Prediction for the recycle of phosphate tailings in enhanced gravity field based on machine learning and interpretable analysis.

Recleaning phosphate tailings using the low-cost enhanced gravity separation method is beneficial for maximizing the recovery of phosphorus element. A machine learning framework was constructed to predict the target variables of the yield, grade, and recovery from the feature variables of slurry concentration, backwash water pressure, and rotational frequency of bowl, whose data came from the phosphate tailings separation experiments in the enhanced gravity field. The coefficient of determination R and mean squared error were used to evaluate the performance of seven machine learning models. After hyper-parameter optimization, GBR demonstrated the best performance in predicting yield, grade, and recovery, with prediction accuracy of 95.58 %, 90.72 %, and 94.25 %, respectively. SHapley Additive exPlanations interpretability analysis revealed that the rotational frequency of the bowl had the most significant impact on the grade and recovery of concentrates, while slurry concentration had the most significant effect on the yield. A lower rotational frequency of the bowl, a higher slurry concentration, and an increased backwash water pressure were positively correlated with both the yield and recovery. However, the grade was favorably correlated with a higher rotational frequency of bowl and a lower slurry concentration, whereas its correlation with the backwash water pressure could be positive or adverse, depending on its specific value. The limitations and implications of these findings were also demonstrated, and the constructed framework was anticipated to achieve higher prediction accuracy with reasonable interpretability in further studies.