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Machine vision based monitoring of an industrial flotation cell in an iron flotation plant
International Journal of Mineral Processing 133 (2014) 60–66
Contents lists available at ScienceDirect
International Journal of Mineral Processing journal homepage: www.elsevier.com/locate/ijminpro
Machine vision based monitoring of an industrial flotation cell in an iron flotation plant A. Mehrabi a,b, N. Mehrshad a, M. Massinaei c,⁎ a b c
Electrical Engineering Department, University of Birjand, Birjand, Iran Senior Automation & Control Engineer, Chadormalu Mining & Industrial Company, Yazd, Iran Mining Engineering Department, University of Birjand, Birjand, Iran
a r t i c l e
i n f o
Article history: Received 11 April 2014 Received in revised form 25 July 2014 Accepted 30 September 2014 Available online 8 October 2014 Keywords: Froth flotation Image analysis Machine vision Process control
a b s t r a c t Froth flotation is the most commonly used technique for the separation of valuable from gangue minerals. Unforeseeable changes in the ore characteristics and operating conditions have necessitated continuous control of the flotation circuits. Experienced operators usually control the process performance through the froth visual features. Machine vision technology now offers a viable means of monitoring and control of the flotation circuits. In the current communication, a machine vision system was installed on a flotation cell in the rougher circuit of an iron flotation plant to monitor the process at different conditions. Bubble size distribution, number of bubbles, froth velocity and stability were the main visual features extracted from the froth images. The results indicate that the developed system is capable of accurately monitoring the process behavior at different conditions. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Flotation is a physico-chemical separation process that utilizes the difference in surface properties of the valuable minerals and the unwanted gangue minerals (Wills and Napier-Munn, 2006). In a flotation cell, already conditioned fine particles with reagents (i.e. collectors and frothers) are subjected to air bubbles and the valuable (hydrophobic) particles attach to air bubbles and rise to the froth layer for recovery, while the unwanted (hydrophilic) particles remain at the cell bottom. Modeling and simulation of a flotation process is a difficult task owing to several variables and interactions involved (King, 2001). Flotation circuits are subjected to a wide range of process disturbances, some of which are caused by the change in mineral characteristics and others to variation in operating conditions. In most flotation plants, process operators monitor the froth surface visually and make adjustments to process parameters. However, due to natural complexity of the flotation process, achieving optimal control is often not possible for the human operators. The need to overcome these problems, coupled with rapid advances in computer technology has led to the development of machine vision systems for monitoring and control of flotation circuits (Moolman et al., 1995; 1996a,b; Holtham and Nguyen, 2002; Kaartinen et al., 2006; Vanegas and Holtham, 2008; Aldrich et al., 2010; Morar et al., 2012). To date, several attempts have been made to develop effective
⁎ Corresponding author. Tel.: +98 561 2502133. E-mail address: [email protected] (M. Massinaei).
http://dx.doi.org/10.1016/j.minpro.2014.09.018 0301-7516/© 2014 Elsevier B.V. All rights reserved.
algorithms for measuring the froth visual properties. A brief description of previous studies will be reviewed in the relevant later sections. In this research work, a machine vision system was developed to characterize the process behavior at different operating conditions. Data acquisition and analysis was carried out on the first flotation cell in the rougher circuit of an iron flotation plant. 2. Chadormalu flotation circuit and video camera set-up Test work was conducted at the Chadormalu (the largest iron deposit in central Iran) iron flotation plant in Iran. In this plant around 2000 t/h ore containing approximately 55% Fe and 1% P (with magnetite, hematite and apatite as the valuable minerals) is treated to produce iron (with 67% Fe and 0.045% P2O5) and apatite (with 33% P2O5 and b 3% iron) concentrates. The comminution is accomplished in a gyratory crusher followed by five parallel SAG mills. The ground slurry is subjected to several stages of low/high intensity magnetic separation followed by froth flotation to produce iron and apatite concentrates. A simplified flowsheet of the beneficiation process is shown in Fig. 1. Sodium carbonate (for pH adjustment), sodium silicate (as depressant of iron oxides) and Berol/Asam combination (as collector/frother) are the flotation reagents which are added to the conditioning tank of the hematite flotation circuit. A video camera was positioned at a distance of 110 cm of the froth surface of the first rougher cell as shown in Fig. 1. The video camera was mounted on a specially made bracket with an adjustable arm allowing lateral and vertical adjustments (see Fig. 2). The zoom and focus of the camera were set-up so that an area of approximately 300 × 400 mm was subjected to filming. Lighting
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
SAG Mill
Low-intensity magnetic separation
61
Magnetite concentrate Hematite concentrate
Camera High-intensity magnetic separation
Apatite flotation
Apatite concentrate
Tailing
Fig. 1. Simplified flowsheet of Chadormalo beneficiation process.
4. Extracted visual features Bubble size distribution, number of bubbles, froth velocity and stability were the main visual features extracted from the captured froth images. More details of the methodology employed to quantify the froth features along with the result implementation are given in the following sections. 4.1. Bubble size distribution
Lighting
Camera
Fig. 2. Video camera and lighting system set-up.
was provided by a single 36 W fluorescent lamp installed at 30 cm of the froth surface (see Fig. 2). 3. Data acquisition The process variables including air flow rate, slurry level, pH, collector/ frother dosage and sodium silicate dosage were changed at different levels and their influence on the froth visual features was monitored. Different levels of the variables were determined in consultation with plant metallurgists and operating staff. In each test program, only one of the operating variables was changed while the other variables were kept constant. The video data were collected after ensuring process stability. Table 1 gives details of the operating conditions set during the video sampling time. Fig. 3 shows typical froth images collected at different operating conditions.
