Challenges of using froth features to predict clean coal ash content in coal flotation

Tan, Jiakun; Liang, Long; Peng, Yaoli; Xie, Guangyuan

doi:10.1080/19392699.2020.1789973

Cited by 8 publications

(1 citation statement)

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“…During the flotation process, the onsite staff artificially adjust the factors affecting flotation production. These factors include the addition of flotation reagents and the amount of aeration by observing the characteristic information of the foam surface. − However, owing to subjective bias, production environment changes, and other factors, the findings of manual judgment are frequently inaccurate, causing frequent fluctuations in flotation production indicators and low utilization of mineral resources . Consequently, using machine vision to capture the characteristic information of flotation foam, to monitor and identify the foam in the flotation process in real time, and to guide the adjustment of production factors, such as reagent dosage and liquid level, affecting flotation production increases the yield and economic benefits of the flotation concentrate in a coal washing plant. − …”

Section: Introductionmentioning

confidence: 99%

Multiscale Convolutional Network for Repairing Coal Slime Foam Images

et al. 2023

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Visual feature information regarding flotation foam is crucial for the flotation process. Owing to a large amount of noise and blur in the foam images collected in the floatation field, feature extraction and segmentation of foam images pose considerable challenges. Furthermore, the visual properties of foam are strongly correlated with current flotation conditions. Therefore, this study presents a method to repair blurred pixels in foam images. In addition to enhancing the image dataset necessary for network model training, the restored images can provide high-quality images extracting foam-feature information. In addition, this research presents a novel fifth-order residual structure that enlarges the network structure by stacking, enhancing the learning ability of complex networks. Experimental results demonstrate that the suggested method can achieve a satisfactory repair effect for foam images under various blurring conditions, laying a foundation for guiding the intelligent adjustment of flotation field parameters.

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