The long way toward multivariate predictive control of flotation processes

Bergh, L.; Yianatos, J.

doi:10.1016/j.jprocont.2010.11.001

Cited by 121 publications

(43 citation statements)

References 26 publications

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“…Instrumentation for the measurement of important parameters such as: ore composition, flow rates and some ore specific properties (e.g. density, pH, pulp levels, particle size) have been available, however, some essential properties such as liberation degree, surface chemistry, bubble size distribution, bubble loading remain difficult to measure and infer [2,3].…”

Section: A Flotation Processmentioning

confidence: 99%

“…Multivariate predictive control is ideally the solution for high quality control. However, to be applicable without losing its benefits, good measurements, acceptable regulatory control of local objectives, reliable dynamic models, explicitly stated process constraints and new methods to promote robustness are needed [3,6]. Flotation processes have weaknesses in most of those aspects.…”

Section: B Modeling and Control Difficultiesmentioning

confidence: 99%

“…International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2016) Some critical aspects of control strategies are [3]: measurement instrumentation, data reconciliation, pattern recognition, fault detection and diagnosis, soft sensors, process and controller performance monitoring. The successful utilization of knowledge-based control strategies related to flotation is mostly dependent on the quality of information and process knowledge.…”

Section: B Modeling and Control Difficultiesmentioning

confidence: 99%

“…The main reason for this is the operational flexibility that exhibit flotation columns when compared to mechanical cell circuits. There are more variables available to shape its operation to the feed characteristics to meet the metallurgical targets [3,5]. The level of detail with which some fuzzy models are developed, is reflective of a more developed approach.…”

Section: B Application Of Fuzzy Logicmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence in Mineral Processing Plants: An Overview

Bergh¹

2016

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

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Abstract-This work review the applications of artificial intelligence in the field of mineral processing in the last decade. The mineral processing industry is characterized for the lack of reliable models and on-line information about key process variables. Therefore, heuristic methods have been extensively used to accommodate the plant operation looking for better metallurgical results. Applications to flotation plants are discussed.

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Section: A Flotation Processmentioning

confidence: 99%

Section: B Modeling and Control Difficultiesmentioning

confidence: 99%

Section: B Modeling and Control Difficultiesmentioning

confidence: 99%

Section: B Application Of Fuzzy Logicmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence in Mineral Processing Plants: An Overview

Bergh¹

2016

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

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“…A more recent elaborate summary of the existed physical and dynamic froth feature extraction was reported by Aldrich 11 , which pointed out that the machine vision based flotation processing monitoring and control attracts increasing inertest since its introduction in the late 1980s, however, linking the visual froth features and the flotation performance remained challenging and demands further researches. What's more, it is also reported that a fully automatic control of the flotation system remained a long way to improve 12 .…”

mentioning

confidence: 99%

Machine Vision Based Production Condition Classification and Recognition for Mineral Flotation Process Monitoring

Liu

Gui

Tang

et al. 2013

IJCIS

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A novel froth image analysis based production condition recognition method is presented to identify the froth phases under various production conditions. Gabor wavelet transformation is employed to froth image processing firstly due to the ability of Gabor functions in simulating the response of the simple cells in the visual cortex. Successively, the statistical distribution profiles based feature parameters of the Gabor filter responses rather than the conventional mean and variance are extracted to delineate the essential statistical information of the froth images. The amplitude and phase representations of the Gabor filter responses are both taken into account by empirical marginal and joint statistical modeling. At last, a simple learning vector quantization (LVQ) neural network model is used to learn an effective classifier to recognize the froth production conditions. The effectiveness of this method is validated by the real production data on industrial scale from a bauxite dressing plant.

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Probability Density Function of Bubble Size Based Reagent Dosage Control for Flotation Process

Zhu¹,

Gui²,

Yang³

et al. 2014

Asian Journal of Control

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As an effective measurement of bubble stability, bubble size structure is believed to be closely related to flotation performance. Reagent dosage has a very important influence on bubble size. A novel probability density function (PDF) of bubble size based reagent dosage control method is proposed in the paper. First, froth images acquired by camera with big and tiny blobs are segmented by use of an improved two-pass watershed algorithm. With non-Gaussian features, the PDF of the bubble size is hence approximated by the estimator based on B-spline such that the PDF of the bubble size is characterized by a weight vector of the B-spline. Finally, a PDF-based reagent dosage control algorithm is proposed to make the output PDF track the given PDF according to the linear matrix inequality technique, which is established to solve its stability condition by Lyapunov stability analysis. Experimental results show the effectiveness of the proposed method.

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The long way toward multivariate predictive control of flotation processes

Cited by 121 publications

References 26 publications

Artificial Intelligence in Mineral Processing Plants: An Overview

Artificial Intelligence in Mineral Processing Plants: An Overview

Machine Vision Based Production Condition Classification and Recognition for Mineral Flotation Process Monitoring

Probability Density Function of Bubble Size Based Reagent Dosage Control for Flotation Process

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