Optimization study of mineral processing in elutriation machine using DE-SVM algorithm
This study explores the application of a hybrid algorithm based on Differential Evolution(DE)and Support Vector Machine(SVM)in the mineral processing of elutriation machine.To address the problems of low quality and efficiency in metal beneficiation during elutriation,the DE-SVM algorithm was proposed,and a corresponding beneficiation quality prediction model was constructed.Experimental results showed that the average prediction accuracy and precision of the DE-SVM algorithm were 93.7％and 95.6％,respectively.The predicted concentrate recovery rate and the absolute error of predicted concentrate grade using the model were 98.4％and 0.309％,respectively.Compared with other algorithms and models,the DE-SVM algorithm and its associated elutriation machine beneficiation quality prediction model demonstrated significant advantages,providing an effective method to improve the quality and efficiency of precious metal beneficiation.
DE algorithmSVMbeneficiationelutriation machineprocess optimization
万方数据
维普
