Image data extraction and neural network modeling-based platinum flotation grade estimation method

A neural network modeling and image data technology, applied in the field of platinum flotation grade estimation, can solve the problems of unrealistic equipment design and development, high maintenance cost of control system, and short service life of sensors, so as to improve production stability and reduce Labor cost, the effect of high degree of automation

Inactive Publication Date: 2015-02-04
FUZHOU UNIV
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Problems solved by technology

The disadvantages of the traditional method mainly include: the design and development of related equipment is not practical, the error of detection d

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  • Image data extraction and neural network modeling-based platinum flotation grade estimation method
  • Image data extraction and neural network modeling-based platinum flotation grade estimation method
  • Image data extraction and neural network modeling-based platinum flotation grade estimation method

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[0028] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0029] The invention provides a platinum flotation grade estimation method based on image data extraction and neural network modeling, such as figure 1 Shown, it is characterized in that, realize according to the following steps:

[0030] S1: Through the variable experiment, and analyze the results of the variable experiment through the Pallas distribution diagram, the degree of influence of the variable on the flotation grade and recovery rate is obtained; the variable includes aeration rate, pulp concentration, collector, Activators, blowing agents and inhibitors;

[0031] S2: Collect and preprocess the platinum foam image, and extract the characteristic data of the platinum foam image;

[0032] S3: Model the relationship between the platinum foam image characteristic data, flotation grade and recovery rate through a multi-layer perce...

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Abstract

The invention relates to an image data extraction and neural network modeling-based platinum flotation grade estimation method. The method comprises the following steps: acquiring the correlation degrees between six variables of aeration rate, pulp density, a collecting agent, an activating agent, a foaming agent and an inhibitor and flotation grade and recovery rate by an variable experiment; carrying out collection and pretreatment on a platinum foam image; extracting five image data of energy, entropy, inertia, homogeneity and gray correlation from four tolerant feature images such as gray level images, histogram equalization, contrast enhancement of images and image binarization obtained by pretreatment; and building a multi-layer perceptron neural network model comprising a three-node input layer, a hidden layer and a dual-node output layer. According to the image data extraction and neural network modeling-based platinum flotation grade estimation method provided by the invention, flotation grade and recovery rate are effectively estimated through the foam image, and the target of monitoring grade and recovery rate in the flotation process in real time is achieved.

Description

technical field [0001] The invention relates to the field of mineral separation, in particular to a platinum flotation grade estimation method based on image data extraction and neural network modeling. Background technique [0002] With the development of science and technology, the requirements for the automatic monitoring technology of various parameters in the beneficiation process are getting higher and higher, and many disadvantages of the traditional method are gradually emerging. Traditional monitoring technologies mainly include: fluorescence analysis and particle size analyzer, which can obtain product grade in a short time. The disadvantages of the traditional method mainly include: the design and development of related equipment is not practical, the error of detection data is large, the service life of the sensor is short due to the poor beneficiation environment, and the maintenance cost of the control system is high. [0003] Artificial neural network (ANN), ...

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Application Information

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IPC IPC(8): G06K9/64G06N3/02
CPCG06N3/049G06V30/195G06N3/044G06N3/045
Inventor 刘述忠郭万富王卫星
Owner FUZHOU UNIV
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