Expert system for supervision of mineral flotation cells using artificial vision

Cipriano, Aldo; Sepúlveda, César; Guarini, Marcelo

doi:10.1109/isie.1997.651807

Cited by 5 publications

(3 citation statements)

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“…Flotation foam is an indispensable intermediate state variable in coal preparation, and the yield and ash of coal preparation products can be directly predicted by observing their shape, colour and bubble size. With the rapid development of computer and image processing technologies, flotation foam images can be automatically replaced with machine vision to monitor the flotation in real time by extracting the characteristic parameters of foam image and to accurately predict the yield and ash content of coal preparation products [1][2][3] .…”

Section: Research Backgroundmentioning

confidence: 99%

“…In mineral processing, Cipriano et al [4] first introduced image processing technology to mineral flotation, analysed flotation foam images, updated the mineral flotation working status in the database in real-time, and constructed an expert prediction system based on fuzzy theory and IF-THEN rule. Moolman et al [5] used four changes to confirm that the power spectral coefficient is related to bubble size.…”

Section: Research Backgroundmentioning

confidence: 99%

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Coal quality prediction based on multi-feature fusion of flotation foam images

Bai

2020

Preprint

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Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

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Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Coal quality prediction based on multi-feature fusion of flotation foam images

Bai

2020

Preprint

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“…In the flotation field, many investigations have been made for froth image utilisation (Bonifazi, Serranti, Volpe, & Zuco, 1999;Cipriano, Guarini, Soto, Briceno, & Mery, 1997;van Deventer Bezuidenhout, & Moolman, 1997;Hyotyniemi & Ylinen, 1998). However, the captured information is quite complex and requires the development of correlation equations with process variables that have to be controlled.…”

Section: Instrumentationmentioning

confidence: 99%

State of the art and challenges in mineral processing control

Hodouin

Jämsa-Jounela²,

Carvalho

et al. 2000

IFAC Proceedings Volumes

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The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal must be hierarchically decomposed into simpler control problems. Model-based and AI methods are reviewed, mainly for grinding and flotation processes, and classified as mature, active or emerging.

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