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

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%)

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

An image segmentation algorithm for measurement of flotation froth bubble size distributions

The bubble size distribution at the froth surface of a flotation cell is closely related to the process condition and performance. The flotation performance can be reasonably predicted through continuous measuring the bubble size distribution by a machine vision system. In this work a new watershed algorithm based on whole and sub-image classification techniques is introduced and successfully validated by several laboratory and industrial scale froth images taken under different process conditions. The results indicate that the developed algorithms, in particular the sub-image classification based segmentation algorithm, can accurately and reliably identify the individual small and large bubbles in the actual froth images, which is often problematic

Development of a new algorithm for segmentation of flotation froth images

It is well known that froth visual features reflect the operating conditions of the flotation process, so that being able to accurately obtain the froth properties is the most significant criteria to optimize and control this process. Froth segmentation is a useful procedure that can determine the bubble size distribution. Several algorithms have been proposed in this field, but marker-based watershed transform shows the best performance. In spite of this fact, the algorithm suffers from oversegmentation in cases when the flotation froth includes large bubbles along with small ones. In the paper, the marker-based watershed method is modified to prevent oversegmentation of large bubbles. The developed algorithm is validated using some froth images captured at different operating conditions, so the results indicate that the method can segment the mixture of big and small bubbles effectively

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