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Pellet consolidation degree evaluation method based on image recognition

A technology of image recognition and evaluation method, which is applied in the field of evaluation of consolidation degree of pellets based on image recognition, can solve the problems of unformed high temperature evaluation index, sintering neck and particle size error, etc. The effect of avoiding human error

Pending Publication Date: 2021-10-29
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the small size and irregular shape of the particles inside the pellets, as well as the limitation of image resolution, this statistical method often causes relatively large errors in the characterization of the sintering neck and particle size
More importantly, the growth behavior of particles, such as the growth index of ore powder during roasting, the uniformity index of grains, and the temperature sensitivity of the continuous crystal process, have not yet formed a unified high-temperature evaluation index.

Method used

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  • Pellet consolidation degree evaluation method based on image recognition
  • Pellet consolidation degree evaluation method based on image recognition
  • Pellet consolidation degree evaluation method based on image recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0078] Example 1: The TFE content is 71.66%, SiO2 is 0.19% high-speed low-silicon magnet mine group roasted consolidation.

[0079] Take 50g high-speed low-silicon magnet mine powder and 4 ml of deionized water, then take 15 g of iron ore powder to the ball group, and the pressed ball is dried in a drying box of 105 ° C to remove the interior of the ball. Free water. Then, the ball was placed in an alumina porcelain, preheating at an Ar (3L / min) protective atmosphere conditions for 5 min. When the temperature reaches 850 ° C, the gas is converted to compressed air (5 l / min) oxidation for 30 min. After the oxidation, the pellet mine is transferred to another high temperature furnace for 30 min, the calcination temperature is set to 1200 ° C, 1250 ° C, 1250 ° C, 1250 ° C 1300 ° C, by analysis of the high-speed rail low silicon magnet mine group SEM image, the internal particle area of ​​the oxide group is mainly concentrated between 0 and 8700 μm2, the total area of ​​the partic...

Embodiment 2

[0080] Example 2: The TFE content was 64.97%, SiO2 was 6.90% of low-iron high silicon magnet mine group baked consolidation.

[0081] Take 50G low-iron high silicon magnet mine powder and 4 ml of deionized water, then take 15 g of iron ore powder into a ball, and press the pressed ball in a dry box of 105 ° C to remove the ball. Internal free water. Then, the ball was placed in an alumina porcelain, preheating at an Ar (3L / min) protective atmosphere conditions for 5 min. When the temperature reaches 850 ° C, the gas is converted to compressed air (5 l / min) oxidation for 30 min. After the oxidation, the pellet mine is transferred to another high temperature furnace for 30 min, the calcination temperature is set to 1200 ° C, 1250 ° C, 1250 ° C, 1250 ° C 1300 ° C, by analyzing the low-iron high silicon magnet mine group SEM image, the internal particle area of ​​the oxide group is mainly concentrated between 0 and 8700 μm2, most concentrated between 0 to 2100 μm2, and the total p...

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Abstract

The invention discloses a pellet consolidation degree evaluation method based on image recognition. The pellet consolidation degree evaluation method comprises the steps of preparing pellets; acquiring a pellet image by adopting a scanning electron microscope or a mineral phase microscope; performing image recognition on the obtained image to obtain the number, perimeter and area of particles in the image; and proposing a pellet consolidation evaluation system based on an image recognition result, and calculating a pellet internal particle growth index, a particle uniformity index, a particle consolidation index and a roasting curing degree. Comprising the following steps: based on a pellet phase structure image, adopting intelligent algorithms such as watershed segmentation and a convolutional neural network to realize segmentation and identification of particles in pellets; obtaining parameters such as the number of particles in the pellets, the boundary perimeter of the particles and the area of the particles through automatic quantification; obtaining the pellet internal particle growth index, the particle uniformity index, the particle consolidation index and the particle curing degree. The invention is of great significance in evaluating the crystal connection performance of the iron ore powder and optimizing roasting process parameters of the pellets.

Description

Technical field [0001] The present invention relates to the technical findings of iron ore oxide groups, and in particular, to a magazine-based consolidation degree evaluation method based on image recognition. Background technique [0002] As an important iron-containing raw material of the blast furnace iron, the ball is mostly a sphere of 8 to 16 mm in diameter. The pellet mine is prepared to contain the ball, the preheating roasting process, covering the mixing of raw materials such as fine ore, dolomite and bentonite, and then oxidation and calcination under high temperature conditions to achieve the chemical composition required for smelting, mechanical properties And metallurgical properties. Among them, the technical parameters such as roasting temperature, calcination time have a great impact on mineral composition, mechanical strength and metallurgical properties of the pellets. It is well known that the solids of the pocolite is mainly based on solid phase reactions, i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06T5/30G06N3/04G06N3/08C22B1/16G01N15/00G01N15/10
CPCG06T7/0002G06T5/30G06N3/08G01N15/00G01N15/10C22B1/16G06T2207/10061G06T2207/20032G06N3/045G01N2015/1029G06T5/70
Inventor 王耀祖贺威刘征建张建良于欣波侯静怡马黎明马云飞
Owner UNIV OF SCI & TECH BEIJING