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ABSTRACT

This investigation has concentrated on optimizing the newlydeveloped Pansep linear screen and developing empirical models topredict the screening performance. Most empirical models developedin the past to predict the performance for industrial screens aresuitable for the vibratory screens. Understandably, these models aredeficient in predicting the performance of linear screens due to thedifference in their operating principle.

Thus, the main objective of this study has been to developempirical models suitable for linear screens to predict the partitioncoefficient for a given size fraction as a function of the aperture size.The inadequacy of the conventional model equation originallydeveloped for vibratory screen has been demonstrated and amodified equation developed to predict the partition coefficient as afunction of the normalized mean particle size (d/d50c). In addition,new model equations have been developed for linear screens topredict the screening efficiency as a function of the separation size(d50) and to predict the separation size as a function of the screenaperture. It is believed that these model equations will be useful forthe plant operators in selecting the mesh panels of correct aperturesize and in predicting the product size distribution based on theselected aperture size.

INTRODUCTION

Screening is achieved through two independent process steps,i.e., stratification of the solid material into a bed having the undersizeparticles closest to the screen surface followed by the passage ofthese undersize particles through the screen openings. Both stepsare equally important for the achievement of high-efficiencyscreening. However, there is a distinct difference in the mechanismsutilized to achieve these two steps in case of vibratory screens andthe new generation linear motion screens. In case of the former,mechanical vibration is transferred from the screen surface to thesolid particles resulting in a continuous lift and fall of solid particles onthe screen surface. The continuous upward and downwardmovements of the particles result in the formation of a stratified bedon the screen-surface with the finest particles forming the bottommost layer that is closest to the screen surface due to the well knownconsolidated trickling phenomenon. Upon reaching the screen-surface, the fine particles pass through the screen openings and

report to the underflow stream subject to their relative size. Whereas,the passage of the particles through the screen openings is notpossible for the oversize particles, the through-passage for theundersize particles is considered as a probability process. The greaterthe difference in size between the undersize particle and the screenopening, greater is the probability of particle-passage or in otherwords lesser is the probability of particle being retained on the screensurface in a single trial. Multiple trials allowed by a longer screensurface further decreases the probability of particle retention on thescreen surface or in other words increases the probability of particlepassage in vibratory screens.

Vibratory screens are widely used for coarse coal and mineralseparation; however they are rarely used for size separation at orbelow 150 micron size due to several reasons discussed in otherpublications (Buisman and Reyeneke, 2000). Cyclone classification isthe most widely used size separation process in the fine particle sizerange. The diameter of classifying cyclones varies depending on themagnitude of the separation size. Small diameter cyclones having theability of producing greater centrifugal field are used to achieve finersize separations whereas larger diameters are more suitable forcoarser size separations. In many coal preparation plants, 38 cmdiameter and 15 cm diameter cyclones are routinely used forachieving size separations at 150 micron and 45 micron, respectively.Although associated with high throughput capacity, classifyingcyclones allow misplacement of a significant amount of fine particlesto the underflow stream and thus impair the overall efficiency of sizeseparation. Several studies (Buisman and Reyeneke, 2000; Brown etal., 2000 and Mohanty, 2002) conducted in recent years havesuccessfully demonstrated more efficient fine screening performanceusing a new generation linear motion screen, known as the Pansepscreen.

The first significant application of linear screens reported is DelkorTechniks screen in South African gold industry in the year 1985(Anon, 1986 and Wills, 1997)). Over the years, Delkor Technik hasimproved the screen design to expand its range of industrialapplications jointly with the Anglo American Corporation of SouthAfrica. The Delkor screen, commonly used for pre-screening,desanding, carbon scavenging and desliming of loaded carbons isbeing developed to be used to reduce over-grinding in millingoperations (Delkor Technik, 1999). Delkor has started using theTrackmatic cloth as a carrier for fine weave screen cloth with highopen area especially suitable for fine particle screening. Linear