It has been extensively reported that the bubble size at the froth surface is strongly related to the operating conditions and the process performance (Moolman et al., 1996a,b). Various techniques developed for bubble size measurement include segmentation (Sadr-kazemi and Cilliers, 1997; Cipriano et al., 1998; Bonifazi et al., 2001; Wang et al., 2003; Mehrshad and Massinaei, 2011), texture spectrum (Nguyen and Thornton, 1995), wavelet texture analysis (Liu et al., 2005), modified texture spectrum approach (Lin et al., 2008) and using interfacial morphological information (Yang et al., 2009). In practice, each of these methods has its respective advantages and disadvantages. In this study, a technique based on the detection of white spots on the bubble surface was employed for bubble size estimation (Wang and Wang, 2000). The size of white spots is proportional to the bubble size and their number is inversely proportional to mean bubble size in the froth image. In this method, correct illumination of the froth surface is of the utmost importance. Hence, a single 36 W fluorescent lamp was mounted at 30 cm of the cell surface (see Fig. 2). The developed algorithm for white spot detection and bubble size distribution includes the following procedures: (i) (ii) (iii) (iv)
Denoise the image using the median filter. Threshold the image. Apply the morphological operators to the image. Estimate the bubble size distribution.
Table 1 Operating conditions used during video sampling. Runs
Conditions
Collector/ Frother
Depressant
pH
Air flow rate
Slurry level
1 2 3 4 5 6 7 8 9 10 11
Normal High collector/frother Low collector/frother High depressant Low depressant High pH Low pH High air flow rate Low air flow rate High slurry level Low slurry level
Medium High Low Medium Medium Medium Medium Medium Medium Medium Medium
Medium Medium Medium High Low Medium Medium Medium Medium Medium Medium
Medium Medium Medium Medium Medium High Low Medium Medium Medium Medium
Medium Medium Medium Medium Medium Medium Medium High Low Medium Medium
Medium Medium Medium Medium Medium Medium Medium Medium Medium High Low
62
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
High air flow rate (a)
Low air flow rate (b)
High pH (c)
Low pH (d)
High collector/frother (e)
Low collector/frother (f)
High slurry level (g)
Low slurry level (h)
High depressant dosage(i)
Low depressant dosage(j)
Normal conditions(k)
Fig. 3. Typical froth images captured at different process conditions.
More details of the proposed algorithm are given in the block diagram presented in Fig. 4. A comparison between the watershed based bubble segmentation and the white spot based bubble detection techniques is presented in Fig. 5. The results reveal that the white spot algorithm is capable of accurately localizing the bubbles of different
Original froth image
Preprocessing by median filter
Adaptive thresholding
Bubble hole filling
Applying morphological operators
Removing bubble sizes less than threshold
Removing fine bubbles
White spot detected froth image Fig. 4. Block diagram of developed white spot detection algorithm.
size and shape. Moreover, the over-segmentation problem associated with the watershed algorithm is overcome. Fig. 6 shows the mean bubble size measured from the froth images at different conditions. The results indicate that different process conditions are well recognized through the mean bubble size values measured from the froth images. The influence of changes in the operating conditions on the bubble size is shown in Fig. 7. It is evident that the bubble size is mostly influenced by the slurry level, pH, depressant dosage and air flow rate. Larger bubbles observed at low slurry level are due to gradual thinning the liquid film between bubbles and hence their coalescence in the froth phase (see Fig. 3h). Finer bubbles captured at higher pH may be ascribed to the change in ionic strength of the solution (Tucker et al., 1994) (see Fig. 3c). Furthermore, pH is sometimes as a frother modifier and some frothers require a higher pH to retain a more lasting frothing power (Bulatovic, 2007). The increase in bubble size in the presence of a depressant (i.e. sodium silicate) is attributed to reduced bubble loading and enhanced bubble coalescence (see Fig. 3i). The froth mean bubble size increases with an increase in the air flow rate which is related to enhanced bubble coalescence (see Fig. 3a). 4.2. Number of bubbles The number of bubbles was calculated by tracking the bubbles in successive video frames (Botha, 1999). A block diagram of the proposed algorithm is presented in Fig. 8. First, optical flow of the video frame was estimated. Secondly, a median filter was employed to filter out noise. The denoised image was adaptively thresholded and then the bubble holes⁎ were filled. Afterward, size and center of the highlighted bubbles ⁎
A hole is defined as an area of dark pixels surrounded by lighter pixels.
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
(a))
(b)
63
(c)
Fig. 5. Original (a), watershed segmented (b) and white spot detected (c) froth images.
