Coal slime flotation foam image classification method based on semi-supervised clustering

Abstract

In order to solve problems of subjectivity, hysteresis and extensiveness existed in reagent amount addition of coal slime flotation in coal preparation plant depended on manual intervention, a coal slime flotation foam image classification method based on semi-supervised clustering was proposed. Firstly, coal slime flotation foam images under known reagent-addition ratio and unknown reagent-addition ratio are collected, and the foam images are preprocessed to extract morphological characteristics such as bubble number, bubble area and bubble perimeter. Then, foam image morphological characteristic samples under known reagent-addition ratio are marked, while foam image morphological characteristic samples under unknown reagent-addition ratio are not marked, and the marked foam image morphological characteristic samples and the unmarked foam image morphological characteristic samples are mixed. Finally, semi-supervised clustering method based on Gaussian mixture model is used to cluster the mixed samples, so as to get various clusters, and information of the marked foam image morphological characteristic samples is mapped to the unmarked foam image morphological characteristic samples in various clusters. The application results show that the method can provide guidance for adjustment of reagent-addition amount in coal slime flotation production process, reduce consumption of reagent, and improve flotation automation level and economic benefit of coal preparation plant.

Keywords

• coal preparation plant
• coal slime flotation
• flotation foam
• reagent-addition amount
• image feature extraction
• semi-supervised clustering
• gaussian mixture model