Method of complex copper–zinc ore typification using neural network models

The article discusses variability of feed stock of a processing plant. Currently it is essential to classify ore meant for treatment. Heterogeneity of the processing feed stock affects the process of beneficiation. It is found that classical regression models fail to provide the wanted reliability of cross impact in pairs of factors due to the multi-factor and nonlinear nature of the flotation object. The geological and mineralogical features of test ore are analyzed using neural network modeling. The test subject was copper–zinc ore. The multi-factor nature of the test ore was analyzed on a model developed using a Kohonen neural network. The neural network model revealed six basic clusters. Each class features similar values of yield. Using the obtained data, the Kohonen self-organizing maps were constructed for the visual interpretation of the results. The technological performance dependence on the type of feed batch is demonstrated. It is required to develop a high-automated flotation technology to ensure enhanced recovery of metals.

Keywords: neural networks, metal recovery, flotation, modeling, raw material typification.
For citation:

Aleksandrova T. N., Ushakov E. K., Orlova A. V. Method of complex copper–zinc ore typification using neural network models. MIAB. Mining Inf. Anal. Bull. 2020;(5):140-147. [In Russ]. DOI: 10.25018/0236-1493-2020-5-0-140-147.

Acknowledgements:

This work was supported by the Russian Science Foundation (project no. 19-17-00096).

Issue number: 5
Year: 2020
Page number: 140-147
ISBN: 0236-1493
UDK: 622.765-52
DOI: 10.25018/0236-1493-2020-5-0-140-147
Article receipt date: 02.02.2020
Date of review receipt: 28.02.2020
Date of the editorial board′s decision on the article′s publishing: 20.04.2020
About authors:

T.N. Aleksandrova1, Dr. Sci. (Eng.), Professor, Head of Chair,
E.K. Ushakov1, Graduate Student,
A.V. Orlova1, Graduate Student,
1 Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

For contacts:

E.K. Ushakov, e-mail: s195065@stud.spmi.ru.

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