Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks
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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.About:
This article is published in Minerals Engineering.The article was published on 2014-12-01 and is currently open access. It has received 85 citations till now. The article focuses on the topics: Froth flotation.read more
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Machine learning applications in minerals processing: A review
John T. McCoy,Lidia Auret +1 more
TL;DR: This review aims at equipping researchers and industrial practitioners with structured knowledge on the state of machine learning applications in mineral processing with suggestions on data collection, technique comparison, industrial participation, cost-benefit analyses and the future of mineral engineering training.
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Froth image analysis by use of transfer learning and convolutional neural networks
TL;DR: The use of a convolutional neural network pretrained on a database of images of common objects, AlexNet, was used as is to extract features from flotation froth images, which could subsequently be used to predict the conditions or performance of the flotation systems.
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A watershed segmentation algorithm based on an optimal marker for bubble size measurement
TL;DR: A watershed segmentation algorithm with an optimal marker is proposed, based on the sub-images, and thus an improved sub-image classification model is built to reduce the under-segmentation.
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Machine vision based monitoring and analysis of a coal column flotation circuit
TL;DR: In this paper, a machine vision system is successfully developed and implemented in a coal column flotation circuit, which can be used for diagnosing the process conditions as well as predicting the process performance at different operating conditions.
Journal ArticleDOI
Soft computing-based modeling of flotation processes – A review
TL;DR: This paper attempts to provide an explanation for the current state and use of soft computing methods, as well as to present some ideas on future initiatives and potential developments within the area.
References
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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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Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis
J.M. Hargrave,S.T. Hall +1 more
TL;DR: In this article, the authors investigated the possibility of using image analysis and neural networks to develop an online control system for the flotation process using images of flotation froths captured at an industrial tin flotation plant.
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Control alternatives for flotation columns
Luis Bergh,Juan Yianatos +1 more
TL;DR: The optimisation of flotation column operation relies mostly on the identification of the key variables to be controlled, how accurately they can be measured, and how coordinately their influence can be dynamically influenced as mentioned in this paper.
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Interpretation of the effect of froth structure on the performance of froth flotation using image analysis
TL;DR: In this paper, the authors investigated the possibility of regulating the performance of a flotation cell by using image analysis to define a desired bubble size in the concentrate, and subsequently to use measured deviations to control bubble coalescence by the compensating addition of surfactants.
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The use of grey level measurement in predicting coal flotation performance
TL;DR: In this article, the grey level of coal froth was found to be correlated with flotation performance (e.g., ash content and froth mass flows rates) for both a single flotation cell and a bank of four pilot-scale cells.