Froth Characteristics Research Articles

A phenomenological model of entrainment and froth recovery for interpreting laboratory flotation kinetics tests

There is currently no standard approach to laboratory flotation testing and interpretation. The deficiency stems, in part, from the difficulties in quantifying the recovery of mineral particles by hydraulic entrainment (Re) and the recovery of collected mineral particles across the froth zone (Rf) of a laboratory flotation cell. For lack of alternatives, most practitioners ignore entrainment (Re = 0) and assume that froth recovery is 100% (Rf = 1). Consequently, differences in test scraping frequency or froth characteristics can lead to large differences in the inferred collection kinetics.This paper proposes resolving the problem by incorporating a phenomenological solution for Re and Rf. By considering the test water balance, incorporating hindered settling theory, and making some simple assumptions regarding the pulp, interface, and froth phases, it can be shown that the entrainment recovery of any mineral can be calculated solely from the particle size and specific gravity. A similar approach can be used to estimate the froth recovery as a function of time.The method is applied to a data set of flotation tests conducted with different scraping rates, and it is demonstrated that it yields similar flotation rate constants regardless of the scraping rate.

Sulfur Flotation Performance Recognition Based on Hierarchical Classification of Local Dynamic and Static Froth Features

This paper proposes a flotation performance recognition system based on a hierarchical classification of froth images using both local dynamic and static features, which includes a series of functions in image extraction, processing, and classification. Within the integrated system, to identify the abnormal working condition with poor flotation performance (NB it could be significantly different with the dynamic features of the froth in abnormal working condition), it is functioned first with building up local dynamic features of froth image from the information including froth velocity, disorder degree, and burst rate. To enhance the dynamic feature extraction and matching, this system introduces a scale-invariant feature transform method to cope with froth motion and the noise induced by dust and illumination. For the performance subdividing under normal working conditions, bag-of-words (BoW) description is utilized to fill the semantic gap in performance recognition when images are directly described by global image features. Accordingly typical froth status words are extracted to form a froth status glossary so that the froth status words of each patch form the BoW description of an image. A Bayesian probabilistic model is built to establish a froth image classification reference with the BoW description of images as the input. An expectation-maximization algorithm is used for training the model parameters. Data obtained from a real plant are selected to verify the proposed approach. It is noted that the proposed system can reduce the negative effects of image noise, and has high accuracy in flotation performance recognition.

Development of a machine vision system for real-time monitoring and control of batch flotation process

Substantial progresses have been made over the past decade in using machine vision for automatic control of the froth flotation process. A machine vision system is able to extract the visual features from the captured froth images and present the results to process control systems. The current research work is concerned with the development and implementation of a machine vision system for real time monitoring and control of a batch flotation system. The proposed model-based control system comprises two in-series models connecting the process variables to the froth features and the metallurgical parameters along with a stabilizing fuzzy controller. The results indicate the developed machine vision based control system is able to accurately predict the metallurgical parameters of the existing batch flotation system from the extracted froth features and efficiently maintain them at their set-points despite step disturbances in the process variables. Furthermore, the proposed control system leads to higher target values for the metallurgical parameters than the previously developed system (RCu=91.1%;GCu=11.2% vs. RCu=87.6%;GCu=8.1%).

Performance of Different Classes of Organic Compounds as Frother in De-ashing of Indian Coking Coal by Froth Flotation

Flotation studies were carried out on a high ash coking coal sample using different classes of organic compounds as frothers. The organic compounds used as frothers in the present study were the following: (a) Frothers from natural sources (cyclic alcohol/phenols), (b) Synthetic frothers: Aliphatic alcohols, Polyglycols, Alkoxy alkane and Cyclic ethers. The time-recovery flotation data were analysed using a modified first order kinetic model. The performances of different frothers were evaluated using the parameters such as yield, ash, recovery of combustible, rate constant and selectivity. It was observed that synthetic frother, in particular poly ethylene glycol (molecular weight 400) showed overall better performance against others. The mechanism of performance of typical frothers was studied. The improved performance of poly-ethylene glycol was attributed to the higher surface activity and the enhanced frothing characteristics.