4.3. Froth velocity Froth velocity can be quantified by measuring the movement of bubble centers in consecutive frames. Block matching, pixel tracing and bubble tracking are the most commonly used techniques to quantify the froth velocity. In the block matching method, the first frame is subdivided into a user-specified number of blocks (Forbes, 2007). Cross correlation is used to search within a window in the subsequent frame for the best match to each of the individual blocks. In this way a motion vector field is generated and the mean values of the motion vector field are used as the final velocity measure. The block matching algorithm is slow and the movement of individual bubbles cannot be tracked. Holtham and Nguyen (2002) developed the pixel tracing technique for measuring the froth velocity (Holtham and Nguyen, 2002). In this approach, a block in the center of an image is compared with corresponding blocks in a subsequent image. The motion vector is determined by the blocks with the highest correlation. The pixel tracing method is likely fast, but is not accurate since it does not examine the entire motion search space.
Variaon of Mean Bubble Size
Mean Bubble Size (pixel)
were determined by applying the morphological operators. To avoid the probable errors, fine bubbles were removed from the image. Finally, for each detected bubble an array was defined in such a way that the tracking process of each bubble to be stored in its corresponding array. In general, two assumptions were made in developing the bubble tracking algorithm. First, the bubble movement in two consecutive frames is low. This is a reasonable assumption according to high video sampling rate employed (i.e. 25 frames per second). Secondly, the bubble size increase or at least remain constant in successive frames. Thus, bubbles with above characteristics and conditions are placed in the same array. If no bubble with these characteristics is found in the next frame, it means that the bubble has burst and its array is ended. Moreover, if a new bubble has been created in the next frame, a new array is assigned to it and the tracking process is performed. To improve the accuracy of the results, arrays with more than 10 frames were stored. Hence, the number of arrays obtained is equal to the number of bubbles tracked. Fig. 9 shows the number of bubble arrays tracked at different experimental conditions. The results indicate that different process conditions are well detected through the number of bubble arrays extracted from the froth images. The influence of changes in the process variables on the number of bubble arrays is shown in Fig. 10. 26 25 24 23 22 21 20 19 18
Fig. 6. Mean bubble size measured at different process conditions.
6.0 5.0 4.0 3.0 2.0 1.0 0.0
Fig. 7. Sensitivity of bubble size to changes in process variables.
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Original froth image in first frame White spot detection of froth image
Optical flow estimation
Calculating size and center of the bubbles
Array formation for bubble tracking
Adding nearest bubble in next frame to last bubble of each array
Two last bubble distance of array is low?
No
Yes
Bubble size is less than last frame’ s bubble?
Yes
Eliminating last bubble and ending bubble array
Sorting arrays with lengths more than threshold values
No Bubble addition to last bubble array
Considering new arrays for new bubbles
Fig. 8. Block diagram of proposed bubble tracking algorithm.
Number of Bubble Arrays
700 7 0 600 6 0 500 5 0 400 4 0 300 3 0 200 2 0 100 1 0 0
Fig. 9. Number of bubble tracked at different process conditions.
froth velocity is shown in Fig. 12. It can be seen that the major variables affecting the froth velocity include the air flow rate, slurry level and depressant dosage. The froth mobility increases with the air flow rate due to increased bubble surface area flux and water recovery (see Fig. 3a). The froth is watery and mobile with small spherical bubbles when the slurry level is high (i.e. froth depth is low) (see Fig. 3g). The less mobile froths created in the presence of sodium silicate is connected with less water recovered and larger bubbles observed at the froth surface (see Fig. 3i).
Variaon of Number of Bubble Arrays
In the bubble tracking technique, the movement of the localized reflections of light on the bubble surfaces is tracked (Botha, 1999). Bubble motion analysis calculates a motion vector for each bubble marker. In the current work, a bubble tracking algorithm was developed for measuring the froth velocity. In this approach after highlighting the bubbles (by the previously described technique), an array was assigned for each bubble and the bubble movement was measured using the Euclidean distance method. Finally, the mean of bubble movements was considered as the froth mobility measure. The correlation between the process conditions and the froth velocity is shown in Fig. 11. Effect of changes in the process variables on the
400 350 300 250 200 150 100 50 0
Fig. 10. Sensitivity of number of bubbles to changes in process variables.
5.5 5 5.0 0 4.5 5 4.0 0 3.5 5 3.0 0 2.5 5 2.0 0 1.5 5 1.0 0
Froth Stability (maximum length of arrays)
Froth Velocity (pixel/s)
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
40 24 00 20 60 16 20 12 80 8 40 4
The froth stability is one of the crucial factors in the control of flotation process. The froth stability can be quantified by analyzing consecutive frames and detecting the rate of change in the appearance of the images (Moolman et al., 1995; Nguyen and Holtham, 1997; Kaartinen et al., 2006). In the bubble tracking technique developed in this study, the array length formed is equal to the number of frames in which the bubble has been tracked. Thus, the maximum of array length was considered as maximum stability feature. By this approach, the froth stability is accurately estimated. It should be mentioned that more stable froths are more viscous and less mobile which produces less watery concentrates. Fig. 13 shows the stability measure extracted from the froth images at different experimental conditions. Fig. 14 presents effect of changes in the process variables on the froth stability. The results indicate that the froth stability is much influenced by pH, depressant dosage and air flow rate. At high aeration rates, the froth becomes unstable because of turbulence created in the pulp and froth zones. Reduced froth stability at higher pH values can be related to decreased slurry viscosity as well as to the change in ionic strength of the solution (Farrokhpay and Zanin, 2011). Increasing the froth stability in the presence of sodium silicate is linked with well-loading of bubbles with minerals in such a way that the froth is neither too brittle nor too stiff.
