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Flotation working condition classification method and system

A classification method and flotation technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve problems such as difficulty in obtaining image information, underutilization of foam video, information loss, etc.

Active Publication Date: 2017-11-24
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the image feature extraction used in the current working condition recognition method has the following limitations: 1. The extraction of foam features is actually a dimensionality reduction calculation process for foam grayscale images, and information loss is inevitable during the extraction process, making it difficult to obtain Essential image information; 2. A large number of foam videos collected by industrial cameras have not been fully utilized, and the foam characteristics obtained for some foam videos cannot perfectly reflect the current working conditions of the foam
Therefore, there are still some problems in the application of the flotation monitoring system based on machine vision technology in the actual flotation plant

Method used

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  • Flotation working condition classification method and system
  • Flotation working condition classification method and system

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Embodiment 1

[0089] see figure 1 , this embodiment discloses a method for classifying flotation working conditions, including:

[0090] Obtain the flotation foam image and perform image preprocessing to obtain a foam image set suitable for building a model;

[0091] A two-step watershed algorithm is used for image segmentation for each single-frame foam image in the foam image set, and each bubble region is saved as a bubble image to obtain a bubble image set of all single-frame foam images in the foam image set;

[0092] Extract the morphological feature vector of the bubble image, and pre-classify the bubble image set according to the morphological feature vector, and obtain the classification label value of the bubble image; The convolutional neural network extracts the pixel set features of the bubble image, and trains a deep convolutional neural network model through a large number of existing bubble image data sets;

[0093] Extract the pixel set features of each bubble image accor...

Embodiment 2

[0167] A system for implementing the above-mentioned method for classifying flotation conditions, characterized in that it includes:

[0168] The first unit: used to obtain the flotation foam image, and perform image preprocessing to obtain a foam image set suitable for building a model;

[0169] The second unit: it is used to segment each single-frame foam image in the foam image set using a two-step watershed algorithm, and each bubble area is saved as a bubble image to obtain all single-frame foam images in the foam image set bubble image set;

[0170]The third unit: used to extract the morphological feature vector of the bubble image, and pre-classify the bubble image according to the morphological feature vector, and obtain the classification label value of the bubble image; use a deep convolutional neural network to extract the bubble The pixel set features of the image, combined with a large number of existing bubble image data sets corresponding to the classification ...

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Abstract

The invention relates to the field of foam flotation automation, and discloses a flotation working condition classification method and a system to provide guidance for subsequent chemical dosing and other operations, and thus improve economic and technical indexes of a flotation process and reduce labour intensity of workers. According to the method, foam images are preprocessed and segmented to obtain all images formed by single bubbles; morphological feature vectors of the images of the single bubbles are extracted; pixel set features of all the image of the bubbles are extracted according to a deep convolutional neural network model, the morphological feature vectors corresponding to the images of the bubbles are combined to finely classify the images of the bubbles, appearance frequency of the various bubbles in the foam images is counted to form a bubble classification frequency set of the single-frame foam images, correspondence relationships between the bubble classification frequency set of the foam images and typical flotation working conditions are analyzed, and thus flotation working condition types reflected by the foam images are obtained.

Description

technical field [0001] The invention relates to the field of froth flotation automation, in particular to a method and system for classifying flotation working conditions. Background technique [0002] Foam flotation is currently one of the most important mineral separation methods in metal smelting. It is based on surface chemistry and separates different minerals according to the surface wettability of different mineral particles. A large number of air bubbles are formed by ground stirring and aeration, and agents are added to adjust the surface properties of different mineral particles, so that useful mineral particles adhere to the surface of the air bubbles, while gangue mineral particles stay in the slurry, thereby realizing mineral separation. Therefore, flotation is a continuous physical and chemical process that occurs on the interface of solid, liquid and gas. The quality of flotation refers to the working state and performance indicators of the flotation process. ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06N3/04
CPCG06V10/462G06N3/045G06F18/2411G06F18/214
Inventor 王晓丽宋晨阳春华谢永芳徐德刚
Owner CENT SOUTH UNIV
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