Key Takeaways & Executive Findings
- •• Ten machine learning algorithms were compared for predicting heavy metal fractions in tailings, with Extra Trees achieving the highest accuracy (R² = 0.946 on validation). • Electronegativity emerged as the most influential feature affecting morphological fractions, highlighting its role in metal mobility. • An ensemble of the top three models (GBDT, Extra Trees, CatBoost) further improved predictive performance, demonstrating the value of model stacking. • The proposed framework offers a rapid, cost-effective alternative to traditional sequential extraction experiments, facilitating environmental risk assessment and resource recovery.
Abstract
Driven by rapid technological advancements and economic growth, mineral extraction and metal refining have increased dramatically, generating huge volumes of tailings and mine waste (TMWs). Investigating the morphological fractions of heavy metals and metalloids (HMMs) in TMWs is key to evaluating their leaching potential into the environment; however, traditional experiments are time-consuming and labor-intensive. In this study, 10 machine learning (ML) algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs. A dataset comprising 2376 data points was used, with mineral composition, elemental properties, and total concentration used as inputs and concentration of morphological fraction used as output. After grid search optimization, the extra tree model performed the best, achieving coefficient of determination (R2) of 0.946 and 0.942 on the validation and test sets, respectively. Electronegativity was found to have the greatest impact on the morphological fraction. The models’ performance was enhanced by applying an ensemble method to the top three optimal ML models, including gradient boosting decision tree, extra trees and categorical boosting. Overall, the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs. This approach can minimize detection time, aid in the safe management and recovery of TMWs.
1. Introduction
The boom in development linked to the industrial revolution has led to rapid development of the global mining and metallurgical industries [1]. According to the United Nations Environment Programme (UNEP), global demands for minerals and natural resources, fueled by sustainable development efforts, are projected to triple by 2050 [2]. The inevitable increase in human exploitation of mineral resources has also led to the creation of more tailings and mine waste (TMWs) [3, 4]. Tailings represent the waste residues produced after the extraction of valuable components or metals from minerals and ores through mining operations [5]. According to KARACAN et al [6], the worldwide volume of tailings exceeds 44.5×10^9 m^3; however, the lack of a systematic waste treatment strategy has resulted in most TMWs being disposed of in tailings ponds or mine landfills [7]. The sheer size of tailings ponds and their abundance of hazardous constituents pose serious threats to local environments, especially in terms of soil, groundwater, and fragile ecosystems [1]. Therefore, the safe disposal or recycling of tailings remains an environmental issue of global importance.
The accumulation of mine tailings and waste can lead to the release of toxic heavy metals (THMs) and metalloids into the environment as a result of geochemical conditions [5, 8]. Once released, THMs represent a major environmental issue due to their high toxicity, wide sources, persistence, and non-degradability [9]. Therefore, it is essential to assess the release of THMs from tailings and their potential environmental impacts to ensure the safe disposal and recycling of tailings [10]. In TMWs, THMs occur under different fractions which directly determine how easily the THMs can be released into the environment [11]. Therefore, accurately detecting the morphological fractions of THMs in TMWs is essential for risk assessment and recycling [12].
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FENG Yu-xin, HU Tao, ZHOU Na-na, ZHOU Min, BARKHORDARI Mohammad Sadegh, LI Ke-chao, QI Chong-chong (2025). Machine learning model comparison and ensemble for predicting different morphological fractions of heavy metal elements in tailings and mine waste. Journal of Central South University. https://doi.org/10.1007/s11771-025-6075-3
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Frequently Asked Questions
What is the main objective of this study?
The study aims to compare and ensemble multiple machine learning models to rapidly predict the concentrations of different morphological fractions of heavy metals and metalloids in tailings and mine waste, reducing the need for time-consuming experimental methods.
Which machine learning model performed best in the study?
The Extra Trees model achieved the highest performance with R² values of 0.946 and 0.942 on validation and test sets, respectively, after hyperparameter optimization.
What was the most influential feature in predicting morphological fractions?
Electronegativity was found to have the greatest impact on the morphological fractions of heavy metals, indicating its importance in determining metal mobility and bioavailability.
How did the ensemble method improve model performance?
By combining the top three models (Gradient Boosting Decision Tree, Extra Trees, and Categorical Boosting), the ensemble method enhanced overall predictive accuracy and robustness compared to individual models.
What are the practical applications of this research?
The proposed framework can significantly reduce detection time and costs, aiding in the safe management, risk assessment, and recovery of valuable metals from tailings and mine waste.
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