Sustainable mineral processing risk analysis based initial settling rates prediction using enhanced machine learning models
The primary contamination concern of mineral processing tailings (MPT) is the leaching of hazardous substances into the environment. The literature indicates that MPT requires effective flocculation and polymer-assisted dewatering to ensure its disposal does not cause environmental damage. In this research, a theoretical modeling framework was adopted, based on the development of a hybrid machine learning (ML) model for predicting flocculation-dewatering efficiency, aiming to reduce the cost of laboratory tests. The proposed ML model is based on an efficient Gaussian process regression (GPR) model that uses a feature impact strategy (FIS) and a kernel matrix dynamically updated using error noise, named enhanced GPR (EGPR). Additionally, the kernel ridge model and SHAP (SHapley Additive exPlanations) method are coupled for feature selection (FS), identifying the most important features among 17 input variables (features). The target variable in this research is the initial settling rate (ISR), which is predicted to utilize the EGPR model. The statistical analysis revealed that the EGPR model outperforms the Deep random vector functional link (DRVFL), least square support vector machine (LSSVM), cascade feedforward neural network (CFNN), and ridge regression with superior error metrics (R = 0.951, RMSE = 0.196, MAPE = 62.22). It also demonstrated the least uncertainty (UI = 17.65), which indicates its reliability and accuracy. Risk analysis (RA) indicates that the EGPR model yields the lowest total risk score (TRS) (5.6) and is classified as a “Very Low” risk method. Furthermore, SHAP analysis exhibits that the solids content (SC) and flocculant dose (FD) positively influenced the prediction of ISR. Consequently, this study presents a foundational methodology for predicting ISR, which could be introduced as an essential tool for flocculate-settling studies that contribute to optimal chemical dosage, real-time contamination monitoring, and the operation of water recovery storage capacity.