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.