Machine learning models for occurrence form prediction of heavy metals in tailings

ABSTRACT

Modern mining and metal ore smelting produce vast tailings, increasing heavy metal pollution. The study of heavy metal occurrence forms is a promising way to remediate contaminated tailings while minimizing environmental damage. However, laboratory measurements of heavy metal occurrence forms are complex and time-consuming, so a fast and accurate identification method is urgently needed. This study used gradient boosting regression tree (GBRT) approaches to predict heavy metal occurrence forms in tailings, with tailings mineralogy information and heavy metal properties as input variables and the percentages of seven occurrence forms as output variables. The optimum GBRT model achieved excellent performance, with R values greater than 0.92 recorded on the testing set for all seven occurrence forms. The feature importance analysis showed that electronegativity was the most critical variable across all occurrence forms, with an average feature importance of 0.442, followed closely by atomic number, which had an average feature importance of 0.211. Overall, this study proposes a reliable and efficient GBRT prediction model for heavy metal occurrence forms, providing new insights into the effects of tailings mineralogy on heavy metal occurrence forms. This approach can be applied to contamination analysis and the safe and efficient use of heavy metals.

KEYWORDS:

• Sequential extraction
• tailing
• occurrence forms
• gradient boosting regression tree
• prediction

Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities of Central South University (No. 2023ZZTS0520), National Natural Science Foundation of China (No. 52004330) and National Key Research and Development Program for Young Scientists (No. 2021YFC2900400). This work was supported in part by the High Performance Computing Center of Central South University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the China Postdoctoral Science Foundation [No. 2023M732382], Central South University [No. 2023ZZTS0520]; National Natural Science Foundation of China [No. 52004330]; National Key Research and Development Program for Young Scientists [No. 2021YFC2900400].