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

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

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