4.5. Correlation between the metallurgical parameters and froth features
3.5 5
system (Holtham and Nguyen, 2002; Kaartinen et al., 2006). The correlation between the image variables and metallurgical performances are shown in Fig. 15. The results indicate that there is a good correlation between the froth features (i.e. mean bubble size, number of bubbles and froth velocity) and metallurgical parameters. As expected, the phosphorous recovery is directly proportional to the number of bubbles and inversely related to the mean bubble size. The positive correlation between the froth velocity and recovery shows that more mobile froths lead to faster flotation. It is evident that there is no clear correlation between the froth stability and the metallurgical parameters in the present conducted experiments. Additional tests must be made to evaluate the exact relationship between the froth stability and process performance. 5. Conclusions In this communication machine vision based monitoring of an industrial mechanical flotation cell was discussed. Bubble size distribution, number of bubbles, froth velocity and stability were extracted from the froth images at different operating conditions. The bubble size distribution was estimated through the detected white spots on the bubble surface. A simple and robust algorithm was developed to measure the number of bubbles by tracking the bubbles in successive video images. The froth velocity was successfully measured by the bubble tracking algorithm. The froth stability was quantified based on the array length and the number of frames in which the bubble has tracked. The results indicated that the extracted visual features were capable of accurately describing the process behavior at different operating conditions. A good correlation between the image variables and metallurgical parameters was found which is of great importance for control purposes.
Variaon of Froth Stability
Variaon of Froth Velocity
Estimation of the metallurgical performances from the visual froth features and feedback control of the process by manipulating the operating variables is the ultimate goal of a machine vision based control
3.0 0 2.5 5 2.0 0 1.5 5 1.0 0 0.5 5 0.0 0
Fig. 12. Sensitivity of froth velocity to changes in process variables.
0
Fig. 13. Froth stability measured at different process conditions.
Fig. 11. Froth velocity measured at different process conditions.
4.4. Froth stability
65
200 0 175 5 150 0 125 5 100 0 75 5 50 0 25 5 0
Fig. 14. Sensitivity of froth stability to changes in process variables.
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
1.1
1.1
1.0
1.0
0.9 0.8 0.7 0.6 0.5
Meaan Buubblee Sizze Num mber of B Bubbles
Phosphorus Recovery (%)
Phosphorus Recovery (%)
66
0.4
00.9 00.8 00.7 00.6
00.4
0 0.9 1.00 1..1 0..0 0.1 0.2 0.33 0.4 00.5 0.66 0.7 0.8
0 0.88 0.9 1.0 1.1 0.0 0.1 0.22 00.3 0.4 0.55 0..6 0.7
Mean Bubble Size Number of Bubbles
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Normalized Froth Features
N Norm malizzed Frooth Feat F turees
Concentrate grade (%)
Concentrate grade (%)
Noormaalizeed FFrothh Feeatuures 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Froth F h Velocityy F h Staabilityy Froth
00.5
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Froth Velocity Froth Stability
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Normalized Froth Features
Fig. 15. Correlation between metallurgical parameters and froth features.
References Aldrich, C., Marais, C., Shean, B.J., Cilliers, J.J., 2010. On-line monitoring and control of froth flotation systems with machine vision: a review. Int. J. Miner. Process. 96, 1–13. Bonifazi, G., Serranti, S., Volpe, F., Zuco, R., 2001. Characterization of flotation froth colour and structure by machine vision. Comput. Geosci. 27 (9), 1111–1117. Botha, C.P., 1999. An Online Machine Vision Froth Flotation Analysis PlatformM.Sc. thesis University of Stellenbosch, Stellenbosch, South Africa. Bulatovic, S.M., 2007. Handbook of Flotation Reagents. Elsevier Science & Technology Books. Cipriano, A., Guarini, M., Vidal, R., Soto, A., Sepulveda, C., Mery, D., Briseno, H., 1998. A real time visual sensor for supervision of flotation cells. Miner. Eng. 11 (6), 489–499. Farrokhpay, S., Zanin, M., 2011. Effect of water quality on froth stability in flotation. Chemeca 2011, Engineering a Better World Sydney, New South Wales, Australia, 18–21 September 2011, pp. 1–8. Forbes, G., 2007. Texture and bubble size measurements for modelling concentrate grade in flotation froth systemsPhD Thesis University of Cape Town, South Africa. Holtham, P.N., Nguyen, L.K., 2002. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. Int. J. Miner. Process. 64, 163–180. Kaartinen, J., Hatonen, J., Hyotyniemi, H., Miettunen, J., 2006. Machine vision based control of zinc flotation—a case study. Control. Eng. Pract. 14, 1455–1466. King, R.P., 2001. Modeling and Simulation of Mineral Processing Systems. ButterworthHeinemann. Lin, B., Recke, B., Knudsen, J.K.H., Jorgensen, S.B., 2008. Bubble size estimation for flotation processes. Miner. Eng. 21, 539–548. Liu, J.J., MacGregor, J.F., Duchesne, C., Bartolacci, G., 2005. Flotation froth monitoring using multiresolutional multivariate image analysis. Miner. Eng. 18, 65–76. Mehrshad, N., Massinaei, M., 2011. New image processing algorithm for measurement of bubble size distribution from flotation froth images. Miner. Metall. Process. J. 28 (3), 146–150. Moolman, D.M., Aldrich, C., Van Deventer, J.S.J., 1995. The interpretation of flotation froth surfaces by using digital image analysis and neural networks. Chem. Eng. Sci. 50, 3501–3513.
