Froth Color Research Articles

Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

Machine vision based monitoring and analysis of a coal column flotation circuit

Flotation columns are being routinely used for recovery of fine coal particles in the coal preparation plants. Because of high sensitivity of the flotation columns to variations in the process conditions, their continuous control is of vital importance. Machine vision is an economically viable, uncomplicated and reliable technique for monitoring and control of flotation circuits. In this study, a machine vision system is successfully developed and implemented in a coal column flotation circuit. Industrial flotation experiments are conducted at various operating conditions (air flow rate, slurry solids%, froth depth, frother and collector dosage) and the froth visual (bubble size, froth velocity, froth color) and textural (energy, entropy, contrast, homogeneity and correlation) features along with the metallurgical performances (combustible recovery, concentrate ash content) are recorded simultaneously. The relationships between the froth characteristics with the process as well as the performance parameters are analyzed. The promising results indicate that the developed system can be successfully used for diagnosing the process conditions as well as predicting the process performance at different operating conditions.

Evaluation of column flotation froth behavior by image analysis: effects of operational factors in desulfurization of iron ore concentrate

ABSTRACTFlotation performance is expected to be affected by froth characteristics due to the changes in the operating conditions. However, very little study has been undertaken to investigate how froth behavior responds to the flotation variables. This study presents an experimental program performed to investigate the effects of flotation variables including gas flow rate, froth depth, slurry solids percent, frother/collector dosage, and pH on the froth features such as bubble size, froth velocity, froth color and froth stability during desulfurization of an iron ore. These parameters were varied using a central composite design (CCD). Image analysis methods have been developed and employed successfully to characterize the froth features. Results of the CCD experiments showed that the flotation variables have different effects on the froth features. The effect of pH and its interaction with collector dosage have a significant impact on all froth features. Process condition can be accurately deduced from the froth surface appearance which provides a convenient way to quantify the parameters as the process control function.

Application of Image Processing and Adaptive Neuro-fuzzy System for Estimation of the Metallurgical Parameters of a Flotation Process

It is a well-known fact in the literature and practice that flotation froth features are closely related to process conditions and performance. The authors have already developed some reliable algorithms for measurement of the froth surface visual parameters such as bubble size distribution, froth color, velocity and stability. Furthermore, the metallurgical parameters of a laboratory flotation cell were successfully predicted from the extracted froth features.In this research study, the fuzzy c-mean clustering technique is utilized to classify the froth images (collected under different process conditions) based on the extracted visual characteristics. The classification of the images is actually necessary to determine the ideal froth structure and the target set-points for a machine vision control system. The results show that the captured froth images are well-classified into five categorizes on the basis of the extracted features. The correlation between the visual properties of froth (in different classes) and the metallurgical parameters is discussed and modeled by the adaptive neuro-fuzzy inference system (ANFIS). The promising results illustrate that the performance of the existing batch flotation system can be satisfactorily estimated from the measured froth characteristics. Therefore, the outputs from the current machine vision system can be inputted to a process control system.

Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks

It is now generally accepted that froth appearance is a good indicative of the flotation performance. In this paper, 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. Flotation experiments were conducted at a wide range of operating conditions (i.e. gas flow rate, slurry solids%, frother/collector dosage and pH) and the froth features (i.e. bubble size, froth velocity, froth color and froth stability) along with the metallurgical performances (i.e. copper/mass/water recoveries and concentrate grade) were determined for each run. The relationships between the froth characteristics and performance parameters were successfully modeled using the neural networks. The performance of the developed models was evaluated by the correlation coefficient (R) and the root mean square error (RMSE). The results indicated that the copper recovery (RMSE=2.9; R=0.9), concentrate grade (RMSE=1.07; R=0.92), mass recovery (RMSE=1.94; R=0.94) and water recovery (RMSE=3.07; R=0.95) can be accurately predicted from the extracted surface froth features, which is of central importance for control purposes.

