Froth Texture Research Articles

Flotation froth image segmentation using Mask R-CNN

Automatic control of the flotation circuits needs online information from froth indicators, such as froth texture, motion speed, shape, and number and size distribution of bubbles. In principle, these indicators may be extracted with a machine vision system. This paper presents a real-time image analysis system based on Mask R-CNN for flotation froth segmentation and bubble size measurement. The main objectives were detection of bubbles in flotation froth, measurement of the size distribution of the bubbles, and detection of non-loaded bubbles in the froth. Application of the classical image segmentation methods in an industrial copper flotation plant showed considerable errors in bubble identification and sizing. The accuracy of the proposed method in bubble detection and sizing was evaluated using manually segmented images. The proposed method could detect bubbles with an accuracy of 92.82%, which is a considerable improvement to classical image segmentation methods. The proposed system is installed, tested, and verified in an industrial copper flotation process.

DTCWT-based zinc fast roughing working condition identification

Abstract The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

Flotation froth image texture extraction method based on deterministic tourist walks

In the flotation process, the flotation froth texture is an indicator of the flotation state. To recognize the flotation state based on texture features accurately and to provide guidance for production operations, this paper proposes a method for flotation froth image texture extraction based on the deterministic tourist walks algorithm. First, a weighted graph model of a froth image is built using deterministic tourist walks. Next, the degree distribution and the unit intensity distribution of the weighted graph are extracted. The contrast of the node degree and the contrast of the node unit intensity are calculated as the texture feature indexes. The texture feature indexes are used for flotation production state classification and recognition. The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production.

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.

Fault condition recognition based on multi-scale co-occurrence matrix for copper flotation process

Image processing technology has been successfully applied to fault detection of copper flotation processes, and the key to realize image processing based fault condition recognition is accurately extracting froth image features closely related to key production indices. To extract texture features of froth images in real-time, a multi-scale gray level co-occurrence matrix (M-GLCM) method is proposed. Firstly, the wavelet transform is applied to the froth gray images, and the coefficients of wavelet approximation sub-images in different scales are mapped into gray level of 0~255. Then the spatial gray level co-occurrence matrices are calculated, a set of texture features of froth images are obtained statistically from the matrices. Lastly, these features are adopted for off-line classification and on-line recognition of froth images in different working conditions. As the result shows, froth texture features extracted through the multi-scale gray level co-occurrence matrix have a good stability and separability suitable for mode classification and thus can be well applied for fault condition recognition in copper flotation processes.

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.

Color co-occurrence matrix based froth image texture extraction for mineral flotation

It is well accepted that the surface texture appearance of the flotation froth involves crucial information about its separation process, which can be used as an effective criterion for the qualitative assessment of the flotation performance. To obtain the distinctive characteristic of the froth surface appearance under various production conditions, a texture feature extraction method based on color co-occurrence matrix (CCM) is presented compared to the commonly used gray level co-occurrence matrix (GLCM). First, the HIS (Hue, Saturation and Intensity) color space is employed to exhibit and quantify the froth image, which yields a more intuitive description of the color properties in comparison with the RGB (Red, Green and Blue) color space. Then, the CCM is computed and the corresponding feature statistics of the froth surface texture are extracted based on the proposed matrix. Next, a new feature parameter is defined and extracted to describe the froth texture complexity based on the aforementioned texture feature statistics. After adequate offline froth images have been obtained from a bauxite flotation plant located in China under various production statuses with the corresponding concentrate grade in the froth assayed manually, the qualitative relationship between the texture complexity and the corresponding concentrate grade is investigated. Consequently, the optimal texture complexity range to achieve satisfactory production index is obtained for the further research of the optimal control of the flotation process. Experimental results have verified the effectiveness of the method and demonstrated its superiority over the previous texture feature extraction methods based on GLCM.

Performance Recognition for Sulphur Flotation Process Based on Froth Texture Unit Distribution

As an important indicator of flotation performance, froth texture is believed to be related to operational condition in sulphur flotation process. A novel fault detection method based on froth texture unit distribution (TUD) is proposed to recognize the fault condition of sulphur flotation in real time. The froth texture unit number is calculated based on texture spectrum, and the probability density function (PDF) of froth texture unit number is defined as texture unit distribution, which can describe the actual textual feature more accurately than the grey level dependence matrix approach. As the type of the froth TUD is unknown, a nonparametric kernel estimation method based on the fixed kernel basis is proposed, which can overcome the difficulty when comparing different TUDs under various conditions is impossible using the traditional varying kernel basis. Through transforming nonparametric description into dynamic kernel weight vectors, a principle component analysis (PCA) model is established to reduce the dimensionality of the vectors. Then a threshold criterion determined by theTQstatistic based on the PCA model is proposed to realize the performance recognition. The industrial application results show that the accurate performance recognition of froth flotation can be achieved by using the proposed method.

