System and method for predicating ball mill grind size distribution by fast Monte Carlo method

Abstract

The invention discloses a system for predicating ball mill grind size distribution by a fast Monte Carlo method and a method thereof, and the system comprises a data acquiring module, a particle size mass fraction calculating module, a representative particle diameter calculating module, a virtual particle number calculating module, a breaking rate calculating module, a breaking probability calculating module, a grinding particle number calculating module and a size distribution calculating module; the method comprises the following steps of: setting the initial time (t=0) of the ball mill, and acquiring relative data; calculating the mass fraction of each particle size of each initial material; calculating the representative particle diameter of each particle size; calculating the virtual particle number of each particle size; calculating the breaking rate of each particle size; calculating the breaking probability of each particle size; acquiring the number of grinding particles in each particle size after grinding by the ball mill; calculating grinded size distribution. In the system and the method, many breaking events are allowed to occur in each time interval, thereby precisely observing a microscopic grinding process and obtaining the size distribution change of the material 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

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