Soft investigating method for overflow grain index of ore grinding system based on case inference

Abstract

The invention is a soft measuring method for mineral grinding system flooding particle index, which is made up of hardware platform and measuring software, the method includes the selection of assistant variables, case representation, acquisition of initial case in case bank, case reasoning, case storing and maintaining steps. The method uses normal computer control system and normal detector to provide online process data, through a little artificial sampling, creates the soft measuring model of mineral grinding flooding particle based on case reasoning. Compared with normal particle meter, the method reduces the cost.

Description

technical field [0001] The invention belongs to the technical field of automatic measurement, and particularly relates to the measurement of the particle size index of the overflow of the hydrocyclone, the final product of the wet grinding system composed of a ball mill and a hydrocyclone, which is used to grind ore in the grinding section of a mineral processing plant. The method of soft measurement. Background technique [0002] In the mineral processing industry, a wet grinding system consisting of a ball mill and a hydrocyclone (referred to as a cyclone) is widely used to grind ore to the particle size range required by the mineral processing process. Too large or too small particles will affect the subsequent processing. Different operations will have adverse effects, so the cyclone overflow particle size (also known as grinding particle size, overflow particle size) is an important indicator to measure the operation quality of the grinding system. At present, there ar...

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Problems solved by technology

At present, there are two conventional detection methods for grinding particle size: one is manual sampling and offline testing, and manual measurement in the laboratory; the other is using particle size detection equipment - particle size meter for online measurement. The reasons are: 1. The influence of human factors is large during manual operation, and the objectivity of the measurement results is poor; 2. The time interval of the measurement is long, and the time for the feedback of the measurement results is also long, so the obtained information lacks guiding significance for the operator; the second method is insufficient The advantage is that although more accurate, objective and timely measurement results can be obtained, the particle size meter is expensive, and it is difficult to equip most of the concentrators in my country, and it is easy to block, and the workload of on-site maintenance is very large.

If the neural network is used for soft measurement of grinding particle size, it must be based on a comprehensive sample space, so the process of collecting samples will affect normal production

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