A Convolutional Neural Network for Classification of Froth Mobility in an Industrial Flotation Cell

ABSTRACT

Froth flotation is widely used in the resource industry as a particle separation process. The performance of the flotation process is significantly affected by the mobility of the froth phase. Despite its importance, little work has been done to develop a simple and robust method for indicating froth mobility. In the present study, a simple method to monitor froth mobility was developed using a readily available web-camera to take images and a convolutional neural network (CNN) model classifying the images mainly based on the degree of motion blur. The CNN model was trained with a newly built froth image dataset, comprising froth images taken near the overflowing lip of an industrial flotation cell at a wide range of operating conditions using the web-camera. It was found that the trained model could correctly classify 98% of the froth images into one of three categories: low, medium, and high mobility. The froth mobility determined by the trained CNN model was in good agreement with the one analyzed with a commercial software. A potential application of the present method for indicating flotation performance was illustrated.

KEYWORDS:

• Convolutional neural network
• froth phase
• process monitoring
• flotation
• image classification

Acknowledgments

HP acknowledges the Queensland Government for an Industry Research Fellowship.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website

Additional information

Funding

This work was supported by The Australian Coal Industry’s Research Program (ACARP C26012).