Estimation of particle size in hydrocyclone underflow streams by use of Multivariate Image Analysis

Aldrich, Chris; Uahengo, Foibe Dimbulukwa Lawanifwa; Kistner, Melissa

doi:10.1016/j.mineng.2014.08.018

Cited by 21 publications

(6 citation statements)

References 21 publications

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“…Variants of this approach are defined by the size of the pixel neighborhoods, the possible subdivision of the image in patches, as well as the inclusion of the number of transitions in the LBP vectors (uniformity) of the vectors. LBP feature extraction is a comparatively recent approach to multivariate image analysis in mineral processing [5,[47][48][49][50].…”

Section: Local Binary Patternsmentioning

confidence: 99%

Deep Learning Approaches to Image Texture Analysis in Material Processing

Liu

Aldrich

2022

Metals

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Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results.

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Section: Local Binary Patternsmentioning

confidence: 99%

Deep Learning Approaches to Image Texture Analysis in Material Processing

Liu

Aldrich

2022

Metals

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“…256 features for 8-bit images) to be used in the subsequent classification step. LBP feature extraction has been applied with considerable success in many fields, including remote sensing (Vatsavai et al 2010), face recognition (Ahonen et al 2006) and biomedicine (Nanni et al 2012), but has only recently been considered in mineral processing (Kistner et al 2013;Aldrich et al 2014Aldrich et al , 2015Fu & Aldrich 2016).…”

Section: Local Binary Pattern Operatorsmentioning

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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“…Eqs. (4) and (5) represent the main design equations for hydrocyclones, in which K, A, a, and b are parameters to be estimated from experimental measurements.…”

Section: Research Articlementioning

confidence: 99%

“…These devices are applied in several industrial sectors, such as mineral processing, chemical, petrochemical, food, and textile industries, wastewater treatment, among others. In the mineral processing industry they are employed to classify mineral slurry by particle size [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Effect of a Cylindrical Permeable Wall on the Performance of Hydrocyclones

2016

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Hydrocyclones are designed for solid-liquid separation widely used in industry due to their advantages including high separation efficiency. Depending on the purpose desired by the user, it is possible to enhance the performance of the hydrocyclone through the combined use of other unit operations with the hydrocycloning such as filtration. The incorporation effect of a porous cylinder on a hydrocyclone with optimized geometry was studied experimentally. According to the main results, the filtering cylindrical hydrocyclone showed significant average reductions in energy consumption compared to conventional hydrocyclones of the same geometry. Minor differences in terms of total efficiency of the filtering equipment were observed compared to the conventional one.

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Estimation of particle size in hydrocyclone underflow streams by use of Multivariate Image Analysis

Cited by 21 publications

References 21 publications

Deep Learning Approaches to Image Texture Analysis in Material Processing

Deep Learning Approaches to Image Texture Analysis in Material Processing

Multivariate image analysis of realgar–orpiment flotation froths

Effect of a Cylindrical Permeable Wall on the Performance of Hydrocyclones

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