Abstract
New research on Machine Learning is th e subject of a report. According to news reporting originating in Zibo, People's Republic of China, by NewsRx journalists, research stated, "Recleaning phosphat e tailings using the low-cost enhanced gravity separation method is beneficial f or maximizing the recovery of phosphorus element. A machine learning framework w as constructed to predict the target variables of the yield, grade, and recovery from the feature variables of slurry concentration, backwash water pressure, an d rotational frequency of bowl, whose data came from the phosphate tailings sepa ration experiments in the enhanced gravity field." The news reporters obtained a quote from the research from the Shandong Universi ty of Technology, "The coefficient of determination R and mean squared error wer e used to evaluate the performance of seven machine learning models. After hyper -parameter optimization, GBR demonstrated the best performance in predicting yie ld, grade, and recovery, with prediction accuracy of 95.58 %, 90.72 %, and 94.25 %, respectively. SHapley Additive exPlan ations interpretability analysis revealed that the rotational frequency of the b owl had the most significant impact on the grade and recovery of concentrates, w hile slurry concentration had the most significant effect on the yield. A lower rotational frequency of the bowl, a higher slurry concentration, and an increase d backwash water pressure were positively correlated with both the yield and rec overy. However, the grade was favorably correlated with a higher rotational freq uency 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."
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