ce

b

traan

Keywords:Froth otationImage analysisProcess modelingNeural networks

ed tbe

er/collector dosage and pH) and the froth features (i.e. bubble size, froth velocity, froth color and frothstability) along with the metallurgical performances (i.e. copper/mass/water recoveries and concentrate

ries, frluable

froth appearance. Today, machine vision systems can reliably and

1996b; Morar et al., 2012; Vanegas and Holtham, 2008).The primary control objectives of otation circuits are the met-

allurgical factors (i.e. recovery and concentrate grade) (Bergh andYianatos, 1993). The on-line measurement and estimation of thesevariables usually requires sophisticated instruments which areexpensive to purchase and maintain (Liu and MacGregor, 2008).

opment of on-line machine vision based control systems.

2.1. Flotation tests

Laboratory experiments were conducted on a copper sulfrom Qaleh-Zari copper mine, located in the south-east of Iran. Theore was rst crushed to 2 mm in a jaw crusher and then furtherground in a ball mill to d80 = 75 lm. The slurry from the ball millwas transferred to a 2.5 L laboratory otation cell (see Fig. 1).The slurry was conditioned with a certain amount of collector(Potassium Amyl Xanthate) and frother (Aerofroth 65) for 2 and0.5 min, respectively, just prior to otation.

Corresponding author. Tel.: +60 176343140.E-mail address: gs32354@mutiara.upm.edu.my (A. Jahedsaravani).

Minerals Engineering 69 (2014) 137145

Contents lists availab

n

elsautomatically measure the froth characteristics from digital imageswithin a short time and present the results to the operators or inputthem to process control systems (Aldrich et al., 2010; Holtham andNguyen, 2002; Kaartinen et al., 2006; Moolman et al., 1996a, 1995,

2. Experimental detailshttp://dx.doi.org/10.1016/j.mineng.2014.08.0030892-6875/ 2014 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).de oreEffective control of otation circuits is a difcult task owing to sev-eral variables involved, unavoidable changes in ore characteristicsand non-linear and dynamic nature of the process (Bonifazi et al.,2002).

The otation circuits have traditionally been controlled by theexperienced plant operators through monitoring changes in the

1996; Kaartinen et al., 2006; Moolman et al., 1995; Morar et al.,2012).

This paper presents the ndings of the laboratory test work con-ducted to evaluate and model the relationship between the frothvisual features and otation performance parameters. Such inves-tigations can provide signicant contributions towards the devel-1. Introduction

In the mineral processing industmon process for separation of the vagrade) were determined for each run. The relationships between the froth characteristics and perfor-mance parameters were successfully modeled using the neural networks. The performance of the devel-oped models was evaluated by the correlation coefcient (R) and the root mean square error (RMSE). Theresults indicated that the copper recovery (RMSE = 2.9; R = 0.9), concentrate grade (RMSE = 1.07;R = 0.92), mass recovery (RMSE = 1.94; R = 0.94) and water recovery (RMSE = 3.07; R = 0.95) can be accu-rately predicted from the extracted surface froth features, which is of central importance for controlpurposes. 2014 The Authors. Published by Elsevier Ltd. This is anopenaccess article under the CCBY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/3.0/).

oth otation is a com-from gangue minerals.

Previous studies have shown that the froth visual characteristicsreect changes in the process conditions and can be used to predictthe metallurgical factors (Aldrich et al., 1997; Banford et al., 1998;Bonifazi et al., 2000; Hargrave and Hall, 1997; Hargrave et al.,Available online 28 August 2014performance in the batch otation of a copper sulde ore is discussed and modeled. Flotation experi-ments were conducted at a wide range of operating conditions (i.e. gas ow rate, slurry solids%, froth-Prediction of the metallurgical performanby image analysis and neural networks