1 Copyright 2003 by SME

2003 SME Annual MeetingFeb. 24-26, Cincinnati, Ohio

Preprint 03-135

PERFORMANCE PREDICTION OF THE PANSEP LINEAR SCREEN

M. K. MohantyA. Palit

Southern Illinois Univ. at CarbondaleCarbondale, IL

(motion) screens of several other types (Derrick, 2002 and Osborn,2002) are used for mostly fine particle dewatering applications. Thelinear motion of these dewatering screens is provided by a vibrationmechanism unlike the linear screens used commonly for fine sizing,which utilize a head and tail pulley system to guide the mesh panelsthrough a linear path.

The Pansep screen is one such linear screen, which wasdeveloped jointly by the Particle Separation Systems and the AngloAmerican Corporation of South Africa to be used for high-efficiencyfine particle screening. The details of the constructional features ofthe Pansep screen have been reported in other publications (Buismanand Reyneke, 2000; Brown et al., 2000). As shown in Figure 1, coalslurry is introduced through a feed distributor evenly on the screensurface moving in a linear direction. A relatively thin bed of solidmaterials formed on the screen surface is subjected to water spraysfrom both top and bottom, which allows the required stratification ofthe solid particles. As in the case of the vibratory screen, greater thedifference between the particle size and the screen opening greater isthe chance of the particles being passed through the screenopenings. The probability of the particle passage is further increasedby increasing the water spray pressure up to certain extent. Inaddition, increased retention time of the particles on the screensurface caused by a slower screen speed may allow a longer periodfor the solid materials to be acted upon by the water sprays and forthe undersize particles to pass through the screen openings.Furthermore, longer retention time allows the near-size particles to besuitably oriented on the rectangular mesh screen to enable their easypassage through the screen-openings. However, decreasing screenspeed may also allow more solid material to be fed per unit time perunit screen length. This may increase the bed thickness to a criticallevel beyond which stratification and thus the overall screeningperformance may be impaired. Thus, there is a trade-off between thelinear velocity of the screen mesh panels and the mass feed rate tothe Pansep linear screen.

In light of the above discussions related to the screeningmechanisms associated with the vibratory screens and the linearscreens, it is understandable that the screening performance obtainedfrom both screen types may not be the same. Therefore, a majorityof the empirical model equations suitable for vibratory screens maynot be useful for predicting the performance from linear screens. Thisphenomenon has been investigated in this study using theexperimental data obtained from a Pansep linear screen for threedifferent coal samples as well as one iron ore sample. Due to theinadequacy of the existing empirical model (Karra, 1979), a modifiedmodel has been developed to predict the partition coefficient as afunction of the normalized particle size (d/d50c). New empirical modelequations have been developed to predict the partition coefficient for

various size fractions and the corresponding screening imperfectionand the effective separation size (d50) as functions of screen aperturesize. The models have been validated using coal and iron oresamples obtained from multiple sources.

EXPERIMENTAL

Samples

Fine coal slurry samples were collected from several processstreams of two different preparation plants treating Illinois No. 5 andMurphysboro seam coals. The process streams included the raw coalclassifying cyclone feed stream, the spiral product-sieve bend feedstream and the secondary classifying cyclone feed stream. The bulkslurry samples were collected over a period of several hours tocompensate for the temporary fluctuations in the plant feedcharacteristics and thus be representative of the feed slurry reportingto the existing size separation devices used in the plant. The slurrysample collected from the feed streams of raw coal cyclone and sievebend were used for evaluating the Pansep screen for its sizeseparation performance at 150 micron and 250 micron, respectively,whereas the secondary desliming cyclone feed slurry sample wasutilized for evaluating the size separation performance at 45 micronparticle size. The coal slurry sample obtained from the preparationplant treating the Illinois seam coals were used for the modeldevelopment tests whereas, the Murphysboro seam coals were usedfor the model validation tests. Size-by-size weight and ash distributionfor each sample used in this investigation is provided in Table 1.

