Multivariate image analysis of realgar–orpiment flotation froths

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...

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Main Authors: Aldrich, Chris, Smith, L., Verrelli, D., Bruckard, W., Kistner, M.
Format: Journal Article
Published: Maney Publishing 2017
Online Access:http://hdl.handle.net/20.500.11937/54467
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author Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
author_facet Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
author_sort Aldrich, Chris
building Curtin Institutional Repository
collection Online Access
description 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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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:58:56Z
publishDate 2017
publisher Maney Publishing
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spelling curtin-20.500.11937-544672018-07-19T08:20:13Z Multivariate image analysis of realgar–orpiment flotation froths Aldrich, Chris Smith, L. Verrelli, D. Bruckard, W. Kistner, M. 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. 2017 Journal Article http://hdl.handle.net/20.500.11937/54467 10.1080/03719553.2017.1318570 Maney Publishing restricted
spellingShingle Aldrich, Chris
Smith, L.
Verrelli, D.
Bruckard, W.
Kistner, M.
Multivariate image analysis of realgar–orpiment flotation froths
title Multivariate image analysis of realgar–orpiment flotation froths
title_full Multivariate image analysis of realgar–orpiment flotation froths
title_fullStr Multivariate image analysis of realgar–orpiment flotation froths
title_full_unstemmed Multivariate image analysis of realgar–orpiment flotation froths
title_short Multivariate image analysis of realgar–orpiment flotation froths
title_sort multivariate image analysis of realgar–orpiment flotation froths
url http://hdl.handle.net/20.500.11937/54467