Prediction Model of Particle Size Distribution in Bauxite Continuous Ball Milling Process

doi:10.16182/j.issn1004731x.joss.201802006

Abstract

Abstract: As it is difficult to detect the particle size distribution of ball milling process on line, a prediction model of particle size distribution in bauxite continuous ball-milling process is proposed, which is based on data-driven method and population balance model (PBM) frame. The break-rate model structure of PBM is improved according to the characteristic data of batch grinding test of bauxite. The residual time distribution density function is improved by considering the characteristics of residence time distribution for different particle sizes. The key parameters of the model are optimized by the data of batch-test and continuous ball-milling process using back-calculation method. The industrial test data verification results show that the model accuracy meets the needs of practical production.

Key words: ball milling process, population balance model, residual time distribution, data-driven

CLC Number:

TP393

Ma Tianyu, Wang Yalin, Shen Kun, Liu Jinping. Prediction Model of Particle Size Distribution in Bauxite Continuous Ball Milling Process[J]. Journal of System Simulation, 2018, 30(2): 414-421.

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