Image Analysis Technique as a Tool for Extracting Features from the Copper Surface Froth in the Flotation Process
American Journal of Chemical Engineering
Volume 1, Issue 4, November 2013, Pages: 70-78
Received: Nov. 24, 2013;
Published: Jan. 10, 2014
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Nasser Saghatoleslami, Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Hajir Karimi, Department of Chemical Engineering, University of Yasuj, Yasuj, Iran
The froth can be adopted as an indicator of the performance of flotation processes. The study of froth image structure would enable us to establish a number of parameters from which could convey the froth characteristics. To monitor the operating performance of the floatation cell by machine vision system, it is crucial to identify and extract those features that are descriptive of the surface froth. Consequently, it can provide interdependency between the froth characteristics with the operating conditions on one hand (e.g., aeration rate, froth depth, chemical compound and pH variation) and the cell parameters performance on the other hand, as well (e.g., copper grade, recovery and solid contents). The aim of the present study is to examine the copper froth characteristics, by adopting an image analysis technique and hence evaluating froth features such as the average bubble size, bubbles distribution, bubble shape features, bubble elongation factor, image average color and the color distribution. Owing to the intricacy aspect of the froth structure and in order to match properly between the real froth and the segmentation images, this algorithm adopts features similar to proper filters in the pre-processing stage, edge detection functions, threshold functions and different mathematical morphology models. The findings of this work reveal that the size and shape of the froth bubbles plays an important role in classifying the froth. Hence, it is possible to incorporate such features for either evaluating the flotation cell performance or adopting it for the automatic on-line control of the flotation process. The findings of this research could also be implemented towards the training of the operators.
Image Analysis Technique as a Tool for Extracting Features from the Copper Surface Froth in the Flotation Process, American Journal of Chemical Engineering.
Vol. 1, No. 4,
2013, pp. 70-78.
N. Saghatoleslami, H. Karimi, R. Rahimi and H.H.A. Shirazi, "Modeling of Texture and Color Froth Characteristics for Evaluation of Flotation Performance in Sarcheshmeh Copper Plant, Using Image Analysis and Neural Networks," Int. J. of Engineering, pp. 1-10, 2002.
F.S. Gouws, C. Aldrich and G.P.J. Schmitz, "The Use of Machine Learning Techniques for the Extraction of Process Knowledge from Industrial Flotation Plants," 26th Proceedings of the Application of Computers and Operations Research in the Mineral Industry, p. 7, 1996.
P. Pelletier, J. Tessier and C. Duchesne, "Application of Numerical Image Analysis to Process Diagnosis and Physical Parameter Measurement in Mineral Processes Part I: Flotation Control Based on Froth Textural Characteristics," Centenary of Flotation Symposium, p. 12, 2005.
S.R. Grano, "Selecting Frothers for the Flotation of Specific Ores by Means of Batch Scale Foaming Tests," Metallurgical Plant Design and Operating Strategies , AUSIMM, 2006.
R.H. Estrada-Ruiz, "Neural Networks to Estimate Bubble Diameter and Bubble Size Distribution of Flotation Froth Surfaces - Synopsis" The Journal of The Southern African Institute of Mining and Metallurgy, vol. 9, p. 9, 2009.
C. Aldrich, "Development of Fuzzy Rule-Based Systems for Industrial Flotation Plants by Use of Inductive Techniques and Genetic Algorithms," JSAIMM, p. 6, 2009.
E.T. Woodburn, J.B. Stockton and D.J. Robbins," Vision-Based Characterization of Three Phase Froths," International Colloquium Developments in Froth Flotation, vol. 1, pp. 1-30, 1989.
G. Bonifazi, P. Massascci, A. Meloni, "3D Froth Modeling by Image Analysis," Proc. XXI Int. Min. Proc. Cong., B8a-178-B8a-189, 2001.
H.H.A. Shirazi, "Application of froth image analysis in mineral industry," PhD Thesis, 1997.
V.A. Glembotski, "Flotation Primary Sources," New York, 1972.
D.W. Moolman, J.J. Ekesteen, C. Aldrich and J.S.J. van Deventer, "The Significance of Flotation Froth Appearance for Machine Vision Control," Int. J. Miner. Process, vol. 48, pp. 135-158, 1996.
J.M. Hargrave, N.J. Miles and S.T. Hall, "The Use of Gray Level Measurement in Predicting Coal Flotation Performance," Mineral Eng., vol. 9, pp. 667-674, 1996.
B.M. Moudgil, "Correlation between Froth Viscosity and Flotation Efficiency," Miner-Metallurg Process, vol. 10, No. 2, pp. 100-101, 1993.