Nonparametric density estimation of froth colour texture distribution for monitoring sulphur flotation process

As an important indicator of flotation performance, froth texture is believed to be related with operational condition in sulphur flotation process. A novel froth images classification method based on froth colour texture unit distribution is proposed to recognise different performance of sulphur flotation in real time. The froth colour texture unit number is calculated by using colour value instead of grey level value in texture unit number, and the probability density function of froth colour texture unit number is defined as colour texture distribution, which can describe the actual textual feature more completely than traditional texture description approach. As the type of the froth colour texture distribution is unknown, a nonparametric kernel estimation method based on the fixed kernel basis is proposed. It is impossible to use the traditional varying kernel basis to compare different colour texture distributions under various conditions while the proposed fixed kernel basis can overcome the difficulty. Through transforming nonparametric description into dynamic kernel weight vector, the combination of normal kernel with polynomial kernel based sparse multiple-kernel least square support vector machine classifiers are constructed to realise the performance classification. Furthermore, the kernel matrices are reduced by Schmidt orthogonalisation theory to lower the computational complexity. The industrial application results show that the accurate performance classification of sulphur flotation can be achieved by using the proposed method.

Application of highlight removal and multivariate image analysis to color measurement of flotation bubble images

AbstractMachine vision based analysis provides a novel technology for froth flotation monitoring. Froth images collected are characterized by fully occupied bubbles with different size and shape under various illuminations. Convex bubbles lead to the formation of white spots that seriously affect froth color measurement. In this article, specular highlights are detected and preprocessed so as to estimate underlying color of white spots region. Because of the fact that color information is believed to be related to flotation performance, therefore, after the application of highlight inpainting, multivariate image analysis is proposed to extract color features, which are further related to mineral grades by a orthogonal least square regression model. The established relationship provides a promising empirical model to predict mineral grade, which is a significant indicator for flotation performance. Experimental results show that, when compared with traditional methods, the proposed algorithm can achieve a robust color measurement and predict mineral concentration effectively. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 316–322, 2009

Multivariable Expert Fuzzy Control for a Rougher Flotation Circuit, Considering Grade Estimation in Intermediate Cells.

Effective control of rougher flotation is important, because a small increase in recovery results in a significant economic benefit. Although many flotation control strategies have been proposed and implemented over the years, none of them incorporate concentrate grade measurements at intermediate cells because these data are not usually available. On the other hand, there is much research on characterizing concentrate froth on the cell surface by image processing in order to extract information on froth color, bubble size, and speed that can then be used for developing expert control strategies. This work presents two multivariable expert fuzzy control strategies for a rougher flotation circuit. The first strategy is based only on general tailings and concentrate grade measurements while the second one includes, beside these data, the intermediate cell grade estimates. Both strategies are compared with a fixed control strategy. Simulation tests show that the recovery can increase by 2,5%, compared to the fixed control strategy.

An evaluation of factors affecting the robustness of colour measurement and its potential to predict the grade of flotation concentrate

Recent developments in machine vision, applied to froth flotation, show the potential for the use of the measurement of froth surface descriptors to provide an improvement to the operation of flotation cells and banks. One such descriptor, froth colour, has been traditionally assumed to relate to the grade of the concentrate recovered. This work sets out to test this assumption, and was performed using a machine vision system, SmartFroth. This system included a camera hood with built in LED lighting, a colour calibration patch and a CIE ‘Lab’ colour space conversion algorithm, which all served to reduce the effect of ambient lighting on the colour measurements. The effectiveness of these techniques was evaluated by comparing colour measurements of dry synthetic ore under various lighting conditions. It was found that the influence of ambient lighting had not been completely eliminated. Batch flotation tests were conducted on a sulphide ore, from the Kennecott Utah Copperton Concentrator, and established that froth colour does not account for entrained gangue, which is barely visible on the froth surface. Thus, froth colour alone cannot be used to accurately predict concentrate grade.

