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Correction of froth gray value in the prediction of clean coal ash content in coal flotation

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TLDR
The correlation between froth properties and the quality of the froth product is the key to predict flotation performance and improve the automatic control of flotation as mentioned in this paper, and it is known that froth gray...
Abstract
The correlation between froth properties and the quality of the froth product is the key to predict flotation performance and improve the automatic control of flotation. It is known that froth gray...

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Citations
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LKDPNet: Large-Kernel Depthwise-Pointwise convolution neural network in estimating coal ash content via data augmentation

TL;DR: In this paper , a data augmentation method for a specific task was proposed to deal with the peculiarities of a small, unbalanced industrial dataset, where the receptive field of the model was adjusted by increasing the kernel size of the depthwise convolution layer and removing the downsampling block.
References
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Journal ArticleDOI

Online monitoring and control of froth flotation systems with machine vision: A review

TL;DR: Machine vision has been used to extract froth characteristics, both physical (e.g. bubble size) and dynamic (froth velocity) from digital images and present these results to operators and/or use the results as inputs to process control systems.
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A review of froth flotation control

TL;DR: In this article, a review of the four essential levels of process control (instrumentation, base level flotation control, advanced flotation, and optimising flotation) is presented.
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Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks

TL;DR: In this article, the relationship between the process conditions and the froth features as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled.
Journal ArticleDOI

Froth imaging, air recovery and bubble loading to describe flotation bank performance

TL;DR: In this article, video image analysis and direct sampling of the froth surface were combined to investigate the relationship between different imaging parameters accounting for the structural features of froth, and the separation efficiency of a flotation bank.
Journal ArticleDOI

The use of machine vision to predict flotation performance

TL;DR: In this paper, the authors investigated whether the measurement of physical machine vision measurements are able to provide accurate measures of mass recovery rate and concentrate grade across variations in operating conditions, and found that although good relationships are found in narrow conditions, a mechanistic understanding and model is needed to determine relationships that are useful over a wide range of operating conditions.
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