A. Jahedsaravani a,, M.H. Marhaban a, M. MassinaeiaDepartment of Electrical & Electronic Engineering, Faculty of Engineering, Universiti PubMining Engineering Department, University of Birjand, P.O. Box 97175-376, Birjand, Ir

a r t i c l e i n f o

Article history:Received 1 May 2014Accepted 1 August 2014

a b s t r a c t

It is now generally acceptthis paper, the relationship

Minerals E

journal homepage: www.Malaysia, 43400 UPM Serdang, Selangor, Malaysia

hat froth appearance is a good indicative of the otation performance. Intween the process conditions and the froth features as well as the processs of a batch otation system

le at ScienceDirect

gineering

evier .com/locate /mineng

2.2. Image processing

138 A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145Fig. 1. Laboratory-scale batch otation cell and video camera set-up.

Table 1Input and output variables of otation experiments.

Input variables Range Output variables

Gas ow rate (L/min) 51015 Cu recovery (Rcu);concentrate grade (Gcu)Slurry solids (%) 242832

Frother dosage (ppm) 51015 Mass recovery (Rm);water recovery (Rw)

Collector dosage (g/t) 203040 Froth bubble size (Db);froth velocity (Vf)

pH 10.811.512.2 Froth color (Cf);bubble collapse rate (Crb)The gas owrate was measured by a gas owmeter and manu-ally regulated by a needle valve. The impeller speed was set at1200 rpm. The froth depth in the cell was kept at a height of2 cm during the experiment. After turning on the air, the frothlayer was formed and the concentrates were collected at timeintervals of 0.5, 2 and 5 min. The froth was allowed to freely over-ow and the concentrates were analyzed for their water, massrecovery and copper content. The tailings were ltered and driedand their copper content was determined.

A video camera was mounted on a metal structure with anadjustable arm allowing lateral and vertical adjustment (as shownin Fig. 1). The distance from the top of the cell to the video cameralens was 20 cm. Lighting was provided by a single 50W halogenlamp next to the camera, as indicated in Fig. 1. The video and met-allurgical data collected until 2 min were compared at differentexperiments. 25 frames per second and 3000 frames per each testwere captured and analyzed individually and the mean value ofeach feature was reported for each run (Moolman et al., 1995).

The otation experiments were conducted at different operat-ing conditions and concentrate copper grade (Gcu), copper recovery(Rcu = CGcu/FFcu1), mass recovery (Rm = C/F), water recovery (Rw = Cw/Cf

2) as well as the froth features were measured and reported foreach test. The operating conditions and the range of variables uti-lized in the otation experiments are listed in Table 1.

In view of the large number of variables involved and their pos-sible interactions, a fractional factorial design was used to reducethe number of experiments and determine the most importantphysical and chemical parameters (Bradshaw et al., 1992). Overall,81 runs were selected to be performed based on a fractional facto-rial design proposed by Statistica software.

1 C: mass of concentrate (g); F: mass of feed (g); Fcu: feed Cu grade (%); Gcu:concentrate Cu grade (%).2 Cw: mass of water in concentrate (g); Cf: mass of water in feed (g).The most signicant froth properties including bubble size dis-tribution, froth color, froth velocity and bubble collapse rate wereextracted from the images in each experiment. Some efcientimage processing algorithms were developed to quantify these fea-tures. Note that the bubble size distribution and froth color are sta-tic variables which are computed from a single image while thefroth speed and bubble collapse rate are dynamic variables whichare calculated from an image pair. In practice, both static anddynamic froth features should be computed in an on-line controlsystem.

2.2.1. Bubble size distributionIt has been demonstrated that the bubble size (Db)3 at the froth

surface is strongly related to the operating conditions and the pro-cess performance (Moolman et al., 1996a, 1996b). Various tech-niques developed for bubble size measurement includesegmentation (Cipriano et al., 1998; Mehrshad and Massinaei,2011; Sadr-Kazemi and Cilliers, 1997; Wang et al., 2003), texturespectrum (Nguyen and Thornton, 1995), wavelet texture analysis(Liu et al., 2005), modied 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 advanta-ges and disadvantages.