Flotation of Chalcopyrite and Molybdenite in the Presence of Organics in Water

One of the water constituents that has not been investigated in great detail for potential detrimental effect on mineral flotation is organic matter. This study investigates the effect of natural organic materials contained in water, such as humic, fulvic and tannic acids, on the flotation of copper and molybdenum sulphides in alkaline conditions and in concentrations similar to those found in natural waters. Results show that copper and molybdenum grades decreased with the addition of humic, tannic and fulvic acid in that order, with a larger depression of molybdenite grade and recovery. Adsorption studies using ultraviolet (UV)-visible spectroscopy and X-ray photoelectron spectrometer (XPS) surface analysis confirmed that these organic materials were adsorbed on the minerals surface. Complimentary analyses of froth characteristics, particle size distribution and fine particles entrainment were also conducted to explain the cause of the negative effect of these organic materials on flotation. The flotation results were explained in terms of the decrease in the hydrophobicity of the mineral surfaces due to the adsorption of hydrophilic groups in these organic materials which then prevent bubble-particle adhesion. The larger detrimental effect of humic acid is due to its higher adsorption on the minerals, high molecular weight and carbon content compared with the other organic acids used.

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.

Investigating the effects of particle shape on chromite entrainment at a platinum concentrator

In flotation, entrainment is undesirable as it results in the recovery of hydrophilic gangue particles which reduces the concentrate grade. For UG2 ore, a South African platinum group mineral (PGM) ore, entrainment is particularly problematic because it leads to the recovery of chromite in the final concentrate which can cause severe problems in the smelter. It is therefore important to understand all factors affecting entrainment. These factors include froth characteristics, as well as particle size distribution and density, which have been studied widely. Theoretically, they should also include shape, as shape affects drag coefficients of particles and thus hindered settling rates of particles within plateau borders. In this study, detailed mineral-specific shape characterisation with Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) was used to assess the effect of particle shape on chromite entrainment during flotation of UG2 ore at a South African platinum concentrator. This plant-scale study suggests that shape does affect entrainment, with more rounded particles showing higher entrainment than angular, elongated particles.

Features Extraction of Flotation Froth Based on Equivalent Binocular Stereo vision

Abstract: It is widely known that the froth features are closely related to the flotation performances. However, the conventional froth features extracted by two-dimensional image processing methods lose the depth information, so it is difficult for these features to reflect the actual stereo perception of froth appearance as human eyes. In this work, a features extraction method based on the equivalent binocular stereo vision with a single camera is proposed for the flotation froth images. At first, the differences between two adjacent frames by a single camera, caused by the flowing of froth, are used to emulating the binocular parallax in the imaging process. Then, the depth of each bubble center from a froth image is obtained by calculating the equivalent baseline based on macro-block matching. At last, the froth features with depth information are extracted. Experimental results show that the froth features extracted by proposed method have better stability and separability than the conventional features.

Froth-based modeling and control of a batch flotation process

Automatic control of the flotation process is a difficult task due to the large number of variables involved, significant disturbances, and the process's complex nature. Previous research has established that flotation performance is reflected in the structure of the froth's surface. This paper describes the application of machine vision and fuzzy logic in controlling a batch-flotation cell. To perform this process, a laboratory flotation cell was operated under different conditions while process and image data were simultaneously recorded. Then, correlations between the resultant froth features and process variables were modeled, and an interpretable froth model was created. A fuzzy controller was designed and implemented to control process performance through the extracted froth features at the desired level by manipulating the selected process variables. The results indicate that the developed control system is able to handle process disturbances and track reference signals.

Online hybrid modeling method with application for predicting Bauxite production indicators

In the bauxite flotation process, concentrate grade and tailings grade are key production indicators; however, they are difficult to measure online. It is also difficult to develop an effective mathematical model for the process because of the complex non-linear and uncertain relationship among the feed parameters (feed grade, pulp density, slurry particle size, etc.), froth features and production indicators. Therefore, an online hybrid modeling method is proposed by analyzing the multiple parameters that affect the production indicators. First, according to the correlation and redundancy in the feed and froth feature parameters, the kernel principle component analysis (KPCA) is used to reduce the number of the parameters. Then, a neutral network model of the regular extreme learning machine (RELM), which is based on wavelet function, is presented to predict these two indicators. To improve generalization capability and prediction accuracy, information entropy is used to distribute the weight of the two models based on their predicting error. At last, an on-line updating strategy of the hybrid model is constructed in order to investigate the influence of the working conditions. The proposed method is tested on the diasporic-bauxite flotation process and shows high predictive accuracy and generalization capability. It lays the foundation for optimal control of the operation parameters based on mineral grade in the flotation process.