Experimental Layout and Procedure

Prior to beginning the experiments, several barrels of the slurrysample are mixed in a feed sump having a capacity of nearly 4000liters shown in the experimental layout of Figure 2. The spray wateris turned on and the Pansep mesh panels are set to motion at adesired speed before introducing the feed coal slurry to the Pansepscreen. A magnetic slurry flow meter is used to monitor the volumetricslurry feed rate to the screen. Overflow and underflow samples arecollected after allowing the screen to run for a few minutes to ensurea steady state condition. Since there is an external source of water forPansep screen in the form of water sprays, the overflow andunderflow slurry are collected together in a settling tank, where theslurry is left for 24 to 48 hours to allow a complete settling of all solidparticles. Upon achieving a complete settling, a calculated amount ofsupernatant water is siphoned out of the settling tank to adjust thesolid content to the desired level. The second pump in the circuit,which is used as a spray water pump while running the experiments,



(a) (b)

Figure 1: (a) A schematic of the Pansep screen and (b) the experimental layout utilized in this investigation.

is utilized to pump the coal slurry from the settling tank to the feedtank to begin a new test series.

RESULTS AND DISCUSSION

The size separation performance of the Pansep screen wasoptimized using factorially designed experimental programs. Differentset of mesh panels having rectangular apertures of 180 x 400 micron,100 x 400 micron and 50 x 200 micron were used for achievingdesired d50c sizes of 250 micron, 150 micron and 45 micronrespectively. Feed solids content appeared to be the most significantoperating parameter influencing the screening performance whileachieving the size separation at 45 microns using mesh panels havingthe finest apertures. On the other hand, for coarser size separation,other process parameters including feed volumetric flow rate, screenlinear speed and spray angle were also found to have significanteffect on the size separation performance. The optimizedperformance from the Pansep screen was found to be significantlybetter than that of the competing conventional technologies includingsieve bend and classifying cyclone. The excellent size separationperformances obtained using mesh panels of all three differentaperture sizes are illustrated by the partition curves shown in Figure2. A simple analysis of the partition curves indicates very lowscreening imperfection values in the range of 0.20 and below at eachaperture size. The details of the factorial experimental programconducted to optimize the Pansep screen have been discussed inanother publication (Mohanty, 2002).

Development of Empirical Models

Numerous studies have been conducted in the past to developtheoretical models to predict the partition functions and thus theproduct size distribution. Ferrara and Preti developed a screeningmodel (Ferrara and Preti, 1975) for vibrating screen and subsequentlyvalidated the model using laboratory and pilot scale screening data

(Ferrara et al., 1988). This screening model developed for dryscreening has been subsequently reviewed by several otherinvestigators (De Pretis et al., 1977; Schena, 1982; Kelly andSpottishwood, 1982; Hess, 1983; Herbst and Obald, 1984) and foundto be a very accurate model. Karra (1979) developed an empiricalmodel for a double deck vibratory screen, which is also useful forconducting simulations with wet screening operations over a very



Table I: Size-by-size distribution of weight and ash percentage of the coal samples used in this investigation

Figure 2: Partition curves corresponding to the best screeningperformance achieved from the Pansep linear screen using meshpanels of three different aperture sizes.

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wide particle size range. From one of the recent books (King, 2001),it appears that Rosin Rammlers empirical model is similar to Karrasequation. In the current investigation, it was intended to developsimilar model equations for predicting the performance from a linearscreen, which are lately being commercialzed for fine particlescreening. Prior to developing new empirical model equations for thelinear screens based on the Pansep linear screen data, it was desiredto investigate the suitability of Karras equation for linear screenapplications. Mathematically, Karras equation is described as follows:

Ci = [1-exp {-0.693(di/d50)5.846}] [1]

where Ci, the oversize partition coefficient in fraction for ith size

fraction and di, mean particle size in microns. This equation wascompared to the partition trend generated by nearly 400 data pointswhich include nearly 330 data points generated from fine coalscreening using three different aperture sizes as well as 70 iron orescreening data points supplied by the then equipment vendor(Reyeneke, 2000). As revealed from the scatter diagram and theresidual plot in Figures 3 (a) and (b), respectively, the Karra modelequation does not satisfactorily represent the linear screen datapoints. It must be realized that some amount of data scatter isexpected due to the unavoidable effects of particle shape andrectangular mesh. However, the deviation of the data points from thecurve representing the Karra model is excessively high beingrevealed by the large residual values ranging from 0.4 to +0.5indicated in Figure 3 (b). It may also be observed that the deviation issignificantly greater in the finer particle size range. In spite of thedeviation, the shape of this curve and hence this type of exponentialequation appear to provide a similar trend as the data points.Therefore, a non-linear regression analysis was conducted using astatistical software package to develop a similar exponential equation(SIU Model), to fit an appropriate model to the aforementioned 400data points. The resulting model equation can be stated as follows:

PNi = [1-exp {-0.7838 (di/d50)2.773}] [2]

where PNi is the oversize partition coefficient corresponding to the ith

size class. The corresponding scatter-diagram and the residual plotsare shown in Figures 4 (a) and (b), respectively. The adjustedstatistical coefficient of determination (R2) of this regression fit is morethan 0.97, which means 97% of the variability in the partition data isexplained by the exponential model equation and only 3% is due tothe error in the model. The total sum of square error was reducednearly 2.5 times in comparison to the Karra model fit.

In addition, the majority of the residual data points were inside the 0.2 partition coefficient band for the SIU model indicating the greaterpreciseness of the model-prediction.

It must be realized that for fine particle screening, there is asignificant difference between the magnitude of effective d50 size andthe size of the aperture size unlike the coarse particle screening.Therefore, both Karra and SIU Models may be useful in predicting theoversize or undersize partition coefficients and thus the overallproduct size distribution for only coarse particle screening, in whichcase the effective d50 cut point of the size separation may be fairlyclose to the aperture size. However in fine screening, a differentmodel equation as a function of screen aperture size instead of d50size may be more useful to predict the partition coefficientscorresponding to individual size classes. More than 300 partition datapoints generated from fine coal screening tests using coal samplesoriginating from two different coal seams and mesh panels of threedifferent aperture sizes have been utilized to develop a best-fitequation for determining the oversize partition coefficient. A nonlinear,step-wise regression analysis has been conducted to generate thebest-fit model equation, which may be mathematically described asfollows:

[3]

where PNi, oversize partition coefficient corresponding to the ith size

fraction; A, mean aperture size in micron; di, mean particle size for the



Figure 3: Comparison of the actual partition data with that predicted using Karra (1979) Model.

(a) (b)

ith size fraction. The adjusted R2 for this model-fit is nearly 0.98,which testifies the goodness of fit of this exponential equation. Clearly,this model equation provides a convenient approach to predict thesize distribution of the screen oversize and undersize products basedon the aperture size selected for the mesh panels.

The goodness of this regression model fit is also exhibitedgraphically by the scatter diagrams and residual plots prepared foreach aperture size studied in this test program. As shown in Figure 5(a), for the rectangular aperture of size 180 m x 400 m having ageometric mean size of 268 m, the model equation fits satisfactorily

to the experimental partition coefficient data corresponding to theindividual size fractions. The residual analysis data plotted in Figure 5(b) indicates a maximum difference of less than 0.2 between theexperimental and the model-predicted partition data. Nearly 90% ofthe 30 experimental data points are within the 0.1 partitioncoefficient band of the model-predicted data. It must be realized thatmany data points are not visible in the plot for being overlapped withone another. For example, although it appears like one data point,there are actually 6 data points corresponding to the mean particlesize of 9.38 m in Figure 5 (a). Figures 6 (a) and (b) illustrate the



Figure 4: A non-linear regression equation representing the best-fit model (SIU Model) for predicting the partition data and theassociated residual plot.