Moolman, D.W., Eksteen, J.J., Aldrich, C., Van Deventer, J.S.J., 1996a. The significance of flotation froth appearance for machine vision control. Int. J. Miner. Process. 48 (3–4), 135–158. Moolman, D.W., Aldrich, C., Schmitz, G.P.J., Van Deventer, J.S.J., 1996b. The interrelationship between surface froth characteristics and industrial flotation performance. Miner. Eng. 9 (8), 837–854. Morar, S.H., Harris, M.C., Bradshaw, D.J., 2012. The use of machine vision to predict flotation performance. Miner. Eng. 36–38, 31–36. Nguyen, K.K., Holtham, P., 1997. The application of pixel tracing techniques in the flotation process. Proceedings of the First Joint Australian and New Zealand Biennial Conference on Digital Imaging and Vision Computing and Applications, pp. 207–212. Nguyen, K.K., Thornton, A.J., 1995. The application of texture-based image analysis techniques in froth flotation. Conference Proceedings DICTA-95Digital Image Computing: Techniques and Applications. Australian Pattern Recognition Society. Sadr-kazemi, N., Cilliers, J., 1997. An image processing algorithm for measurement of flotation froth bubble size and shape distributions. Miner. Eng. 10 (10), 1075–1083. Tucker, J.P., Deglon, D.A., Franzidis, J.P., Harris, M.G., O'Connor, C.T., 1994. An evolution of a direct method of bubble size distribution measurement in a laboratory batch flotation cell. Miner. Eng. 7 (5/6), 667–680. Vanegas, C., Holtham, P., 2008. On-line froth acoustic emission measurements in industrial sites. Miner. Eng. 21, 883–888. Wang, A., Wang, L., 2000. Froth image segmentation algorithms and their validation. International Conference on Signal Processing Proceedings, Beijing, China, pp. 2042–2045. Wang, W., Bergholm, F., Yang, B., 2003. Froth delineation based on image classification. Miner. Eng. 16, 1183–1192. Wills, B.A., Napier-Munn, T.J., 2006. Mineral Processing Technology. Elsevier Science & Technology Books. Yang, C., Xu, C., Mu, X., Zhou, K., 2009. Bubble size estimation using interfacial morphological information for mineral flotation process monitoring. Trans. Nonferrous Met. Soc. China 19, 694–699.
Contents lists available at ScienceDirect
International Journal of Mineral Processing journal homepage: www.elsevier.com/locate/ijminpro
Machine vision based monitoring of an industrial flotation cell in an iron flotation plant A. Mehrabi a,b, N. Mehrshad a, M. Massinaei c,⁎ a b c
Electrical Engineering Department, University of Birjand, Birjand, Iran Senior Automation & Control Engineer, Chadormalu Mining & Industrial Company, Yazd, Iran Mining Engineering Department, University of Birjand, Birjand, Iran
a r t i c l e
i n f o
Article history: Received 11 April 2014 Received in revised form 25 July 2014 Accepted 30 September 2014 Available online 8 October 2014 Keywords: Froth flotation Image analysis Machine vision Process control
a b s t r a c t Froth flotation is the most commonly used technique for the separation of valuable from gangue minerals. Unforeseeable changes in the ore characteristics and operating conditions have necessitated continuous control of the flotation circuits. Experienced operators usually control the process performance through the froth visual features. Machine vision technology now offers a viable means of monitoring and control of the flotation circuits. In the current communication, a machine vision system was installed on a flotation cell in the rougher circuit of an iron flotation plant to monitor the process at different conditions. Bubble size distribution, number of bubbles, froth velocity and stability were the main visual features extracted from the froth images. The results indicate that the developed system is capable of accurately monitoring the process behavior at different conditions. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Flotation is a physico-chemical separation process that utilizes the difference in surface properties of the valuable minerals and the unwanted gangue minerals (Wills and Napier-Munn, 2006). In a flotation cell, already conditioned fine particles with reagents (i.e. collectors and frothers) are subjected to air bubbles and the valuable (hydrophobic) particles attach to air bubbles and rise to the froth layer for recovery, while the unwanted (hydrophilic) particles remain at the cell bottom. Modeling and simulation of a flotation process is a difficult task owing to several variables and interactions involved (King, 2001). Flotation circuits are subjected to a wide range of process disturbances, some of which are caused by the change in mineral characteristics and others to variation in operating conditions. In most flotation plants, process operators monitor the froth surface visually and make adjustments to process parameters. However, due to natural complexity of the flotation process, achieving optimal control is often not possible for the human operators. The need to overcome these problems, coupled with rapid advances in computer technology has led to the development of machine vision systems for monitoring and control of flotation circuits (Moolman et al., 1995; 1996a,b; Holtham and Nguyen, 2002; Kaartinen et al., 2006; Vanegas and Holtham, 2008; Aldrich et al., 2010; Morar et al., 2012). To date, several attempts have been made to develop effective
⁎ Corresponding author. Tel.: +98 561 2502133. E-mail address: [email protected] (M. Massinaei).
http://dx.doi.org/10.1016/j.minpro.2014.09.018 0301-7516/© 2014 Elsevier B.V. All rights reserved.
