The flotation of millerite — A single mineral study

Smith, L.K.; Senior, G.D.; Bruckard, Warren J.; Davey, K.J.

doi:10.1016/j.minpro.2011.02.004

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…To this end, a realgar (A 2 S)-orpiment (A 2 S 3 ) system with a quartz diluent was investigated. These arsenic sulphides are present as gangue materials in base metal flotation, especially copper and nickel systems (Smith et al 2013), where their removal is problematic (Smith et al 2012). Since arsenic is a penalty element for base metal smelters (Ma & Bruckard 2009;Haque et al 2012), an online system for detecting arsenic in froth concentrate, such as considered in this paper, could be of considerable commercial benefit.…”

Section: Introductionmentioning

confidence: 99%

Multivariate image analysis of realgar–orpiment flotation froths

Aldrich

Smith

Verrelli

et al. 2017

Mineral Processing and Extractive Metallurgy

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Multivariate image analysis was used to estimate the arsenic concentrations in froths resulting from the flotation of different mixtures of realgar and orpiment particles in a laboratory batch flotation cell. The realgar floated rapidly and in excess of 90% of the mineral could be recovered after 2 minutes, whereas only 48-75% of the orpiment could be recovered in the same time. Textural features, based on grey level co-occurrence matrices (GLCMs), local binary patterns (LBPs), steearable pyramids and textons were used in the analysis. Random forest models could explain approximately 71-77% of the variance in the arsenic using either of the texton, steerable pyramid or LBP features. This was considerably better than what could be obtained with the GLCM features. Monitoring of froth flotation cells was simulated with the batch data. The texton textural features were the most discriminatory with regard to detecting changes in the arsenic content of the froth.

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Section: Introductionmentioning

confidence: 99%