(a) (b)

Figure 5: Comparison of the actual experimental partition data and the partition curve predicted using the new model equationdeveloped for the mean aperture size of 268 m.

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(a) (b)

goodness of fit of the new model for the aperture size of 100 m x 400m having a mean aperture size of 200 m utilizing 125 experimentaldata points. The entire data set is within +0.08 and 0.06 partitioncoefficient band of the model-predicted data. Similar comparison ofexperimental data and the predicted partition data is illustrated inFigure 7 (a) for an aperture size of 50 x 200 m having a mean sizeof 100 m. The experimental data consisting of 150 partition datapoints are within + 0.06 to 0.07 partition coefficient band of themodel-predicted data as indicated in Figure 7 (b).

Model Validation

The empirical models stated in Equations 2 and 3 were developedduring this investigation to predict the oversize and undersize partitioncoefficients as a function of the d50 and the aperture size,respectively. It was desired to further validate these empirical modelsusing test data generated from new series of experiments utilizingcoal slurry samples originating from another coal seam, i.e.,Murphysboro seam. The SIU-model stated in Equation 2 is validatedat two different d50 sizes as shown in Figure 8 (a). In total, 186



(a) (b)


(a) (b)


partition data points were generated from the Pansep screeningconducted using two different coal slurry samples and two sets ofmesh panels. Partition coefficients corresponding to individual sizefractions were also predicted using the SIU-model of Equation 2 to becompared with the experimental partition data as shown in Figure 8(a). The proximity of majority of the data points to the diagonal verifiesthe validity of the SIU-model equation.

Equation 3 states an empirical model, which is believed to bemore useful for the plant operators from a practical point of view. In areal-life situation, the plant operators may like to have a reliable modelso that they may predict the size distribution of the overflow andunderflow of a screen based on the aperture size they use. Thus, itwas desired to check the validity and reliability of the model equationsutilizing coals slurry samples originating from different seamsscreening at multiple aperture sizes. In addition, a set of iron ore dataobtained from the equipment vendor was also utilized to check theutility of this model equation for other minerals. In total, 222 partitiondata points were generated from the Pansep screening conductedusing fine coal slurry samples originating from two different coalseams and mesh panels of two different aperture sizes, i.e., 100 mx 400 m and 50 m x 200 m. Another set of 63 partition data pointswas obtained from iron ore screening using mesh panels of aperturesize 200 m x 80 m. Oversize partition coefficients corresponding toindividual size fractions were also predicted using the empirical modelof Equation 3 to be compared with all 285 experimental partition datapoints as shown in Figure 8 (b). Although there is some scatter in thedata, the diagram reveals a satisfactory comparison. It is believed thatthe nature of different coal/mineral samples, their size distribution andcharacteristic shapes may account for the scatter in the data.However, it is believed that the model predictions will be reliable withinan acceptable level of error to be used by the plant operators.

A very useful application of this model in a real-life situation maybe in the selection of the aperture of the correct size for producing adesired oversize or undersize product from a feed material of a givensize distribution. Equation 3 may simply be written as

where, PNi, over size partition coefficient corresponding to ith size

fraction; A, geometric mean aperture size; di, geometric mean particlesize of the ith size fraction; fitting constants: a, -0.0546; b, 4.2604.Therefore,

By plugging in the values a and b in the above equation, the followingrelationship is obtained:



(a) (b)

Figure 8: Comparison of the model prediction and the partition number calculated using the validation test data.

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The above equation will allow the production of the type of plotsshown in Figure 9, which will be extremely useful for the plantoperators in the selection of suitable aperture size based on thedesired size distribution of the product. Further manipulation ofEquation 4 will produce new expressions for d50 cut-size and theimperfection (I) of screening as functions of aperture size:

Understandably, Equations 5 and 6 can be used for the predictionof screening efficiency over the fine aperture size range investigatedduring this investigation. Some error in model-prediction is expectedfor other minerals due to their characteristic particle shapes and alsothe rectangular apertures used in fine screening applications.