algorithms for measuring the froth visual properties. A brief description of previous studies will be reviewed in the relevant later sections. In this research work, a machine vision system was developed to characterize the process behavior at different operating conditions. Data acquisition and analysis was carried out on the first flotation cell in the rougher circuit of an iron flotation plant. 2. Chadormalu flotation circuit and video camera set-up Test work was conducted at the Chadormalu (the largest iron deposit in central Iran) iron flotation plant in Iran. In this plant around 2000 t/h ore containing approximately 55% Fe and 1% P (with magnetite, hematite and apatite as the valuable minerals) is treated to produce iron (with 67% Fe and 0.045% P2O5) and apatite (with 33% P2O5 and b 3% iron) concentrates. The comminution is accomplished in a gyratory crusher followed by five parallel SAG mills. The ground slurry is subjected to several stages of low/high intensity magnetic separation followed by froth flotation to produce iron and apatite concentrates. A simplified flowsheet of the beneficiation process is shown in Fig. 1. Sodium carbonate (for pH adjustment), sodium silicate (as depressant of iron oxides) and Berol/Asam combination (as collector/frother) are the flotation reagents which are added to the conditioning tank of the hematite flotation circuit. A video camera was positioned at a distance of 110 cm of the froth surface of the first rougher cell as shown in Fig. 1. The video camera was mounted on a specially made bracket with an adjustable arm allowing lateral and vertical adjustments (see Fig. 2). The zoom and focus of the camera were set-up so that an area of approximately 300 × 400 mm was subjected to filming. Lighting
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
SAG Mill
Low-intensity magnetic separation
61
Magnetite concentrate Hematite concentrate
Camera High-intensity magnetic separation
Apatite flotation
Apatite concentrate
Tailing
Fig. 1. Simplified flowsheet of Chadormalo beneficiation process.
4. Extracted visual features Bubble size distribution, number of bubbles, froth velocity and stability were the main visual features extracted from the captured froth images. More details of the methodology employed to quantify the froth features along with the result implementation are given in the following sections. 4.1. Bubble size distribution
Lighting
Camera
Fig. 2. Video camera and lighting system set-up.
was provided by a single 36 W fluorescent lamp installed at 30 cm of the froth surface (see Fig. 2). 3. Data acquisition The process variables including air flow rate, slurry level, pH, collector/ frother dosage and sodium silicate dosage were changed at different levels and their influence on the froth visual features was monitored. Different levels of the variables were determined in consultation with plant metallurgists and operating staff. In each test program, only one of the operating variables was changed while the other variables were kept constant. The video data were collected after ensuring process stability. Table 1 gives details of the operating conditions set during the video sampling time. Fig. 3 shows typical froth images collected at different operating conditions.
It has been extensively reported that the bubble size at the froth surface is strongly related to the operating conditions and the process performance (Moolman et al., 1996a,b). Various techniques developed for bubble size measurement include segmentation (Sadr-kazemi and Cilliers, 1997; Cipriano et al., 1998; Bonifazi et al., 2001; Wang et al., 2003; Mehrshad and Massinaei, 2011), texture spectrum (Nguyen and Thornton, 1995), wavelet texture analysis (Liu et al., 2005), modified texture spectrum approach (Lin et al., 2008) and using interfacial morphological information (Yang et al., 2009). In practice, each of these methods has its respective advantages and disadvantages. In this study, a technique based on the detection of white spots on the bubble surface was employed for bubble size estimation (Wang and Wang, 2000). The size of white spots is proportional to the bubble size and their number is inversely proportional to mean bubble size in the froth image. In this method, correct illumination of the froth surface is of the utmost importance. Hence, a single 36 W fluorescent lamp was mounted at 30 cm of the cell surface (see Fig. 2). The developed algorithm for white spot detection and bubble size distribution includes the following procedures: (i) (ii) (iii) (iv)
Denoise the image using the median filter. Threshold the image. Apply the morphological operators to the image. Estimate the bubble size distribution.
Table 1 Operating conditions used during video sampling. Runs
Conditions
Collector/ Frother
Depressant
pH
Air flow rate
Slurry level
1 2 3 4 5 6 7 8 9 10 11
Normal High collector/frother Low collector/frother High depressant Low depressant High pH Low pH High air flow rate Low air flow rate High slurry level Low slurry level
Medium High Low Medium Medium Medium Medium Medium Medium Medium Medium
Medium Medium Medium High Low Medium Medium Medium Medium Medium Medium
Medium Medium Medium Medium Medium High Low Medium Medium Medium Medium
Medium Medium Medium Medium Medium Medium Medium High Low Medium Medium
Medium Medium Medium Medium Medium Medium Medium Medium Medium High Low
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High air flow rate (a)
Low air flow rate (b)
High pH (c)
Low pH (d)
High collector/frother (e)
Low collector/frother (f)
High slurry level (g)
Low slurry level (h)
High depressant dosage(i)
Low depressant dosage(j)
Normal conditions(k)
Fig. 3. Typical froth images captured at different process conditions.
