How Metso improved flotation recovery through AI froth-vision optimization
Computer vision quantifies bubble size, velocity and stability, feeding advanced control. Operators stabilize froth faster and hold targets longer.
Context
Operators relied on manual interpretation of froth appearance, leading to inconsistent flotation control. Variability in human judgment made it difficult to maintain optimal recovery and grade. These limitations reduced process stability and throughput.
Solution
AI-based vision analytics were integrated with advanced control strategies to interpret froth behavior in real time. The system quantifies key froth parameters and feeds them into automated adjustments. This reduces dependency on subjective manual observation.
Results
The site achieved more stable froth conditions, leading to higher recovery and improved grade consistency. Real-time diagnostics also enabled faster responses to changing flotation conditions.
Data Inputs
Froth camera video, process tags (airflow, reagents, levels)