CONCLUSIONS

The conclusions obtained from this Pansep linear screen studymay be summarized as follows:

The existing fine screening model (by Karra and RosinRammler), which were originally developed for vibratory screens, hasbeen found to be deficient in predicting the performance from a linearscreen. A more suitable empirical model, named as SIU Model, hasbeen developed using nearly 400 experimental partition data andvalidated to predict oversize partition coefficient as a function of thenormalized mean particle size (d/d50).

For fine particle screening, a new empirical model has beenproposed to predict oversize and undersize partition coefficients andthus the product size distribution for a given feed size distribution asa function of the screen aperture size. A total of 285 validation testdata obtained from fine coal and iron ore screening comparefavorably with the model-prediction. Some of the errors in the modelprediction may be attributed to the effect of particle shape notconsidered while formulating the model equation.

The new model equations developed for predicting the

screening imperfection and separation size (d50) may significantlyhelp the plant operators in selecting the correct aperture size toproduce a desired oversize or undersize particle size distribution.

ACKNOWLEDGMENTS

The author sincerely acknowledges the funds provided by theOffice of Coal Development of the Illinois Department of Commerceand Community Affairs for this investigation under therecommendation of the Illinois Clean Coal Institute. In addition, theauthor greatly appreciates the technical guidance and supportprovided during the course of this investigation by Mr. Rein Buisman,Mr. Kobus Reyneke and Mr. Shelby Akers of the then Pansep group.

REFERENCES

Anon., New linear screen offers wide applications, Engineeringand Mining Journal, September, 187: 79 (1986).

Brown, J. V., Buisman, R., Imhof R. M., Cost savings Potentials ofPansep Screening Technology, Proceedings, Major Trends inDevelopment of Sulfide Ores Up-Grading in the 21st century, Norilisk,April 24-28 (2000).

Buisman Rein and Kobus Reyneke, Fine Coal Screening Usingthe New Pansep Screen, Proceedings, 17th International CoalPreparation Conference, Lexington, Kentucky: 71-85 (2000).

Delkor Technik, www.delkor.co.za (1999).De Pretis, A., Ferrara, G., Gaurascio, M. and Preti, U., A new

approach to screen design, Proceedings, 12th IMPC, Sao Paulo,Brazil (1977).

Derrick, www.derrickcorp.com (2002).Ferrara, G. and Preti U., A contribution to screening kinetics.

Proceedings, 11th IMPC, Calgiari, Italy (1975).Ferrara, G., Preti U. and Schena G. D., Modeling of Screening

Operations, , International Journal of Mineral Processing, 22: 193-222(1988).

Herbst, J. A. and Obald, A. E., A population balance model forscreening. Proceedings, 9th Powder in Bulk Solids Conference,Chicago, Illinois (1984)

Hess, F., Mathematical modeling of screen and related units for



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iner

Figure 9: Model Simulation results to help select the appropriate aperture size for fine particle screening.

plant simulation, Ph. D. thesis, University of Queensland, Australia(1983)

Karra, V. K., Development of a model for predicting the screeningperformance of a vibrating screen, CIM Bulletin, April: 167-171(1979).

Kelly E. G. and Spottiswood D. J., Screening and Sieving,Introduction to Mineral Processing, John Wiley & Sons, Inc., Canada:169-198 (1982).

King R.P., Modeling and Simulation of Mineral ProcessingSystems, Butterworth Heinemann, Chapter 4: 89 (2001).

Mohanty, M. K., Fine coal screening performance enhancementusing the Pansep screen, International Journal of Mineral Processing,in press, (2002)

Osborn, www.osborn.co.za (2002).Reyneke, K., Waltech group, Personal communication (2000).Schena, G. D., The processing of indutrial screening data, A

modeling approach. Internal Report, 1st Miniere e Geofisica Appl.,Universita di Trieste. (1982).

Wills, B. A., Industrial Screening, Mineral Processing Technology,Butterworth Heinemann, Chapter 8: 177-191 (1997).



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