Characterization of Bitumen Properties Using Microscopy and Near Infrared Spectroscopy: Processability of Oxidized or Degraded Ores

Abstract Oxidized or degraded oil sands can exhibit poor processability, which is often not correlated with the fines or clay contents in the ore. Chemical markers (such as low pH and high soluble iron and calcium) for oil sands oxidation are sometimes not present even though significant changes in bitumen properties may have occurred. In these cases, changes in bitumen chemistry have been successfully quantified using microscopic techniques developed at CANMET. More recently, an on-line tool using near infrared (NIR) spectroscopy, which correlates with the CANMET microscopic method, has been developed with Suncor Energy Inc. An on-line technique based on NIR that can quantify the amount of degraded ore coming to the extraction plant from Suncor Energy Inc.'s Steepbank mine will be useful in effectively controlling additions of process aids for treating oxidized or degraded ores. This paper discusses the processability of oxidized or degraded ores along with a microscopic method for identifying oxidized ore and its correlation with the NIR spectroscopic technique. Introduction Oil sands processing efficiency is dependent upon many factors, including the quality of the ore. The operating companies have developed correlations between extraction recovery and bitumen and fines content in the ore. These correlations generally fit observed processability, but often ores are encountered with recoveries that fall well outside these simple relationships. These are variously known as bad ores, problem ores, type X ores, or ores with a high misery factor (the misery due to the loss in recovery or to the "difficult to settle" tailings). Detailed characterization can reveal the reasons for such poor processing behaviour and they can sometimes be traced to unusual water chemistries or unusual clay properties. Often, however, changes in bitumen chemistry can be the source of the problem, and this is the focus of the present discussion. Bitumen oxidation and its negative impact on processability has been extensively studied, mostly on stockpiled or stored samples(1–7). Experience at Suncor Energy Inc.'s Steepbank mine has shown that bitumen changes that may have occurred in geological time frames can also be important in determining processability(8–10). Steepbank Sampling During the commissioning of the Suncor Energy Inc. Steepbank mine, areas of ore were identified that created processing difficulties, in particular high froth densities. The high froth densities resulted in problems in downstream facilities where high solids content in the froth resulted in overloading of the froth treatment centrifuges. These effects were quickly identified with certain areas of the mine and a sampling program was undertaken to identify the causes of the poor processability. The ores linked to the poor processability were generally high-grade ores containing in excess of 12% w/w bitumen (occasionally >14% w/w) with low fines content, that do not fit the normal profile for poorly processing ores. Cryogenic sampling of the froths in the commercialscale separation cell was carried out in order to help identify the cause of the high froth densities. Macroscopic observation of the froths from the problem ores included very large bubble sizes and sometimes a distinct reddish froth colour(9,12).

Characterisation of flotation froth colour and structure by machine vision

It is well known and well recognised that flotation is a process that is complex to monitor and study if a classical approach based on the evaluation of the signals resulting from sensors is adopted. Sensors are usually strategically positioned in the bank cells and detect global process variables such as pH, reagent addition, froth level, on-stream chemical analysis, particle size distribution, etc. In the last ten years several studies have been carried out with the main goal to utilise imaging techniques to detect froth bubbles characteristics and to evaluate the flotation process performance. In this paper an approach of this type is described. More specifically, image processing techniques to automatically measure the colour and the structure of the froth bubbles are presented and the results are discussed. All the investigations are carried out on digital sample images collected in an industrial flotation plant operating in steady-state conditions. The colour analysis is performed on the whole surface of the froth images considering different colour reference systems (RGB, HSV, HSI); the morphological measurements are obtained after the application of selected enhancement and segmentation techniques, necessary to consider the bubbles as separate domains. The multiple correlation analysis performed between froth mineral concentrations (Cu, MgO, Zn and Pb content) and the extracted colour and structure parameters are good in most situations.

Prediction of complex sulfide flotation performances by a combined 3D fractal and colour analysis of the froths

It has long been accepted that froth appearance is a good qualitative indicator of the performance of the flotation process. This paper investigates the possibility of the froth surface 3D reconstruction that can provide significant information about the process efficiency. The froth 3D model was realised performing a preliminary watershed segmentation of the image, a further morphological and morphometrical analysis and finally building a series of “domes” founded on the equivalent ellipses of the identified bubbles. Height and curvature of the resulting ellipsoids were defined analysing light intensity distribution inside each identified domain (bubble). The technique developed generates an elevation model of the surface that is then analysed by a fractal method. The froth surface showed two fractal dimensions that can be used, along with froth colour parameters, to forecast mineral grades of the froths.