In the current study, a marker-based watershed algorithm wasdeveloped to quantify the bubble size distribution (Jahedsaravaniet al., 2014). In this method, three sets of markers were extractedfrom the pre-processed images and then the bubble edges weredetected using a watershed transform. More details of the devel-oped algorithm can be found in Jahedsaravani et al. (2014). Fig. 2shows segmentation results of some sample froth images takenat different process conditions. The results indicate that the pro-posed algorithm is capable of accurately detecting bubbles of dif-ferent sizes, which is often problematic.

2.2.2. Froth velocityFroth velocity (Vf)4 can be quantied by tracking the bubbles

movement in consecutive frames. Block matching (Forbes, 2007),pixel tracing (Holtham and Nguyen, 2002) and bubble tracking(Botha et al., 1999) are the most commonly used techniques to mea-sure the froth velocity. In this work, the block matching algorithmwas employed to estimate the froth velocity.

Because of varying froth velocity at different parts of the cellsurface, two parallel blocks were chosen. In this algorithm, rstlytwo blocks are selected in the rst frame (as source blocks) andthen neighboring region (i.e. where the target block will besearched there) is determined in the next frame in accordancewith the maximum froth displacement recorded in the database(i.e. 20 pixels) (see Fig. 3). Red5 rectangles in Fig. 3 represent thesource and target blocks in the rst and second frames, whilegreen rectangle shows the neighboring region. Finally, the sourceblocks are searched in the dened neighboring region in the sec-ond frame, assuming the froth movement direction is towardsthe cell lip.

The searching process is a critical stage in a block matchingalgorithm which may lead to wrong estimation of the bubble dis-placement. Hence, the similarity between the source and targetblocks (which is assumed to be in the neighboring region) is calcu-lated by the two dimensional discrete cross correlation as:

3 Bubble size (pixel): number of pixels forming diameter of bubble.4 Froth velocity (pixel/s): rate of bubble displacement in successive frames per unittime.5 For interpretation of color in Fig. 3, the reader is referred to the web version of

this article.

Fig. 2. Segmentation of froth images by the developed marker-based watershed algorithm.

First frame Second frame

Fig. 3. Froth velocity measurement by the block matching algorithm.

A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145 139

Fig. 4. Quantication of color feature from the froth images. (For interpretation of the rethis article.)

Table 2Correlation coefcient between color channels and metallurgical performances.

Metallurgical performances Color channels

R G B

Rcu 0.48 0.45 0.37Gcu 0.6 0.53 0.39Rm 0.62 0.58 0.43Rw 0.65 0.59 0.45

Fig. 5. Bubble collapse algorithm: (a) source block image (s1), (b) translated block imdisappearing bubbles.

140 A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145Ci; j XM1

m0

XN1m0Am;nBm i;n j

0 6 i < 2M 1 & 0 6 j < 2N 1 1

where A and B are the source and target blocks respectively, the

ferences to colour in this gure legend, the reader is referred to the web version ofblock size ism n (240 135), and the bar over B denotes complexconjugation. The maximum value of matrix C shows how similar thesource and target blocks are. The peak point among the maximumvalues is an indicator of the froth movement (see Fig. 3). Sometimesno froth motion is detected between two successive frames (e.g. at

age (s2), (c) difference image (sb) and (d) number and location of appearing and

Table 3Input and output variables of modeling procedure.

Inputs

Variables Range Mean

Db (pixel) 12.8729.86 17.46 3.5Vf (pixel/s) 38.67250.7 115.35 45.83Cf (intensity) 92.1119.07 106.42 6.9Crb 0.293.05 1.25 0.59

Table 4Correlation matrix between process and image variables.

