Image processing-based monitoring of a batch flotation process

Machine vision technology now offers a viable means of monitoring and controlling flotation performance. In this study an image analysis algorithm utilizing an adaptive marker based watershed transform was developed to segment the froth images and measure the bubble size over a wide range of process conditions. Flotation experiments were conducted at a wide range of operating conditions (i.e. gas flow rate, slurry solids %, frother dosage and collector dosage) and the froth mean bubble size was determined for each run. The results showed that the proposed algorithm can be successfully applied to monitor the flotation process at different conditions.