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A Prediction Method of Material Particle Size Distribution in Ball Milling Process

A particle size distribution and material technology, applied in the direction of neural learning methods, biological neural network models, special data processing applications, etc., can solve problems such as the inability to guarantee long-term prediction accuracy, complex ore sources, complex and variable time-varying process dynamic characteristics, etc.

Active Publication Date: 2018-02-23
CENT SOUTH UNIV
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  • Abstract
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Problems solved by technology

However, domestic ore sources are complex, and the dynamic characteristics of the process are complex, changeable, and time-varying. If the prediction model cannot be corrected online, its prediction accuracy cannot be guaranteed for a long time

Method used

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  • A Prediction Method of Material Particle Size Distribution in Ball Milling Process
  • A Prediction Method of Material Particle Size Distribution in Ball Milling Process
  • A Prediction Method of Material Particle Size Distribution in Ball Milling Process

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Embodiment Construction

[0015] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0016] Aiming at the shortcomings of insufficient prediction of the particle size distribution of ball milled materials in the prior art and the inability to correct the prediction results online, the present invention proposes a method for predicting the particle size distribution of ball milled materials.

[0017] figure 1 The flow chart of the method for predicting the particle size distribution of the material in the ball milling process of the embodiment of the present invention is shown, as shown in the figure, including:

[0018] S1, based on the material's crushing distribution function, continuous crushing rate function and residence time distribution function, obtain the prediction model of the particle si...

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Abstract

The invention provides a predicting method of material grain size distribution in a ball-milling process. The method comprises the following steps: acquiring a material grain size distribution predicting model of continuous ore grinding based on a crushing distribution function, a continuous crushing rate function and a standing time distribution function of a material; acquiring a predicted error between the predicted material grain size distribution and the actual material grain size distribution based on the material grain size distribution predicting function; and setting an adjusting threshold value and a plurality of error intervals, and correspondingly adjusting the predicting model based on the relationship between the probability of the predicting error in different error intervals within a certain time and the adjusting threshold value. The method provided by the invention creates conditions for guiding ball-milling production and realizing optimized control of the ball-milling process and energy-saving power consumption.

Description

Technical field [0001] The invention relates to the technical field of ball milling material analysis, and more specifically, to a method for predicting the particle size distribution of materials in the ball milling process. Background technique [0002] The ball milling process is an important part of the beneficiation process, which uses a ball mill to grind the minerals to the size required by the operation to facilitate flotation and obtain more concentrates. In the ball milling process, over-grinding will cause the power consumption and steel consumption of the ball mill itself, and also increase the power consumption of the classifier; under-grinding will also cause energy consumption, which cannot meet the subsequent production requirements. For this reason, optimizing the operation of the ball milling process to grind minerals to the required particle size and maintain stability is of great significance for improving the flotation efficiency and comprehensive utilization...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50G06N3/08
CPCG06F30/20G06N3/08
Inventor 王雅琳杨少明孙备张鹏程彭凯王晓丽桂卫华
Owner CENT SOUTH UNIV
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