The concentrate ash content analysis of coal flotation based on froth images

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

doi:10.1016/j.mineng.2016.02.006

Cited by 49 publications

(12 citation statements)

References 60 publications

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“…Ash content is crucial for coal flotation efficiency. Froth is the key element of determining flotation concentrate, and between concentrate froth and the ash content, there is a strong relation [44].…”

Section: Ash Reductionmentioning

confidence: 99%

Reverse Flotation

Öztürk¹

2018

Energy Systems and Environment

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Reverse flotation of coal can be explained as a process where valuable minerals are depressed, while undesired and unhealthy minerals are floated with the help of some reagents. Nowadays, conventional enrichment method of coal could not achieve removing unhealthy minerals partially from internal structure of coal such as sulfur, Hg, Au, which propagate in air after burning treatment, or heavy metals such as Be, Cr, Ni, As, Cd, Co, Ni, Sb, Se, Pb, Co, Cl, Be, Ba, which involve in water and soil where habitat and human health can directly be influenced from them. In fact, reverse flotation of coal enables to remove these undesired mineral content from coal structure not only in macro size but also in micro size. On the other hand, like undesired minerals, valuable ones like vanadium, germanium, etc. are also taken from coal particles by using the same procedure. Thus, with all these respect, reverse flotation is considered as an alternative and innovative solution for coal beneficiation especially for low rank coal since low rank coal is more compatible for reverse flotation because of being more hydrophilic which means tendency to float is less. Around the world wide, most of the coal reserves belong to low rank coal, so application of reverse flotation is becoming more inevitably common in future.

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Section: Ash Reductionmentioning

confidence: 99%

Reverse Flotation

Öztürk¹

2018

Energy Systems and Environment

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“…In coal (gangue) separation technology [9], [10], besides manually selecting the gangue and coal, the automatic separation method can be split into dry selection and wet selection depending on the use of water resources. Wet selection of gangue mainly includes moving sieve jigging and heavy media separation of the gangue and so on.…”

Section: Introductionmentioning

confidence: 99%

Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network

Bian

2022

IEEE Access

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To effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared images without considering the selection of feature extraction and classifier. Firstly, the specific architecture and detailed parameters of the model are optimized and the CNN model based on only one Inception Block contains three different convolution kernels are considered to be the most appropriate model. Next, performance of the proposed identification model is analyzed and evaluated by the infrared image dataset, and we discovered that the CNN model is capable of correctly identifying 192 training samples and 48 test samples. Finally, compared with the traditional recognition model and other CNN recognition model, it is proved that the proposed CNN model has superior recognition performance. The results state clearly that the combination of infrared image and CNN can quickly and accurately identify coal and gangue without complex image processing steps. At the same time, the model has a certain anti-interference ability for different noises. And it has a certain reference value for the research and development of intelligent coal preparation equipment. INDEX TERMS Coal-gangue identification, convolutional neural network, infrared imaging technology, image identification.

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“…Unlike problems focused on the recognition of predefined operational regimes, which are typically treated as classification problems [25,26], or the use of computer vision systems as soft sensors that are typically casted as regression problems [27,28], monitoring in general is conducted in an unsupervised learning framework. These approaches are based on data representative of normal operating conditions (NOCs) against which new data can be compared to determine whether a deviation from NOCs has occurred.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Aldrich

Liu

2021

Minerals

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Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.

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The concentrate ash content analysis of coal flotation based on froth images

Cited by 49 publications

References 60 publications

Reverse Flotation

Reverse Flotation

Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network

Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

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