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

Abstract

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.