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

Abstract

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.