Automatic Control of Flotation Columns

Villar, R. del; Desbiens, André; Maldonado, M.; Bouchard, Jean-Marie

doi:10.1007/978-1-84996-106-6_6

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…The modified schematic representation is given in Figure 1. (adapted from Hodouin, 2011;Miljanović, 2008b) The influence of the diverse variables on the course and results of flotation process is the research topic for many authors (Lynch et al, 1981;Yianatos, 1989;Zhang, 1989;King, 2001;Vera, 2002;Drzymała, 2007;Uçurum & Bayat, 2007;Villar et al, 2010;Shammas et al, 2010;Maldonado, 2010;Rahman, 2012;Rath, 2013). A conclusion can be drawn from these extensive researches, is that the influence of flotation variables in varying degrees and their interactions on the one hand, and distinct tendency for achieving the process stability and products quality on the other hand are conflicted in the domain of process control and achieving the desired performances with the simultaneous maintenance of all disturbances under control.…”

Section: Flotation Variablesmentioning

confidence: 99%

Modelling Of Flotation Processes By Classical Mathematical Methods – A Review

Jovanović¹,

Miljanović²

2015

Archives of Mining Sciences

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Flotation process modelling is not a simple task, mostly because of the process complexity, i.e. the presence of a large number of variables that (to a lesser or a greater extent) affect the final outcome of the mineral particles separation based on the differences in their surface properties. The attempts toward the development of the quantitative predictive model that would fully describe the operation of an industrial flotation plant started in the middle of past century and it lasts to this day. This paper gives a review of published research activities directed toward the development of flotation models based on the classical mathematical rules. The description and systematization of classical flotation models were performed according to the available references, with emphasize exclusively given to the flotation process modelling, regardless of the model application in a certain control system. In accordance with the contemporary considerations, models were classified as the empirical, probabilistic, kinetic and population balance types. Each model type is presented through the aspects of flotation modelling at the macro and micro process levels.Keywords: flotation, mathematical modelling, empirical models, probabilistic models, population-balance models, kinetic modelsModelowanie procesów flotacji nie jest zagadnieniem prostym, głównie z uwagi na skomplikowany charakter samego procesu, czyli znaczną liczbę zmiennych które, w mniejszym lub w większym stopniu, mają wpływ na końcowy wynik procesu separacji cząstek materiału wykorzystującego różnice w ich właściwościach powierzchniowych. Próby stworzenia ilościowego modelu predyktywnego który w sposób pełny opisywałby przemysłowe procesy flotacji podjęto w połowie ubiegłego wieku a badania trwają po dzień dzisiejszy.W artykule przedstawiono przegląd działalności badawczej podejmowanej w celu opracowania modelu procesu flotacji opartego o zasady matematyki klasycznej. Opisu i systematyki modeli flotacji dokonano w oparciu o dostępną literaturę przedmiotu, główny nacisk kładąc na te modele, które wykorzystywane były wyłącznie do analizy procesu flotacji, bez względu na możliwość ich zastosowania także w układach sterowania. Zgodnie z obecnymi założeniami, modele sklasyfikowano jako empiryczne, probabilistyczne, kinetyczne oraz modele równowagi populacji. Każdy model zaprezentowany jest w kontekście modelowania procesu flotacji, z uwzględnieniem skali mikro oraz makro.Słowa kluczowe: flotacja, modelowanie matematyczne, modele empiryczne, modele probabilistyczne, modele równowagi populacji, modele kinetyczne IntroductionThe underlying principles of the flotation process are well established, but, according to the reports from many researchers, it is very hard to create the quantitative predictive models that can be used to simulate the operation of flotation cells in typical industrial circuits. The reason for these difficulties lies in the complexity of the many microprocesses and their mutual interactions influencing overall result, which is the separation of d...

