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COLLECTION RATE CONSTANT MODELING IN A FULL- SCALE FLOTATION COLUMN

M. Massinaei, R. Doostmohammadi, A. Aryafara

First published: 2010DOI pendingView metrics

Abstract

In the present study collection rate constant (kc) was modeled as a function of the most significant operating variables which affect gas dispersion in a flotation column (i.e. superficial gas velocity, slurry density (solids\%) and frother dosage/type) using artificial neural network (ANN) and statistical techniques. Experiments were performed in an industrial flotation column (4 m in diameter and 12 m in height) in rougher circuit of a copper concentrator. RTD studies using a radioactive tracer have already confirmed that mixing condition in the existing columns is closed to well-mixed and could be characterized by the large and small tank-in-series model. Accordingly, a kinetic model to calculate kc in industrial flotation columns was proposed. The development of the models and validation using a number of randomly selected datasets is outlined in the present communication.

Publication details

Title
COLLECTION RATE CONSTANT MODELING IN A FULL- SCALE FLOTATION COLUMN
Authors
M. Massinaei, R. Doostmohammadi, A. Aryafara
Proceedings
10th International Multidisciplinary Scientific GeoConference SGEM2010
Publisher
SGEM Scientific GeoConference
Year
2010
Pages
631-638
SWS Citekey
Massinaei2010472
ISSN
1314-2704
ISBN
954-91818-1-2
Language
en
Publication type
Conference Paper
Keywords
Flotation machinesFroth flotationModelingNeural networks
References9

  1. Jameson, G.J., Nam, S., Young, M., 1997. Physical factors affecting recovery rates in flotation. Mineral Science Engineering 9 (3), 103-1 18.

  2. Massinaei, M., Kolahdoozan, M., Noaparast, M., Oliazadeh, M., Sahafipour, M. and Finch, J.A., 2007. Mixing characteristics of industrial columns in rougher circuit. Minerals Engineering, 20,1360-1367.

  3. Yianatos, J., Bucarey, R, Larenas, J., Henriquez, F., Torres, L., 2005. Collection zone kinetic model for industrial flotation columns. Minerals Engineering, Vol. 18, 1373-1377.

  4. Massinaei, M., Kolahdoozan, M., Noaparast, M., Oliazadeh, M., Yianatos, J., Shamsadini, R., Yarahmadi, M., 2008. Hydrodynamic and metallurgical characteristics of industrial and pilot columns in rougher circuit. Minerals Engineering. 22, 96-97.

  5. Finch, J.A. andDobby, G.S., 1990. Column Flotation. Pergamon Press. Oxford.

  6. Massinaei, M., Kolahdoozan, M., Noaparast, M., Oliazadeh, M., Yianatos, J., Shamsadini, R., Yarahmadi, M., 2009. Froth zone characterization of an industrial flotation column in rougher circuit. Minerals Engineering, 22 (3) 272-278.

  7. Haykin, S., 1999. Neural networks: A comprehensive foundation. 2nd edition, Prentice hall, Englewood cliffs, NJ. 842.

  8. Zurada, J.M., 1992. Introduction to artificial neural systems. West Publishing Company. 683.

  9. Bishop, CM., 1996. Neural networks for pattern recognition. Oxford university press. 504.

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