Machine vision based monitoring of an industrial flotation cell in an iron flotation plant

• A machine vision system was designed to monitor an industrial flotation cell.
• Algorithms were proposed to measure froth features at different conditions.
• Mean bubble size, number of bubbles, froth velocity and stability were extracted from froth images.
• Correlation between image variables and process conditions as well as performance was explored.

Froth flotation is the most commonly used technique for the separation of valuable from gangue minerals. Unforeseeable changes in the ore characteristics and operating conditions have necessitated continuous control of the flotation circuits. Experienced operators usually control the process performance through the froth visual features. Machine vision technology now offers a viable means of monitoring and control of the flotation circuits. In the current communication, a machine vision system was installed on a flotation cell in the rougher circuit of an iron flotation plant to monitor the process at different conditions. Bubble size distribution, number of bubbles, froth velocity and stability were the main visual features extracted from the froth images. The results indicate that the developed system is capable of accurately monitoring the process behavior at different conditions.

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