Process variables Image variables

Db Vf Cf Crb

Gas ow rate 0.29* 0.68* 0.22* 0.31*

pH 0.64* 0.35* 0.64* 0.65*Frother dosage 0.28* 0.25* 0.10 0.15Collector dosage 0.16 0.15 0.09 0.00Slurry solids% 0.07 0.17 0.09 0.12

* Signicant at 95% condence level.

Table 5Correlation matrix between metallurgical performances and image variables.

Metallurgical performances Image variables

Db Vf Cf Crb

Rcu 0.71 0.51 0.48 0.23Gcu 0.72 0.76 0.6 0.39Rm 0.69 0.88 0.62 0.43Rw 0.71 0.89 0.65 0.43

Table 6Correlation matrix between image variables.

Image variables Db Vf Cf Crb

Db 1.00 0.56 0.52 0.39Vf 1.00 0.60 0.27Cf 1.00 0.64Crb 1.00

Fig. 6. Correlation between bubble size, froth velocity and bubble collapse rate.

A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145 141low air ow rates), therefore for detecting the peak value at zeropoint of displacement, the neighboring area is dened from a pixelahead of the source block, so the width of search area is 21 pixels.

2.2.3. Froth colorIt has been accepted that the froth color (Cf)6 is an indicative of

the concentrate grade and recovery (i.e. the bubble loading) in theotation cells (Aldrich et al., 2010). The froth color can be measuredthrough extraction of the red, green and blue (RGB) values from colorimages. In the current study, the mean value of the R, G and B valueswere calculated for quantifying the froth color (see Fig. 4). Finally,the mean of R channel which has the maximum correlation withmetallurgical factors was chosen as representative of froth color(see Table 2) (Kaartinen et al., 2006). To avoid the effect of reec-tance and shadow, both extremely dark and bright intensity valueswere excluded from the calculation.

2.2.4. Bubble collapse rateBubble collapse rate (Crb)7 at the froth surface is an indicator of

froth stability. The froth stability is quantied by analyzing consec-utive frames and detecting the rate of change in the appearance ofthe images. In this work, the bubble collapse rate was calculatedfrom the difference between reectance and shadow created at thefroth surface, as a result of bubble appearing and disappearing insuccessive frames, along with the froth velocity information (seeFig. 5) (Kaartinen et al., 2006). Initially, the target block in the secondframe is sent back to the source block in the rst frame. After that,the difference between the rst frame and the translated frame iscomputed. Finally, the bubble collapse rate is calculated by applyinga threshold value followed by a size lter to eliminate the noise.

Each bubble has its own white spot where pixel intensitiesincrease almost up to 255 (in 8 bit RGB color space). Bubbleappearing process leads to white spot formation while bubble dis-appearing process leads to generation of dark regions where pixelintensities tend to 0. Assume that a bubble is collapsed in consec-utive frames so the white spot in the rst frame converts to thedark spot in second frame. If the rst image be subtracted fromthe second image then a bright region appears in location of bubblecollision. Hence, the target block (i.e. s2 obtained by the froth veloc-ity algorithm) was translated back to its rst position where thesource block (i.e. s1) is. So

s2x s2xd 2

Outputs

Variables Range Mean

Rcu 6795.65 85.91 6.23Gcu 3.7223.24 9.45 3.84Rm 2.825.76 12.82 5.11Rw 1.8445.83 21.19 10.33where s2 and s2 are the translated and target blocks respectively andd is block displacement obtained by the froth velocity algorithm.Then, the absolute difference of two images was computed as

Sb js1 s2j 3where sb is difference image. So, bright regions are appeared in theresulting image. Two features of the bright regions distinguish themfrom obtained noise in the difference image. Actually, the appeared

6 Froth color (intensity): mean of gray level values of red channel.7 Bubble collapse rate: rate of bubble collapse (appearing and disappearing

bubbles) in consecutive frames.

