Effect of particle size on magnesite flotation based on kinetic studies and machine learning simulation

This research focused on the effect of particle size and flotation time on magnesite flotation, and the flotation performance of various size fractions were predicted by a machine learning (ML) method. Four kinetic models were used to fit the recovery of MgO and SiO2 in various size fractions of magnesite flotation. The results demonstrated that the flotation of magnesite exhibits good agreement with the classical first-order kinetic model. Besides, the effect of various particle sizes on MgO recovery and selectivity index was predicted by ML method. It was shown that the proposed ML model could accurately reproduce the effects of particle size and flotation time on magnesite flotation performance. Furthermore, the developed model revealed that the optimal mean size range for magnesite flotation is 30 to 48 μm. Therefore, this paper is of great significance to the application of ML methods in the prediction of various magnesite size flotation performance. [Display omitted] •The classical first-order flotation kinetic model is suitable for magnesite flotation.•Flotation is more effective for separation of intermediate particles.•The maximum of selectivity index appears under the intermediate particles flotation.•ET model has good prediction effect on magnesite flotation performance.•The predicted optimum magnesite flotation size range is 30 to 48 μm.