System and method for predicting grinding particle size distribution of ball mill by fast Monte Carlo method

Abstract

A system and method for predicting ball mill grinding particle size distribution using a fast Monte Carlo method. The system includes: a data acquisition module, a mass fraction calculation module for each particle size, a representative particle size calculation module, a virtual particle number calculation module, and a crushing rate calculation module. , crushing probability calculation module, grinding particle number calculation module and particle size distribution calculation module; the method includes: setting the initial time of the ball mill to t=0, and at the same time, obtaining corresponding data; calculating the mass fraction of each particle size of the initial material; Calculate the representative particle size of each particle size; calculate the number of virtual particles for each particle size; calculate the crushing rate of each particle size; calculate the crushing probability of each particle size; obtain the internal grinding of each particle size after ball mill grinding Number of particles; calculate particle size distribution after grinding. The invention allows multiple crushing events to occur within each time interval, and can accurately observe the microscopic grinding process, thereby obtaining changes in the particle size distribution of materials in the ball mill before and after grinding.

Description

technical field [0001] The invention belongs to the technical field of simulation and Monte Carlo simulation of a ball mill grinding process, and in particular relates to a system and method for predicting the particle size distribution of a ball mill grinding by a fast Monte Carlo method. Background technique [0002] In the mineral processing industry, ore grinding is an intermediate process in the entire production process, a continuation of the ore crushing process, and also the last processing of ore before separation. The purpose of the grinding operation is to separate all or most of the useful components in the ore, and at the same time try to avoid over-grinding and meet the particle size requirements of the sorting operation. The production quality index of grinding is mainly measured by the grinding particle size. Grinding particle size should meet the requirements of sorting operation, too high or too low is not suitable. If the fineness is not enough, the mine...

AI Technical Summary

Problems solved by technology

However, due to the randomness of the crushing of grinding particles, it is difficult for a deterministic model to express the randomness of the system, so it is difficult for the population balance equation to observe the microscopic grinding process

[0004] The grinding process shows strong discreteness and randomness, so the differential equations established based on the general balance principle are difficult to describe the dynamic characteristics of grinding particles

The stochastic simulation method represented by the Monte Carlo simulation method can accurately describe the grinding particle crushing process, but the traditional Monte Carlo method only allows one crushing event to occur at a time. Low-efficiency prediction of ball mill grinding particle size by Luo method and lack of practicability

Images