142 A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145bright regions are brighter and bigger than noise so a thresholdvalue (keeping the bright regions) followed by a size lter (main-taining the bigger regions) leads to noise elimination and measuringthe bubble collapse rate.

2.7. Process modeling

The relationship between the image features and metallurgicalperformances were modeled by the neural networks. The perfor-mance of the developed models was evaluated by the correlationcoefcient (R) and the root mean square error (RMSE) calculatedfrom the following expressions:

R covyi; yivaryi varyi

p 4

RMSE 1n

Xni1

yi yi2vuut 5

where yi and yi are the observed (actual) and model outputs,respectively.

Fig. 7. Correlation between image variab3. Results and discussion

3.1. Relationship between process variables, metallurgical parametersand froth features

Estimation of the metallurgical performances from the visualfroth features and feedback control of the process by manipulatingthe operating variables is the ultimate goal of a machine visionbased control system (Holtham and Nguyen, 2002; Kaartinenet al., 2006). Input and output variables employed in the modelingprocedure are given in Table 3. Inputs are the froth featuresextracted from the images and the outputs are the metallurgicalfactors measured in each experiment.

The correlation matrix between the process and image variablesis shown in Table 4. The results suggest that the most signicantprocess variables in terms of their inuence on the froth featuresare pH, gas ow rate and frother dosage. An increase in the gas owrate lead to forming more mobile and unstable froths with largebubbles owing to enhanced bubble surface area ux.

The pH is a key factor which affects the otation selectivity.More stable and runny froths with ne bubbles observed at highpH values can be related to the increased slurry viscosity as well

les and metallurgical performances.

ls Engineering 69 (2014) 137145 143A. Jahedsaravani et al. /Mineraas to the change in ionic strength of the solution (Tucker et al.,1994). Furthermore, pH is sometimes as a frother modier andsome frothers require a higher pH to retain a more lasting frothingpower (Bulatovic, 2007).

As expected, the froth mobility and stability increase in thepresence of frothers owing to their role in hindering the bubblecoalescence.

The correlation matrix between the metallurgical performancesand image variables are listed in Table 5. It should be noted that allthe correlations obtained are signicant at 95% condence level.The results indicate that the bubble size and froth velocity havethe most signicant correlations with the metallurgical factors,which is of central importance for on-line control of the otationcircuits. This is in agreement with plant experience and some com-mercially available on-line machine vision systems (Holtham andNguyen, 2002).

The correlationbetween the imagevariables are shown inTable 6and Fig. 6. All the correlations achieved are signicant at 95% con-dence level. There is a negative correlation between the bubble sizeand froth velocity and a positive correlation between the bubble size

Fig. 8. Relationship between image variables and metallurgical parameters.

Fig. 9. Structure of the developed feed forward neural network for Gcu model.

ls EnTable 7Performance evaluation of the developed neural network model.

Metallurgical performances Training data R (correlation coefcient)

Checking data Testing data

RCu 0.90 0.88 0.9GCu 0.97 0.92 0.92Rm 0.97 0.93 0.94Rw 0.97 0.95 0.95

144 A. Jahedsaravani et al. /Mineraand bubble collapse rate. The results reveal that ner bubbles aremore stable and move faster than the coarse bubbles.

Figs. 7 and 8 show the correlation between the froth featuresand the copper recovery and concentrate grade. The results indi-cate that ner bubbles and more mobile froths result in increasedcopper recovery, which causes the secondary effects of an increasein the mass and water recoveries and a decrease in the concentrategrade.

The froth color is an indicator of thequantity and typeofmineralsloaded on the bubbles. There is a negative correlation between thecolor feature and concentrate grade which is mainly due to a largeamount of gangue minerals recovered by entrainment.

Froth stability or bubble collapse rate is a function of bubbleloading and water hold-up in the froth zone.

More stable froths (with low bubble collapse rate) result in theincreased recovery and poor concentrate grade owing to moreentrained gangue particles recovered.

