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Journal of the Southern African Institute of Mining and Metallurgy

On-line version ISSN 2411-9717

J. S. Afr. Inst. Min. Metall. vol.109 n.7 Johannesburg Jul. 2009

 

TECHICAL NOTE

 

Neural networks to estimate bubble diameter and bubble size distribution of flotation froth surfaces

 

 

R.H. Estrada-Ruiz; R. Pérez-Garibay

Cinvestav-IPN (Unidad Saltillo), Ramos Arizpe, Coahuila, México

 

 


SYNOPSIS

This work analyses a new approach to estimates bubble size distribution of froth surfaces using artificial neural networks (ANN). Also, the robustness of ANN to interpret images with illumination perturbations, produced by light problems or dirt attached to the window of the video camera is evaluated. The experimental work was carried out in a laboratory flotation column, instrumented with an image acquisition system. The images were processed making use of a perceptron model with a hidden layer, sigmoidal transfer function and unitary bias, and the ANN trained with a back propagation algorithm. The results of validation show that ANN are reliable for learning and producing generalized predictions of the froth mean bubble diameter and bubble size distribution, when the model is trained using a database that contains information on the illumination intensity.

Keywords: image analysis, neural networks, bubble diameter, bubble size distribution


 

 

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