Abstract: Aiming at the problems such as low fitting degree and large prediction error of the existing flotation recovery prediction model,based on the actual working condition data of a copper mine,the box diagram and filtering algorithm were used to pre-process the data,and the corresponding flotation recovery prediction model was constructed by traditional machine learning algorithms (DT,SVR and RF algorithms) and deep learning algorithms (DNN and CNN algorithms). The fitting effect and prediction effect of five recovery prediction models were compared and analyzed,and verified by field data. The results showed that the RF prediction accuracy of the traditional machine learning algorithm model is the best,the error area of ±2% is 80. 1%,and the error area of ±4% is 93. 0%. The prediction effect of the deep learning model is better than that of the traditional machine learning algorithm model. The R2 of the DNN and CNN prediction models are 0. 854 and 0. 907,respectively; the accuracy of the ±2% error region is 91. 6% and 90. 6%,respectively;The accuracy of the ±4% error region is 96. 6% and 98. 1%,respectively. The CNN model is slightly better than the DNN model,but the training time is longer,thus the DNN model is the first choice in the deep learning algorithm model. The research results could provide technical support for real-time prediction of flotation recovery rate and collaborative optimization of flotation process.

Key words: flotation recovery rate,machine learning,data preprocessing,deep learning