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Section: Flotation Variablesmentioning

confidence: 99%

Modelling Of Flotation Processes By Classical Mathematical Methods – A Review

Jovanović¹,

Miljanović²

2015

Archives of Mining Sciences

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“…Galena (PbS) and its oxidized forms cerussite (PbCO 3 ) and anglesite (PbSO 4 ) are the most common forms of lead in the earth’s crust. Of these minerals, galena has the highest lead content, and it coexists in nature with sphalerite (ZnS), chalcopyrite (CuFeS 2 ), pyrite (FeS 2 ), quartz (SiO 2 ), calcite (CaCO 3 ), fluorite (CaF 2 ), silver, and so forth, thus, froth flotation can be applied selectively in order to obtain high-grade concentrates. , …”

Section: Introductionmentioning

confidence: 99%

Determination of Optimum Parameters for Flotation of Galena: Effect of Chain Length and Chain Structure of Xanthates on Flotation Recovery

Özün

Ergen

2019

ACS Omega

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The structure of the xanthates’ hydrocarbon (C–H) chain is one of the major factors which affect flotation recovery. The effectiveness and the collecting power of xanthates increase with increasing chain length and vary depending on the chain structure: branched and straight chains. In this regard, the influences of length (2–5 carbon) and structure (straight: normal and branched: iso) of xanthate’s hydrocarbon chain on flotation recovery of galena were investigated under different experimental conditions: xanthate concentration, conditioning time, air flow rate (AFR), and air bubble size. Because of the steric effects of the chain structure, the branched chain xanthates gave lower flotation recoveries with shorter conditioning times compared to those with straight chain xanthates. Over-conditioning with straight chain xanthates resulted in hydrophobic aggregation of galena particles which resulted in the detachment of galena particles from air bubbles due to increasing weight, leading to lower flotation recoveries. In the case of flotation with different AFRs, the flotation recoveries increased with increasing AFR to 7 lph and further increase in AFR (10 lph) negatively affected the flotation recoveries when particles had insufficient hydrophobic surfaces. The maximum flotation recoveries were obtained with the addition of MIBC (methyl isobutyl carbinol) as a frother; the size of the air bubbles deceased with increasing MIBC concentration which increased higher encounter/collision probability of air bubbles with galena particles, resulting in higher flotation recoveries.

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“…Although mineral grade is a key indicator of performance in flotation systems, its online measurement is challenging, and in turn this is an impediment to the development of advanced process control systems in flotation. Real-time grade information can be obtained by the use of X-ray on-stream analysers, but these devices are expensive, and can have long sampling times, with limited accuracy (Del Villar et al 2010). In principle, these difficulties can be circumvented by the use of froth image analysis (Duchesne 2010), and several studies have indicated that the concentrate grade of a froth flotation system can be inferred from the spectral and textural features extracted from froth images (Moolman et al 1995; Bonifazi et al 2000; Kaartinen et al 2006; Aldrich et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Multivariate image analysis of realgar–orpiment flotation froths

Aldrich

Smith

Verrelli

et al. 2017

Mineral Processing and Extractive Metallurgy

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Multivariate image analysis was used to estimate the arsenic concentrations in froths resulting from the flotation of different mixtures of realgar and orpiment particles in a laboratory batch flotation cell. The realgar floated rapidly and in excess of 90% of the mineral could be recovered after 2 minutes, whereas only 48-75% of the orpiment could be recovered in the same time. Textural features, based on grey level co-occurrence matrices (GLCMs), local binary patterns (LBPs), steearable pyramids and textons were used in the analysis. Random forest models could explain approximately 71-77% of the variance in the arsenic using either of the texton, steerable pyramid or LBP features. This was considerably better than what could be obtained with the GLCM features. Monitoring of froth flotation cells was simulated with the batch data. The texton textural features were the most discriminatory with regard to detecting changes in the arsenic content of the froth.

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Automatic Control of Flotation Columns

Cited by 7 publications

References 43 publications

Modelling Of Flotation Processes By Classical Mathematical Methods – A Review

Modelling Of Flotation Processes By Classical Mathematical Methods – A Review

Determination of Optimum Parameters for Flotation of Galena: Effect of Chain Length and Chain Structure of Xanthates on Flotation Recovery

Multivariate image analysis of realgar–orpiment flotation froths

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