Complex networks-based texture extraction and classification method for mineral flotation froth images

With recent improvements in instrumentation and computer infrastructure, machine vision technologies have produced innovative approaches for controlling and monitoring mineral flotation process. In order to efficiently analyze froth image, froth texture is used to accurately and rapidly extract froth characteristics based on the statistics of image pixels. The characterization and identification of texture require a method that can express the context surrounding each pixel by combining local and global texture characteristics. To extract the distinctive froth texture features in different production states, a novel complex networks-based texture extraction and classification method for froth imaging is proposed in this paper. A network model is constructed by expressing pixels as network nodes and similarities between pixels as network links. This method automatically sets the optimal algorithm parameters for the complex network modeling of the froth images according to bubble sizes by using the Minkowski distance. Energy and entropy measurements are used to quantify the properties of the connectivity and topology of the froth-image network model. Copper froth images at different production states extracted from the flotation monitoring system in a flotation plant are used to test the froth-image network model. The experimental results show that the proposed method accurately describes the froth image texture and also robustly classifies the different production states.

Estimation of metallurgical parameters of flotation process from froth visual features

The estimation of metallurgical parameters of flotation process from froth visual features is the ultimate goal of a machine vision based control system. In this study, a batch flotation system was operated under different process conditions and metallurgical parameters and froth image data were determined simultaneously. Algorithms have been developed for measuring textural and physical froth features from the captured images. The correlation between the froth features and metallurgical parameters was successfully modeled, using artificial neural networks. It has been shown that the performance parameters of flotation process can be accurately estimated from the extracted image features, which is of great importance for developing automatic control systems.

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.

Modeling the Relationship between Froth Bubble Size and Flotation Performance Using Image Analysis and Neural Networks

Machine vision technology now offers a viable means of monitoring and control of froth flotation systems. In this study the relationship between process conditions and the surface bubble size as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled by neural networks. Flotation experiments are conducted at a wide range of process conditions (i.e., gas flow rate, slurry solids %, frother/collector dosage, and pH) and the froth mean bubble size along with the metallurgical parameters are determined for each run. An adaptive marker based watershed algorithm is successfully developed for segmentation of the froth images and measurement of the bubble size at different conditions. The results show that there is a strong correlation between process conditions and the froth mean bubble size, which is of great importance for control purposes. Even though the metallurgical parameters can be estimated from the froth mean bubble size alone, other froth features (i.e., froth velocity, color, and stability) are required to be measured in order to achieve more accurate predictions of the process performance.

An intelligent optimal setting approach based on froth features for level of flotation cells

In the flotation production process, the liquid level of flotation cells is usually set by on experiences. The liquid level can fluctuate in a large range such that the concentrate grade and tailings grade may not meet the requirement.In this paper, an intelligent optimal setting approach based on forth image features is proposed. On the basis of analysis of flotation cells working principle and relationship between level and froth image features, the pre-setting model based on CBR, the improved least squares support vector machine(LS-SVM) grade prediction model based on multiple froth features, and the self-learning fuzzy reasoning intelligent compensation model based on BP neural network are integrated together. This method is tested on a bauxite flotation process. The level fluctuation of rougher flotation cells decreases.The pass rates of concentrate and tailings grade in rougher flotation and the pass rate of the concentrate grade and recovery of the overall flotation increase.

