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

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.