More details of the proposed algorithm are given in the block diagram presented in Fig. 4. A comparison between the watershed based bubble segmentation and the white spot based bubble detection techniques is presented in Fig. 5. The results reveal that the white spot algorithm is capable of accurately localizing the bubbles of different
Original froth image
Preprocessing by median filter
Adaptive thresholding
Bubble hole filling
Applying morphological operators
Removing bubble sizes less than threshold
Removing fine bubbles
White spot detected froth image Fig. 4. Block diagram of developed white spot detection algorithm.
size and shape. Moreover, the over-segmentation problem associated with the watershed algorithm is overcome. Fig. 6 shows the mean bubble size measured from the froth images at different conditions. The results indicate that different process conditions are well recognized through the mean bubble size values measured from the froth images. The influence of changes in the operating conditions on the bubble size is shown in Fig. 7. It is evident that the bubble size is mostly influenced by the slurry level, pH, depressant dosage and air flow rate. Larger bubbles observed at low slurry level are due to gradual thinning the liquid film between bubbles and hence their coalescence in the froth phase (see Fig. 3h). Finer bubbles captured at higher pH may be ascribed to the change in ionic strength of the solution (Tucker et al., 1994) (see Fig. 3c). Furthermore, pH is sometimes as a frother modifier and some frothers require a higher pH to retain a more lasting frothing power (Bulatovic, 2007). The increase in bubble size in the presence of a depressant (i.e. sodium silicate) is attributed to reduced bubble loading and enhanced bubble coalescence (see Fig. 3i). The froth mean bubble size increases with an increase in the air flow rate which is related to enhanced bubble coalescence (see Fig. 3a). 4.2. Number of bubbles The number of bubbles was calculated by tracking the bubbles in successive video frames (Botha, 1999). A block diagram of the proposed algorithm is presented in Fig. 8. First, optical flow of the video frame was estimated. Secondly, a median filter was employed to filter out noise. The denoised image was adaptively thresholded and then the bubble holes⁎ were filled. Afterward, size and center of the highlighted bubbles ⁎
A hole is defined as an area of dark pixels surrounded by lighter pixels.
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(a))
(b)
63
(c)
Fig. 5. Original (a), watershed segmented (b) and white spot detected (c) froth images.
4.3. Froth velocity Froth velocity can be quantified by measuring the movement of bubble centers in consecutive frames. Block matching, pixel tracing and bubble tracking are the most commonly used techniques to quantify the froth velocity. In the block matching method, the first frame is subdivided into a user-specified number of blocks (Forbes, 2007). Cross correlation is used to search within a window in the subsequent frame for the best match to each of the individual blocks. In this way a motion vector field is generated and the mean values of the motion vector field are used as the final velocity measure. The block matching algorithm is slow and the movement of individual bubbles cannot be tracked. Holtham and Nguyen (2002) developed the pixel tracing technique for measuring the froth velocity (Holtham and Nguyen, 2002). In this approach, a block in the center of an image is compared with corresponding blocks in a subsequent image. The motion vector is determined by the blocks with the highest correlation. The pixel tracing method is likely fast, but is not accurate since it does not examine the entire motion search space.
Variaon of Mean Bubble Size
Mean Bubble Size (pixel)
were determined by applying the morphological operators. To avoid the probable errors, fine bubbles were removed from the image. Finally, for each detected bubble an array was defined in such a way that the tracking process of each bubble to be stored in its corresponding array. In general, two assumptions were made in developing the bubble tracking algorithm. First, the bubble movement in two consecutive frames is low. This is a reasonable assumption according to high video sampling rate employed (i.e. 25 frames per second). Secondly, the bubble size increase or at least remain constant in successive frames. Thus, bubbles with above characteristics and conditions are placed in the same array. If no bubble with these characteristics is found in the next frame, it means that the bubble has burst and its array is ended. Moreover, if a new bubble has been created in the next frame, a new array is assigned to it and the tracking process is performed. To improve the accuracy of the results, arrays with more than 10 frames were stored. Hence, the number of arrays obtained is equal to the number of bubbles tracked. Fig. 9 shows the number of bubble arrays tracked at different experimental conditions. The results indicate that different process conditions are well detected through the number of bubble arrays extracted from the froth images. The influence of changes in the process variables on the number of bubble arrays is shown in Fig. 10. 26 25 24 23 22 21 20 19 18
Fig. 6. Mean bubble size measured at different process conditions.
6.0 5.0 4.0 3.0 2.0 1.0 0.0
Fig. 7. Sensitivity of bubble size to changes in process variables.
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Original froth image in first frame White spot detection of froth image
Optical flow estimation
Calculating size and center of the bubbles
Array formation for bubble tracking
Adding nearest bubble in next frame to last bubble of each array
Two last bubble distance of array is low?
No
Yes
Bubble size is less than last frame’ s bubble?
Yes
Eliminating last bubble and ending bubble array
Sorting arrays with lengths more than threshold values
No Bubble addition to last bubble array
Considering new arrays for new bubbles
Fig. 8. Block diagram of proposed bubble tracking algorithm.
Number of Bubble Arrays
700 7 0 600 6 0 500 5 0 400 4 0 300 3 0 200 2 0 100 1 0 0
Fig. 9. Number of bubble tracked at different process conditions.
froth velocity is shown in Fig. 12. It can be seen that the major variables affecting the froth velocity include the air flow rate, slurry level and depressant dosage. The froth mobility increases with the air flow rate due to increased bubble surface area flux and water recovery (see Fig. 3a). The froth is watery and mobile with small spherical bubbles when the slurry level is high (i.e. froth depth is low) (see Fig. 3g). The less mobile froths created in the presence of sodium silicate is connected with less water recovered and larger bubbles observed at the froth surface (see Fig. 3i).