Image Analysis Technique as a Tool for Extracting Features from the Copper Surface Froth in the Flotation Process

The froth can be adopted as an indicator of the performance of flotation processes. The study of froth image structure would enable us to establish a number of parameters from which could convey the froth characteristics. To monitor the operating performance of the floatation cell by machine vision system, it is crucial to identify and extract those features that are descriptive of the surface froth. Consequently, it can provide interdependency between the froth characteristics with the operating conditions on one hand (e.g., aeration rate, froth depth, chemical compound and pH variation) and the cell parameters performance on the other hand, as well (e.g., copper grade, recovery and solid contents). The aim of the present study is to examine the copper froth characteristics, by adopting an image analysis technique and hence evaluating froth features such as the average bubble size, bubbles distribution, bubble shape features, bubble elongation factor, image average color and the color distribution. Owing to the intricacy aspect of the froth structure and in order to match properly between the real froth and the segmentation images, this algorithm adopts features similar to proper filters in the pre-processing stage, edge detection functions, threshold functions and different mathematical morphology models. The findings of this work reveal that the size and shape of the froth bubbles plays an important role in classifying the froth. Hence, it is possible to incorporate such features for either evaluating the flotation cell performance or adopting it for the automatic on-line control of the flotation process. The findings of this research could also be implemented towards the training of the operators.

Relationship between Surface Froth Features and Flotation Indexes in the Flotation of a Sulphide Copper Ore

Froth images are pre-processed, which are acquired at the flotation laboratory. Digital image analysis techniques are used to analysize these froth images and their grey histogram and to extract statistical texture features of those froth images. Finally, the relation model for statistical texture features of those froth images and flotation index is established by RBF neural networks. A simulation showed that the relation model is higher precise

The use of rhamnolipid biosurfactants as a frothing agent and a sample copper ore response

The flotation performance of a copper ore sample in the presence of a rhamnolipid biosurfactant produced by Pseudomonas aeruginosa MA01 strain was studied. The rhamnolipid product was first investigated with respect to surface tension and frothing characteristics. Rhamnolipid showed a higher surface activity and frothability compared to DF-250 and MIBC frothers. Copper and molybdenum grades and recoveries were found to decrease as the biosurfactant concentration increased. In contrast, iron flotation was fairly improved in the presence of rhamnolipid. Results indicated that the use of rhamnolipid significantly increased the flotation rate of all minerals. As predicted by hydrophile–lipophile balance number/molecular weight diagram, rhamnolipid as a powerful surfactant increased the recovery of coarser particles to the concentrates. Although the use of rhamnolipids in copper ore flotation was not pleasing but the improved performance of iron minerals flotation may lead to its possible application in the dense mineral flotation such as lead and zinc.

Sphalerite Flotation Using an Arylhydroxamic Acid Collector: Improving Grade while Using a Reduced Amount of Copper Sulfate for Activation

N-Hydrocinnamoyl-N-phenylhydroxylamine (HCNPHA) was determined to float sphalerite without copper sulfate activation. However, the concomitant flotation of sulfidic (pyrite) and nonsulfidic gangue (silica) minerals significantly reduced the grade of the float concentrate. The addition of only 200 g/t copper sulfate, which is ∼20% of that which is being used currently in the xanthate reagent scheme, improved the grade and the recovery. Seven different types of carboxymethylcellulose (CMC-1−CMC-7) were tested. CMC-1 was found to be the best as a depressant of gangue flotation. Copper sulfate appeared to differentiate the sphalerite surface well and facilitated chelation while the CMC changed the frothing characteristics of the slurry and prevented entrainment of gangue into float concentrate (froth). Studies on flotation kinetics also confirmed the improved selectivity attained by using copper sulfate and a CMC. The selectivity index (SI) for sphalerite, with respect to pyrite, improved from 0.88 to 2.35 wh...

Visual information model based predictor for froth speed control in flotation process

Image processing sensors are emerging as an important measurement option in mineral processing, mainly due to their non-intrusive characteristics. Their principal application areas have been the determination of ore size distributions in grinding and froth features in flotation. The incorporation of visual information in control loops is the logical step. However, the excessive processing required brings a new problem that must be solve: to count with a strategy able to provide a measured value for each visual sensor in the plant. A first approach is to assign one computer to each sensor yielding a distributed architecture, but this means the implementation of a huge computer network. A more efficient alternative is alternated sampling, but the succeed of this option is limited to the existence of virtual sensors capable of give accurate values that must be use during the unsampled period. In this paper we begin by reviewing classical image processing algorithms used in flotation froth feature extraction. Then a new method is introduced for the characterization and recognition of visual information using dynamic texture techniques. Finally we developed a dynamic texture based virtual sensor for the prediction of froth speed in the unsampled period, tested with industrial data.