Zinc floatation condition state dividing method based on isomerism textural features

Abstract

The invention discloses a zinc floatation state dividing method based on isomerism textural features. Zinc floatation image textural features are extracted by combining a gray-level co-occurrence matrix algorithm which has a good effect on high-frequency band textural features and a Gauss Markov random field algorithm which has a good modeling effect on low-and-medium-frequency texture images, and the zinc floatation image textural features are subjected to Gauss normalization to serve as a textural feature vector. In an integrated clustering algorithm, partitional clustering with high efficiency is conducted firstly to eliminate the influences of noise points and outliers, then a hierarchical clustering algorithm with high clustering quality and high stability is adopted to combine clustering centers output through partitional clustering, and then a final clustering result is obtained. Experiments prove that the extracted textural feature quantity has high mode separability, and foam in different states can be distinguished with the integrated clustering algorithm; furthermore, the method can be directly realized on a computer and is low in cost, high in efficiency and easy to implement.

Description

technical field [0001] The invention belongs to the technical field of froth flotation, and in particular relates to a method for dividing zinc flotation working conditions. Background technique [0002] Froth flotation is one of the most important beneficiation methods in zinc smelting today. The flotation method is a method that uses the different physical and chemical properties of the surface of mineral particles to cause different hydrophilicity, and then separates the minerals. It has a strong Practical value. Through continuous stirring and aeration during the flotation process, a large number of bubbles with different sizes, colors, shapes and textures can be formed, and mineral particles adhere to the surface of the bubbles to achieve mineral separation. Correct identification of flotation conditions is the basis and key to realize the optimal operation of flotation production. The visual characteristics of flotation froth contain a large amount of information rel...

AI Technical Summary

Problems solved by technology

However, the micro-heterogeneity, complexity, and ambiguity of the concept of the foam texture have brought great challenges to the texture extraction. It is difficult to identify the state of the zinc flotation foam with a single method. To classify and identify the selected state, it is necessary to analyze the texture features from multiple aspects to further realize the automatic classification and identification of flotation production conditions

At the same time, traditional foam image clustering mostly uses a single clustering model, which is difficult to meet the requirements of clustering accuracy and efficiency at the same time. Therefore, it is necessary to combine the advantages of multiple clustering methods to achieve a more accurate zinc flotation image clustering algorithm

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