Research on prediction model of ore grinding particle size distribution

Abstract

The prediction of grinding particle size is an effective measure to optimize the grinding process. Cassiterite polymetallic sulfide ore and lead-zinc ore, as the research object in this paper, their particle size prediction mechanism are studied based on the drop weight test, batch grinding test, the theory law of media motion in ball mill and population balance model. The results show that particle size distribution of crushing products under different crushing energies and ore particle sizes is obtained by drop weight test, and the crushing parameters A and b are calculated by fitting regression, and then the correlation between t_x and t₁₀ obtained in drop weight test is also applicable to the correlation between t_x and t₁₀ of grinding products in ball mill medium throwing motion, which can effectively solve the crushing function and the selection function with the help of Matlab, and then establish the grinding population balance prediction model based on the drop weight test and the motion law of ball mill medium. The adaptability and reliability of the prediction model of grinding particle size are verified by batch grinding test.

Graphical Abstract

Keywords:

• Grinding
• particle size distribution
• prediction model
• drop weight test
• theory law of media motion

Additional information

Funding

National Natural Science Foundation of China (No. 51741401; 51264001); Preliminary Research Projects of 973 Program (No. 2014 CB460606); Guangxi Natural Science Foundation (No. 2018GXNSFAA281204).