Off-line image analysis for froth flotation of coal
Introduction
Studies show that the performance of froth flotation is affected by the froth structure, and has a strong relationship with visual information of the froth surface (Pryor, 1965, Glembotskii, 1972; Cutting, Barber, & Newton, 1986; McKee, 1990). The determination of the froth structure, therefore, plays a vital role for controlling the process (Sadr-Kazemi & Cilliers, 1997; Banford, Aktas, & Woodburn, 1998). Some progresses have been made in determining metallurgical parameters that influence surface froth appearance, and in image analysis of flotation froths (Banford, 1996, Holtham & Nguyen, 2002). However, looking at even the recent reports, there seems to be difficulties, such as computational complexity and incorrect edge detection, to overcome. This work intends to develop a pixel tracing technique for off-line image analysis to determine the size distribution of froth. Although image analysis has been previously reported as a simple and robust means of determining froth structure (Biland, 1987, Wiklund & Granlund, 1987), the present work provides some improvements in this perspective.
Section snippets
Image analysis technique
Images were recorded by a camera over the flotation cell, where Zonguldak bituminous coal was used. Each image as shown in exemplary Fig. 1 is, then considered to be a matrix of dimensions m×n. Each element or pixel of this matrix is the brightness intensity scaled from 0 to 255 (8 bits/pixel).
On high solids concentrations, the froth structure is sticky and the bubbles have large elliptical shapes (Moolman, Eksteen, Aldrich, & Van Deventer, 1996). To simplify the algorithm, froth shape is
Experimental
A Turkish bituminous coal from Zonguldak colliery was chosen for the investigation. The coal was first crushed to less than 5 mm diameter in a crusher. A ball mill was provided to achieve further particle size reduction. The milled sample was sieved and particles of less than 53 μm (for experiment 1) were collected and stored in sealed plastic bags. Some of the fraction of the feed coal (−53 μm) was re-milled in order to clarify the effects of finer particle size on froth structure, froth
Results and discussion
The tests were performed in the presence of Triton X-100 or its mixtures with MIBC at various ratios. A number of photographs taken at various time intervals during the experiment 1 (initial reagent loading: 0.9 mg/g coal) are shown in Fig. 9. As clearly shown from the images given in Fig. 9, the mean bubble diameters were smaller at the start of the experiments. However, the mean bubble diameter increased gradually towards the end of the tests. The results obtained from three tests were used in
Conclusion
The suggested image analysis method is coded and implemented in C language with a user interface, and consequently applied on the images taken in different times of froth flotation tests. The mean bubble diameters are calculated for each image. The cumulative grade, namely the purity of the final product is found experimentally for different times of the froth flotation. The relationship between the mean bubble diameter and the cumulative grade is represented by the fitted functions, hence for
References (12)
- et al.
Interpretation of the effect of froth structure on the performance of froth flotation using image analysis
Powder Technology
(1998) - et al.
On-line analysis of froth surface in coal and mineral flotation using JKFrothCam
International Journal of Mineral Processing
(2002) - et al.
The significance of flotation froth appearance for machine vision control
International Journal of Mineral Processing
(1996) - et al.
An image processing algorithm for measurement of flotation bubble size and shape distributions
Minerals Engineering
(1997) Some observations on the utility of separation measures in coal flotation tests
Chemical Engineering Journal
(1983)- Banford, A. W. (1996). The use of off-line image analysis in assessing the effect of various reagent addition...
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