Flotation concentrate grade detection method based on multi-parameter feature fusion

Abstract

The invention relates to a flotation concentrate grade detection method based on multi-parameter feature fusion. The method comprises the following steps: (1) collecting flotation mineral particles and flotation froth images; (2) detecting the particle size distribution condition of the collected mineral particle image based on an image processing technology; (3) extracting image features of flotation froth, and establishing a sample database; (4) measuring ore pulp concentration and density indexes on line; (5) establishing a multivariable concentrate grade prediction model by using the mathematical relationship among the ore pulp concentration, the density and the concentrate grade; (6) respectively establishing different concentrate grade prediction models based on five different neural networks; and (7) performing multivariable joint optimization by using parameters of the concentrate grade prediction model established by using a particle swarm algorithm, and taking an average value of accumulated sums of output results of the six models as an output value Y of the final flotation concentrate grade prediction model, namely a detection value of the concentrate grade. The method has the advantages that the flotation concentrate grade detection accuracy and precision are higher according to the prediction model.

Description

technical field [0001] The invention belongs to the technical field of flotation concentrate grade detection and analysis in mine development, and in particular relates to a flotation concentrate grade detection method based on multi-parameter feature fusion. Background technique [0002] Concentrate grade is a key process index that characterizes the product quality and production efficiency of the flotation process, and it is of great significance to control the concentrate grade within the target value range. The particle size, concentration and flotation foam characteristics of flotation pulp will all have an impact on the final concentrate grade. However, it is difficult to realize online continuous detection of concentrate grade, pulp concentration and particle size, and it is usually controlled indirectly by controlling the liquid level of the flotation tank, flotation reagents, etc., and the concentrate grade and these variables have strong coupling and nonlinear cha...

AI Technical Summary

Problems solved by technology

At present, the widely used automatic concentrate grade detection technology includes X-ray fluorescence analysis, but the ray type grade meter is dangerous during use, it is difficult to manage, and it is potentially harmful to the surrounding environment and the health of employees.

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