Variaon of Number of Bubble Arrays
In the bubble tracking technique, the movement of the localized reflections of light on the bubble surfaces is tracked (Botha, 1999). Bubble motion analysis calculates a motion vector for each bubble marker. In the current work, a bubble tracking algorithm was developed for measuring the froth velocity. In this approach after highlighting the bubbles (by the previously described technique), an array was assigned for each bubble and the bubble movement was measured using the Euclidean distance method. Finally, the mean of bubble movements was considered as the froth mobility measure. The correlation between the process conditions and the froth velocity is shown in Fig. 11. Effect of changes in the process variables on the
400 350 300 250 200 150 100 50 0
Fig. 10. Sensitivity of number of bubbles to changes in process variables.
5.5 5 5.0 0 4.5 5 4.0 0 3.5 5 3.0 0 2.5 5 2.0 0 1.5 5 1.0 0
Froth Stability (maximum length of arrays)
Froth Velocity (pixel/s)
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40 24 00 20 60 16 20 12 80 8 40 4
The froth stability is one of the crucial factors in the control of flotation process. The froth stability can be quantified by analyzing consecutive frames and detecting the rate of change in the appearance of the images (Moolman et al., 1995; Nguyen and Holtham, 1997; Kaartinen et al., 2006). In the bubble tracking technique developed in this study, the array length formed is equal to the number of frames in which the bubble has been tracked. Thus, the maximum of array length was considered as maximum stability feature. By this approach, the froth stability is accurately estimated. It should be mentioned that more stable froths are more viscous and less mobile which produces less watery concentrates. Fig. 13 shows the stability measure extracted from the froth images at different experimental conditions. Fig. 14 presents effect of changes in the process variables on the froth stability. The results indicate that the froth stability is much influenced by pH, depressant dosage and air flow rate. At high aeration rates, the froth becomes unstable because of turbulence created in the pulp and froth zones. Reduced froth stability at higher pH values can be related to decreased slurry viscosity as well as to the change in ionic strength of the solution (Farrokhpay and Zanin, 2011). Increasing the froth stability in the presence of sodium silicate is linked with well-loading of bubbles with minerals in such a way that the froth is neither too brittle nor too stiff.
4.5. Correlation between the metallurgical parameters and froth features
3.5 5
system (Holtham and Nguyen, 2002; Kaartinen et al., 2006). The correlation between the image variables and metallurgical performances are shown in Fig. 15. The results indicate that there is a good correlation between the froth features (i.e. mean bubble size, number of bubbles and froth velocity) and metallurgical parameters. As expected, the phosphorous recovery is directly proportional to the number of bubbles and inversely related to the mean bubble size. The positive correlation between the froth velocity and recovery shows that more mobile froths lead to faster flotation. It is evident that there is no clear correlation between the froth stability and the metallurgical parameters in the present conducted experiments. Additional tests must be made to evaluate the exact relationship between the froth stability and process performance. 5. Conclusions In this communication machine vision based monitoring of an industrial mechanical flotation cell was discussed. Bubble size distribution, number of bubbles, froth velocity and stability were extracted from the froth images at different operating conditions. The bubble size distribution was estimated through the detected white spots on the bubble surface. A simple and robust algorithm was developed to measure the number of bubbles by tracking the bubbles in successive video images. The froth velocity was successfully measured by the bubble tracking algorithm. The froth stability was quantified based on the array length and the number of frames in which the bubble has tracked. The results indicated that the extracted visual features were capable of accurately describing the process behavior at different operating conditions. A good correlation between the image variables and metallurgical parameters was found which is of great importance for control purposes.
Variaon of Froth Stability
Variaon of Froth Velocity
Estimation of the metallurgical performances from the visual froth features and feedback control of the process by manipulating the operating variables is the ultimate goal of a machine vision based control
3.0 0 2.5 5 2.0 0 1.5 5 1.0 0 0.5 5 0.0 0
Fig. 12. Sensitivity of froth velocity to changes in process variables.
0
Fig. 13. Froth stability measured at different process conditions.
Fig. 11. Froth velocity measured at different process conditions.
4.4. Froth stability
65
200 0 175 5 150 0 125 5 100 0 75 5 50 0 25 5 0
Fig. 14. Sensitivity of froth stability to changes in process variables.
A. Mehrabi et al. / International Journal of Mineral Processing 133 (2014) 60–66
1.1
1.1
1.0
1.0
0.9 0.8 0.7 0.6 0.5
Meaan Buubblee Sizze Num mber of B Bubbles
Phosphorus Recovery (%)
Phosphorus Recovery (%)
66
0.4
00.9 00.8 00.7 00.6
00.4
0 0.9 1.00 1..1 0..0 0.1 0.2 0.33 0.4 00.5 0.66 0.7 0.8
0 0.88 0.9 1.0 1.1 0.0 0.1 0.22 00.3 0.4 0.55 0..6 0.7
Mean Bubble Size Number of Bubbles
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Normalized Froth Features
N Norm malizzed Frooth Feat F turees
Concentrate grade (%)
Concentrate grade (%)
Noormaalizeed FFrothh Feeatuures 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Froth F h Velocityy F h Staabilityy Froth
00.5
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Froth Velocity Froth Stability
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Normalized Froth Features
Fig. 15. Correlation between metallurgical parameters and froth features.
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