Fig. 10. Observed vs. predicted values of the mRMSE

Total data Training data Checking data Testing data Total data

0.89 2.52 2.9 2.9 2.640.96 1.09 1.39 1.07 1.130.96 1.26 1.38 1.94 1.40.97 2.51 2.9 3.07 2.66

gineering 69 (2014) 1371453.2. Modeling relationship between froth features and metallurgicalperformances

Neural network is a robust computational technique for model-ing of complex non-linear systems which are not easily modeledwith conventional methods (Fausett, 1994). In this work, a threelayer feed-forward perceptron neural network was employed formodeling the process. It should be mentioned that 70% of datawas randomly selected for training, 15% for checking and 15% fortesting.

Determination of the number of hidden layer neurons (HN) is akey stage in design of an effective neural network. The correctselecting the HN is essential to avoid over/under-training. In otherwords, a large number of neurons applied in the hidden layer leadto over-training, as the weight of hidden layer neurons increasesprogressively, and decreasing the generalization capability of sys-tem while few neurons employed may lead to insufcient training.

etallurgical performances for testing set.

In the current work, the number of hidden layer neurons wascomputed by a simple technique developed by Doukim et al.(2010). In this method, at rst, the number of hidden layer neuronsis computed using the binary search mode (i.e. HN = 1, 2, 4, 8, etc.)and then several networks with these values are trained and thebest-tted one (with the lowest RMSE value) is chosen. Afterwards,a sequential search in the vicinity of HN is performed in order toobtain the smallest value of HN. This method is repeated for everyoutput and the best results are reported. HN was chosen as 9, 7, 7

Bonifazi, G., Massacci, P., Meloni, A., 2002. A 3D froth surface rendering and analysistechnique to characterize otation processes. Int. J. Miner. Process. 64, 153161.

Botha, C., Weber, D., Van Olst, M., Moolman, D., 1999. A practical system for real-time on-plant otation froth visual parameter extraction, Africon, 1999 IEEE.IEEE, 103106.

Bradshaw, D., Upton, A., OConnor, C., 1992. A study of the pyrite otation efciencyof dithiocarbamates using factorial design techniques. Miner. Eng. 5, 317329.

Bulatovic, S.M., 2007. Handbook of Flotation Reagents, Elsevier Science &Technology Books.

Cipriano, A., Guarini, M., Vidal, R., Soto, A., Seplveda, C., Mery, D., Briseno, H., 1998.A real time visual sensor for supervision of otation cells. Miner. Eng. 11, 489499.

A. Jahedsaravani et al. /Minerals Engineering 69 (2014) 137145 145and 8 for Cu/mass/water recovery and Cu grade models,respectively.

The structure of developed neural network for the concentrategrade (Gcu) model is presented in Fig. 9. This network has an inputlayer (with 4 neurons), a hidden layer (with 8 neurons) and an out-put layer (with 1 neuron).

Assessment factors of the predictive accuracy of the proposedneural networks models are given in Table 7. Fig. 10 shows scatterplots of the observed versus predicted values of the metallurgicalperformances for the testing data. The results show that the devel-oped neural networks can successfully model the complicated rela-tionship between the input and output variables. Furthermore, theprocess performance parameters can be accurately predicatedfrom the froth visual features. The predicted variables can be usedas inputs to a feedback control system.

4. Conclusion

In the present paper, the relationship between the froth imagevariables (as inputs) and the metallurgical factors (as outputs) ofa batch otation process was successfully modeled using neuralnetworks. Accurate and reliable algorithms were developed formeasuring the froth characteristics including the bubble size dis-tribution, froth color, froth velocity and bubble collapse rate. Astrong correlation between the froth visual features particularlythe bubble size and froth velocity and performance factors weredetected, which is of great signicance for control purposes. Theimportance of such investigations is that a signicant contributiontowards the development of a machine vision based control systemfor industrial applications is made.

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