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.
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.
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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.
155 citations
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.
83 citations
Cites background from "Prediction of the metallurgical per..."
...Models based on artificial neural networks have been popular choices in this regard (Moolman et al., 1995; Marais and Aldrich, 2010; Jahedsaravani et al., 2014)....
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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.
53 citations
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.
49 citations
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.
47 citations
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01 Jul 1994TL;DR: In this chapter seven Neural Nets based on Competition, Adaptive Resonance Theory, and Backpropagation Neural Net are studied.
Abstract: 1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. References. Index.
2,665 citations
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.
203 citations
TL;DR: In this paper, a multiresolutional multivariate image analysis (MR-MIA) is proposed for the monitoring and control of flotation processes, which can handle spatial (i.e., morphological) and color information of froth images efficiently, and is inherently robust to image quality and lighting conditions.
132 citations
TL;DR: In this paper, images from pyrite batch flotation tests conducted after a factorial design and images from a copper flotation plant were used to understand the relationship between froth characteristics and flotation performance better.
122 citations
TL;DR: In this article, a multi-camera system and expert controllers were used to improve flotation performance of Pyhasalmi mine in Finland. But the results showed that the new approach has only improved the recovery of the zinc circuit, resulting in a substantial increase in the mill's annual profit.
120 citations