• Lei, Haomin
• Wang, Xinhai
• Yuan, Xiaoqing

Titel

Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite

Ist Teil von

• The Science of the total environment, 2024-05, Vol.926, p.171986-171986, Article 171986

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation. [Display omitted] •Six machine learning models were used to predict heavy metal adsorption capacity.•XGB model demonstrated the best predictive performance and generalization capacity.•Dosage and initial concentration are vital for heavy metal adsorption on bentonite.•A web-based GUI software was developed to enable anyone to access the XGB model.