The Relationship between Froth Image Features and Platinum Flotation Grade

Abstract Many endeavors have been made attempting to relate froth appearance to key performance indicators such as grade and recovery. The platinum industry have questioned the possibility of doing this based on the results of simple models trying to relate physical features (e.g. bubble size) to grade. In this study a fractional factorial design of laboratory experiments were conducted using platinum ore. The flowrate (x 1 ), pulp level (x 2 ), collector (x 3 ), activator (x 4 ), frother (x 5 ) and depressant (x 6 ) were varied for each experiment and videos were recorded of the froth. The analysis of the videos rendered 20 texture features from each image. The non-linear relationship between the froth texture features and grade were investigated using kernel Fisher Discriminant Analysis (kFDA). The results indicate that these features could be sufficient as input into advanced model based control systems for PGM flotation plants. Multivariate linear regression confirmed the hypothesis and performed very well when the kFDA features are used as predictors.

Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes—Part I: Flotation control based on froth textural characteristics

This paper summarizes research into the study of the control of flotation processes based on froth images. A review of the froth flotation operation background and the relevance and role of the froth phase are presented. The use of numerical image analysis techniques for the extraction of significant patterns from industrial flotation froths is discussed. First, the froth was analysed by the use of the Multivariate Image Analysis (MIA) method to extract the spectral variation contained in a RBG image. The Partial Least Squares (PLS) method was applied to develop an empirical model for the prediction of froth grade. The results of this application are illustrated using an industrial case study. Second, the froth was also analysed by Grey-Level Co-occurrence Matrix (GLCM) and Wavelet Transform Analysis (WTA) to extract features related more to morphological aspects. Since there is useful information hidden in froth texture, these methods are used as texture descriptor to classify different types of froths. They also provide qualitative information on the changes in the visual appearance of the froth. The pros and the cons of those methods are highlighted. Third, a control strategy based on a froth structure indicator was developed and implemented on an industrial zinc flotation circuit. The feedback controller uses the reagent flow-rate in order to maintain the froth structure at its set point. Process improvements obtained from the implemented control strategy are highlighted. Finally, the feasibility of implementing a hybrid control strategy consisting in a froth structure supervision controller based on the froth concentrate grade estimated by MIA and recovery inferred by image analysis is discussed.

The interpretation of flotation froth surfaces by using digital image analysis and neural networks

The rapid developments in computer vision, computational resources and artificial intelligence, and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In chemical and minerals engineering, numerous opportunities for the application of these systems exist, of which the characterization of flotation froth structures is a good example of the utilization of visual data as a supplement to conventional plant data. In this paper images from pyrite batch flotation tests conducted after a factorial design as well as images from a copper flotation plant were used to understand the relationship between froth characteristics and flotation performance better. The results show that a significant amount of data can be extracted from flotation surface froths. Techniques have been developed to characterize chromatic information, average bubble size, froth texture, froth stability and mobility of surface froths. It has been shown that most of the froth characteristics of this study can be explained in terms of the concentration of solids in the froth and the factors that affect the solids concentration. The techniques developed proved to be useful in investigating the effect of a mixed collector and the addition of copper sulphate. The depressing effect of the copper sulphate and the higher grades and recoveries made possible by the mixed collector under these conditions were explained by analysis of the froth features. Excellent results were obtained in modelling the relation between froth characteristics or froth grade and recovery by using a backpropagation neural network. A sensitivity analysis showed that the most important froth features for the experimental conditions of this study are the froth stability, mobility and average bubble size. This computer vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various flotation parameters. This paper also shows how the rapid development in computer technology and related disciplines can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems.

On-Line Image Analysis to Improve Industrial Flotation Plant Performance

In this study image features extracted from froth images by an on-line machine vision system in an industrial platinum flotation plant were used to relate froth characteristics and flotation performance by using neural networks. It has been shown that a considerable amount of data can be extracted from flotation surface froths and that both novel feedback control procedures and feedback control as a complement to conventional feedforward systems are made possible. Feature measures such as chromatic information, average bubble size, froth texture, froth stability and mobility of surface froths were used in the on-line classification of flotation froths. This intelligent vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various flotation parameters. This paper shows how the rapid development in computer technology and neural networks can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems.