ABSTRACT ENHANCED CLASSIFYING CYCLONE PERFORMANCE FOR ULTRAFINE PARTICLE SEPARATIONS
The conventional techniques for ultrafine particle classification are plagued by inherent inefficiencies, which limit the ability to achieve efficient size separations. A major contributing factor to this problem is the significant amount of by-pass of submicron particles to the coarse particle product stream as result of particle entrainment in the water reporting to this fraction. Past studies have shown that increasing feed solids concentration or apex diameter reduces by-pass. However, both of these measures result in increased particle separations sizes and elevated viscosity effects, which have a negative impact on efficiency. This study focused on ways to improve classifying cyclone performance by improving classification efficiency at such fine p article sizes The addition of viscosity modifiers at high solids concentrations improved classification efficiency at decreased cutsizes and low ultrafine by-pass. Furthermore, it was discovered that viscosity modifiers had the potential of eliminating roping conditions in classifying cyclones thereby improving classification efficiency by up to 200%.
In-plant studies were carried on a two-stage classifying cyclone circuit at an Eastern Kentucky coal mining operation. The goal of this study was to achieve a low ash clean coal product. The circuit and test conditions emphasized the achievement of ultrafine cutsizes ( D50) while reducing the amount of hydraulically entrained material using 15-cm (6-in) gMax classifying cyclones. The classification circuit achieved particle size separations in the range of 25 – 50 50 microns while limiting ultrafine by-pass to values less than 10%. The classification efficiency was excellent as indicated by a typical alpha value of 2.75. As a result, the ash content was reduced from around 50% to values in the range of 22% to 30% with a mass recovery of about 30%, which equates to 81% rejection of the ash forming mineral matter. Further reductions in the coarse product ash content were limited due to a density effect and the remaining presence of a significant quantity
ABSTRACT ENHANCED CLASSIFYING CYCLONE PERFORMANCE FOR ULTRAFINE PARTICLE SEPARATIONS
The conventional techniques for ultrafine particle classification are plagued by inherent inefficiencies, which limit the ability to achieve efficient size separations. A major contributing factor to this problem is the significant amount of by-pass of submicron particles to the coarse particle product stream as result of particle entrainment in the water reporting to this fraction. Past studies have shown that increasing feed solids concentration or apex diameter reduces by-pass. However, both of these measures result in increased particle separations sizes and elevated viscosity effects, which have a negative impact on efficiency. This study focused on ways to improve classifying cyclone performance by improving classification efficiency at such fine p article sizes The addition of viscosity modifiers at high solids concentrations improved classification efficiency at decreased cutsizes and low ultrafine by-pass. Furthermore, it was discovered that viscosity modifiers had the potential of eliminating roping conditions in classifying cyclones thereby improving classification efficiency by up to 200%.
In-plant studies were carried on a two-stage classifying cyclone circuit at an Eastern Kentucky coal mining operation. The goal of this study was to achieve a low ash clean coal product. The circuit and test conditions emphasized the achievement of ultrafine cutsizes ( D50) while reducing the amount of hydraulically entrained material using 15-cm (6-in) gMax classifying cyclones. The classification circuit achieved particle size separations in the range of 25 – 50 50 microns while limiting ultrafine by-pass to values less than 10%. The classification efficiency was excellent as indicated by a typical alpha value of 2.75. As a result, the ash content was reduced from around 50% to values in the range of 22% to 30% with a mass recovery of about 30%, which equates to 81% rejection of the ash forming mineral matter. Further reductions in the coarse product ash content were limited due to a density effect and the remaining presence of a significant quantity
of high-ash slime material in the coarse product. The typical D50 for the coal was 40 microns while the estimated estimated value for mineral matter matter was 17 microns. As a result, the ash contents for all particle size fractions below 75 microns increased in the cyclone underflow streams. The ash content increase in the -25 micron fraction from 61% to 84% indicates that true classification is achieved on a portion of the fraction rather than the common opinion that 100% of the fraction reports as a function of water recovery to the cyclone output streams
TABLE OF CONTENTS
LIST OF FIGURES ...........................................................................................................5 LIST OF TABLES .............................................................................................................8 1.1
Background ....................................................................................................10
1.1
Goals and Objectives .....................................................................................15
1.3 Thesis Organization .............................................................................................16 2. LITERATURE REVIEW: ULTRAFINE SIZING ...................................................17 2.1 Screening ..................................................... .................................................... ......17 2.1.1 Fundamentals ..............................................................................................17 2.1.2
Techniques ............................................................................................19
2.1.3 Industrial Practice ......................................................................................25 2.1.3
Problems ...............................................................................................29
Classification ..................................................................................................29
2.2
.1 Fundamentals .......................................................................................29
2.2
2.1.2 Techniques ...................................................................................................31 2.1.3 Industrial Practice ......................................................................................47 2.2.4 Problems ......................................................................................................50 3. EXPERIMENTAL ..................................................... ..................................................54 3.1 Laboratory Classifying Circuit .................................................... .......................54 3.2
In-Plant Classifying Circuit ..........................................................................57
3.3
Classifying Efficiency Determination ..................................................... ......60
3.4
Coal Characteristics .......................................................................................61
3.5
Viscosity Modification Tests .........................................................................63
4. RESULTS AND DISCUSSION ..................................................................................66 4.1 Classifying Cyclone Parameter Evaluation .......................................................66 4.2 Classifying Circuit Evaluation ..................................................... .........................4 4.2.1
Operation Condition Effects .................................................................4
4.2.3 Coal and Mineral Matter Partitioning ......................................................95 4.2.4 Circuit Modeling .......................................................................................100
4.2.5 Viscosity Effects ......................................................................................107 5. CONCLUSSIONS AND RECOMMENDATIONS ................................................119 5.1 Conclusions ..................................................................................................... ....119 5.2 Recommendations for Future Work ................................................................122 References .......................................................................................................................124
LIST OF FIGURES
Figure 1.1 Flowsheet of a Conventional fine coal cleaning circuit. ..................................11 Figure 2.1 Schematic of the Krebs Varisieve. ...................................................................21 Figure 2.2 Schematic of the Derricks Multifeed System. ..................................................22 Figure 2.3 Schematic of the Derrick Stack Sizer. ..............................................................23 Figure 2.4 Schematic diagram of the Pansep screen..........................................................25 Figure 2.5 Flowsheet of a fine coal cleaning circuit, which uses Derrick Multifeed screens. .................................................................................................... ...............27 Figure 2.6 Flowsheet of a fine coal cleaning circuit, which uses Derrick Stack Sizers. ...28 Figure 2.7 Force balance diagram of a particle settling at terminal velocity. ....................30 Figure 2.8 A schematic diagram of a continuous Knelson Concentrator ..........................34 (Honaker et al , 2005) .........................................................................................................34 Figure 2.9 Classification performance curves achieved by the Knelson Concentrator (Honaker et al , 2005) .............................................................................................35 Figure 2.10 Schematic diagram of the Counter-flow centrifugal classifier .......................38 (Heiskanen, 1996) ..............................................................................................................38 Figure 2.11 Schematic diagram of the TU Clausthal classifier (Heiskanen, 1996) ...........38 Figure 2.12 Schematic diagram of the Krebs gMax cyclone (Krebs, 2003) ......................40 Figure 2.13 Schematic diagram of the Krebs Cyclowash (Krebs, 2003) ...........................45 Figure 2.14 Schematic flowsheet of a fine coal classifying circuit. ..................................48 Figure 2.15 Schematic diagram of a fine coal classifying circuit. .....................................49 Figure 2.16 Schematic illustration of the operating principles of the Falcon Concentrator (Honaker et al , 1996) .............................................................................................52 Figure 3.1 Schematic diagram of the experimental set-up used in the laboratory tests.....55 Figure 3.2 Schematic flowsheet of the two-stage Classifying cyclone circuit set-up. ......58 Figure 4.1 Comparison of the experimental and empirical models for cutsize(D50). ..........1 Figure 4.2 Comparison of the experimental and empirical models for ultrafine by-pass....1 Figure 4.3 Comparison of the experimental and empirical models for imperfection. .........2 Figure 4.4 Simulated results for cutsize. ............................Error! Bookmark not defined.
Figure 4.5 Simulated results for ultrafine by-pass. ...................................................... ........2 Figure 4.6 Simulated results for imperfection. ..................Error! Bookmark not defined. Figure 4.7 Performance curves showing the effect of apex diameter on classifying cyclone performance. ...............................................................................................6 Figure 4.8 Performance curves showing the effect of solids concentration on classifying cyclone performance. ...............................................................................................7 Figure 4.9 Partition curves for the two-Stage circuit with 1.27 cm apex in both primary and secondary classifying cyclones. ......................................................................84 Figure 4.10 Performance curves for two-stage classifying cyclone with 0.635 cm apex in primary and secondary cyclone. ..................................................... .......................90 Figure 4.11 Performance curves for different two-stage classifying cyclone circuit configurations at different dilution levels. .............................................................94 Figure 4.12 Coal and mineral matter size separation achieved by the classification circuit according to the (a) Actual and (b) Corrected performance curves; primary apex = 0.635cm. ....................................................... .................................................... ......97 Figure 4.13 Performance curves of the two-stage circuit models. ...................................104 Figure 4.14 Three Stage Circuits with (a) No Recycle (b) Recycle (c) Countercurrent. .105 Figure 4.15 Performance curves of the three-stage circuit models. .................................106 Figure 4.16 Effect of viscosity modifier addition on the classification performance achieved with a feed solids content of 5% by weight and a relatively large apex diameter................................................................................................................109 Figure 4.17 Effect of viscosity modifier addition on the classification performance achieved with a feed solids content of 7.5% by weight and a small apex diameter.110 Figure 4.18 Interactive effect of modifier concentration and apex diameter on the separation cutpoint; feed solids concentration = 7.5% by weight. ......................112 Figure 4.19 Interactive effect of modifier concentration and apex diameter on the imperfection ( I ) value; feed solids concentration = 7.5% by weight. ..................112 Figure 4.20 Partition curves for two different feed solids concentration with and without addition of sodium dodecyl sulfate. .....................................................................115
Figure 4.21 Partition curves for two different feed solids concentration with and without addition of NALCO 9762 viscosity modifier. .....................................................115 Figure 4.22 Feed viscosity effect on the particle size cutpoint. .......................................116 Figure 4.23 Effect of Sodium dodecyl sulphate on the flotation rate of a West Virginia coal sample...........................................................................................................117 Figure 4.24 Effect of NALCO 9762 on the flotation rate of an Illinois coal sample. .....118
LIST OF TABLES
Table 1.1 Size by size analysis results of a fine coal waste slurry sample on a dry basis. 13 Table 2.1 Test conditions for the particle size separation experiments with the Knelson fluidized bed...........................................................................................................35 Table 2.2 Comparison of conventional classifying cyclone and Cyclowash test results...46 Table 2.3 Typical probable error values and specific gravity cut points achieved by various enhanced gravity concentrators for the treatment of 210 x 37-µm size fraction of various coal samples (Honaker et al, 1996). ........................................53 Table 3.1 Dimensions and operating conditions used in the study for the primary and secondary classifying cyclones. ...................................................... .......................59 Table 3.2 Particle size analysis of the phase 1 and 2 laboratory classifying cyclone tests feed. .................................................... ..................................................... ...............62 Table 3.3 Particle size analysis of Phase 3 laboratory classifying cyclone tests feed. ......62 Table 4.1 Test results obtained from the experimental program evaluating the effect of Apex diameter, modifier addition and feed solids on Classifying cyclone performance. ............................................................................................. .............67 Table 4.2 Statistical evaluation of the experimental error realized from the Classifying Cyclone parametric test..........................................................................................69 Table 4.3 ANOVA table generated for the response variables associated with the laboratory classifying cyclone tests. ......................................................................71 Table 4.4 Classification performance models and associated model fitness values ..........74 Table 4.5 Laboratory classifying cyclone Test Results .......................................................8 Table 4.6 Two-stage classifying cyclone circuit with no secondary cyclone feed dilution data. ........................................................................................................................83 Table 4.7 Two-stage classifying cyclone circuit with secondary cyclone feed dilution data. .................................................... ....................Error! Bookmark not defined. Table 4.8 Two-stage classifying cyclone circuit with secondary cyclone feed dilution and no recycle data. ......................................................................................................89
Table 4.9 Particle size-by-size analysis data of overall circuit process stream samples obtained using an apex diameter of 0.635 cm in the primary and secondary classifying cyclones with no recycle......................................................................91 Table 4.10 Particle size-by-size analysis data of overall circuit process stream samples obtained using an apex diameter of 0.635 cm in the primary and secondary classifying cyclones with recycle...........................................................................92 Table 4.11 Summary of classification circuit performances achieved under various operating conditions. ..................................................... .........................................95 Table 4.12 Summary of classification circuit performances achieved for coal and mineral matter under various operating conditions. ...................................................... ....100 Table 4.12 Summary of the results obtained from classifying cyclone circuit modeling.106
INTRODUCTION
1.1 Background
The processing of fine coal is an important part of the overall plant recovery process. This has however not always been the case. Past practices involved sending the entire fine fraction to a thickener for dewatering and then pumping it out to a slurry impoundment as plant waste. This was done because it was considered uneconomical to recover this fraction. In addition, moisture concerns associated with the fine coal provided little incentive for fine coal recovery. Since then, increased mechanization fueled by the need for higher production rates to stay profitable has resulted in the generation of higher quantities of fine coal. Improvements in fine particle separation technologies such as flotation, sizing and gravity methods coupled with more efficient dewatering techniques have made fine coal recovery a more attractive option. Furthermore, permitting requirements for the construction of impoundments have become increasingly stringent making it necessary for mine operators to reduce the volume of solid material to be impounded. Thus, improved fine particle recovery achieves the dual purpose of increasing the quantity of saleable coal produced as well as freeing available impoundment space for tailings disposal.
Figure 1.1 shows a schematic flowsheet for fine coal processing. Feed to the fine circuit generally consists of -1mm particle size material, which is usually fed to a classifying cyclone to achieve a 0.15 mm size cut.
-150 m -1mmFeed
Froth Flotation
O/F
Flotation Reject Flotation Product
Classifying Cyclone
Circuit Product
1 x 0.15mm
Screen Bowl Centrifuge
U/F
Spirals Concentrator
Thickener Spiral Reject Spiral Product O/F
Feed U/F
+25µm Clean Coal Product
Circuit Reject
Figure 1.1 Flowsheet of a Conventional fine coal cleaning circuit.
The nominal 1 x 0.15 mm material is treated by spirals while the -0.15 mm fraction is recovered by froth flotation. The flotation and spiral product combine to form the circuit product that is dewatered using a screen bowl centrifuge. The spiral tailings are screened and the underflow combined with the flotation tailings to make up the circuit rejects. This is then dewatered using a thickener.
A common occurrence in the Central Appalachian coal field in the U.S. is the existence of a relatively low ash material in the 150 x 37 m particle size fraction reporting to the fine coal circuit of operating preparation plants. Particle size analysis results of a typical fine coal waste sample collected from an eastern Kentucky coal preparation plant are shown in Table 1.1. The plant processes coal from the Hazard No. 4 seam, which is a high-volatile bituminous coal with a dry-based heating value of around 14,200 Btu/lb. The particle size-by-size data indicates that the +37 m material has a low ash content of 5.97% and a moisture-free heating value of 13,950 Btu/lb. This fraction represents 22.4% of the total material that is currently disposed into a slurry impoundment at the eastern Kentucky operation, which equates to approximately 28 tph. Currently, the only technology capable of efficiently removing the high ash ultrafine fraction from the coal is froth flotation, which is a separation process based on surface chemistry differences rather than particle size.
Froth flotation involves injecting air into a flotation cell filled with slurry composed of a mixture of coal particles and air bubbles. The bubbles collide with the coal particles within the cell resulting in bubble particle attachment between the bubbles and the hydrophobic coal particles within the slurry.
Table 1.1 Size by size analysis results of a fine coal waste slurry sample on a dry basis.
Size Fraction (microns)
Weight (%)
Ash (%)
Total Sulfur (%)
Heating Value (Btu/lb)
+210
0.17
2.99
0.77
14520
210 x 150
0.62
3.00
0.77
14515
150 x 75
8.00
3.03
0.77
14431
75 x 44
9.26
6.39
0.74
13878
44 x 37
4.33
11.05
0.70
13101
-37
77.62
60.80
0.35
4850
Total
100.00
48.53
0.44
6886
This results in a low-density mineralized froth containing 70 to 80% air, which rises to the top of the cell as flotation product. Wash water is employed to deslime the froth and reduce hydraulic entrainment. Although flotation efficiently recovers the hydrophobic coal, it suffers from poor selectivity, which negatively affects the quality of the flotation product especially when treating mixed-phase particles. The selectivity limitation is associated with its efficient collection whereby fine particles having only 5% of its surface being hydrophobic are able to attach to the bubble surface and report as a high-ash particle into the flotation product. In addition, froth flotation requires the use of reagents, which add additional cost to the process. Collectors such as fuel oil are required to enhance the surface hydrophobicity and increase the flotation rate of coal. Frothers such as methylisobutylcarbinol (MIBC) reduce the surface tension of water enhancing bubble formation whilst modifiers such as pH regulators provide the necessary surface charge for collector adsorption. For efficient flotation, all these reagents must be added at optimum dosages.
Excessive collector addition beyond the optimum often results in higher flotation rate increases for gangue minerals than coal further reducing selectivity and may result in overly stiff froth with poor water drainage and ineffective froth washing. Furthermore, high collector dosages have a negative effect on frother performance increasing the required frother dosage. Erol et al (2003) also report the occurrence of high product ash for coal flotation at high MIBC concentrations. This was because of smaller bubble formation leading to greater water recovery and increased entrainment of high-ash particles. Furthermore, the finer bubbles generated at high frother concentrations result in hydraulic entrainment of fine high ash particles further reducing product quality. Changing coal surface properties also result in poor flotation. The normal pH for coal flotation lies within a range 5-7. Weathered coal floats poorly due to the presence of an oxide coating on the coal surface, which reduces the surface hydrophobicity of the coal particle. According to Osborne (1988) however, weathered coal can be made to float at a pH of 2 when deslimed and/or scrubbed. Thus, in addition to cost, flotation may provide complex challenges, which have to be understood in order to achieve an efficient separation.
Due to the potential for lower costs and a better understanding of the separation principles by the operators, a classification system that provides an economical and efficient ultrafine particle size separation would be preferred. Current trends in fine particle separation involve the use of small diameter classifying c yclones. These cyclones however, are unable to provide sharp size cuts at high feed rates due to the low residence time experienced by particles within the cyclone body. Inefficiencies in fine particle separation also typically occur due to Brownian motion, particle interaction effects as well as viscosity that lead to the by-pass of fine particles into the coarse product. High particle concentrations within the apex region of classifying cyclones leads to increased viscosity and hindered settling effects that negatively impact classification performance, especially at high feed solids concentrations. Minimizing these effects therefore, could make classification a viable alternative to flotation for fine particle recovery in specific applications such as those represented by the coal characteristics described in Table 1.1.
This study focuses on the enhancing the classification performance of small diameter hydrocyclones that provide the potential for enhancing the classification performance for particle size cuts in the 25-50 m size range.
1.2 Goals and Objectives
The goal of this project was to improve the efficiency of classifying cyclones for ultrafine (25µm) size separations while minimizing ultrafine by-pass. If successful, the process could be used as an alternative or compliment to flotation process. Success will be measured based on the ability to achieve a classification performance that will result in a 25 micron cutsize and less than 5% ultrafine by-pass.
To achieve the project goals the following specific objectives were developed:
Evaluate a two-stage classifying cyclone circuit using 15-cm units in an operating plant to treat -150 micron coal.
Quantify the circuit classification efficiency and particle cutpoints under different operating conditions.
Minimize by-pass by reducing water recovery to the underflow stream of the cyclones through high feed solids contents and small apex diameters.
Determine the ability of modifiers to reduce the effect of elevated viscosity values on classification performance. The higher viscosities are due to the higher solids concentrations in the feed and underflow streams.
1.3 Thesis Organization
This thesis is divided is divided into 5 (five) main chapters. Chapter 1 presents information, which provides the background leading to the research. It also presents the goals and objectives, which this study was designed to accomplish. Chapter 2 contains a literature review of the fundamentals of screening and classification as well some contemporary fine sizing techniques involving both screening and classification. Explanations for the limits of screening in ultrafine sizing are discussed. Chapter 3 describes the experimental methods and conditions under which the various tests were performed. A discussion of the experimental results is contained in Chapter 4 while Chapter 5 presents the conclusions and recommendations for future work.
2. LITERATURE REVIEW: ULTRAFINE SIZING
The two most widely used methods for sizing are screening and classification. Screening is generally used to effect separations on + 250 µm particles. Classification processes are employed for particle size separations below 250 microns where screening is impractical due to low feed capacities and efficiencies. The prevalent techniques in wet classification can be broadly grouped based on fluid movement and particle settling directions (Heiskanen, 1993). Separation depends on the ability of a particle to settle within a fluid medium. When the fluid movement is horizontal and forms an angle with the particle trajectory, the classification is referred to as sedimentation or cross flow classification. Classifiers operating under such conditions include rake, spiral, bowl and tank classifiers. Centrifugal sedimentation involves sedimentation under the influence of enhanced gravitational forces. The units include classifying cyclones and solids bowl centrifuges. Hydraulic or counterflow classification however is classification in which the fluid and the settling particle move in opposite directions. In this case, clean water is injected into the classifier to maintain the counter flow. Such classifiers include counter current classifiers, hydraulic tank classifiers, cone classifiers and fluidized bed classifiers. 2.1 Screening 2.1.1 Fundamentals
Screening is the most commonly used particle-sizing technology. It is based on a particle’s ability to successfully pass through an aperture of a particular size on a
vibrating screen. Particles with physical dimensions larger than aperture size are retained on the screen while those with dimensions less than the aperture size pass through the screen. During screening two basic processes, occur, i.e, (1) Stratification which is the process whereby large particles migrate to the top of the vibrating material bed while at the same time the fine particles sift through the
voids created by the larger particles. Without stratification, the particles would have very little chance of passing through the screen. (2) Probability of Separation, which describes the stochastic probability of a particle passing through a given aperture once presented to the opening. The ease with which a particle x passes through a screen of aperture size a depends on how much aperture area is available for particle passage. A larger difference between the particle and aperture size (a x) , results in easier screening. A particle in contact with the screen surface encounters both the aperture a and wire diameter b , represented by (a b) . The probability P of a particle passing through a screen represents the ratio of the available aperture area to the screen surface encountered as shown below: m
a x P a b
(2.1)
Equation (2.1) implies that P increases with increasing screen opening, decreasing particle size, decreasing wire diameter and increasing number of screening trials m . For fine particle screening, b becomes significantly greater than a, which results in a high probability of retaining undersize particles in the screen overflow The number of screening trials is a function of screen length, frequency and amplitude and assumes good stratification. Increasing screen length increases P by increasing m . A perfect separation could therefore be achieved by using an infinitely long screen. An increase in frequency of vibration of the screen enhances the migration of particles to the screen surface and reduces screen blinding. The amplitude of vibration also affects screening in that high amplitudes at low feed rates result in some material bouncing too high of the surface of the screen and reducing m . The ratio of the total aperture space to the total area of the screen surface is called the open area. Increasing open area increases P by presenting a greater surface area for screening.
2.1.2
Techniques
The practice of fine particle screening in industry employs a range of different techniques. However, the effectiveness of screens for fine particle separations is adversely affected by limited open area as well as blinding of the screen apertures, which increases the by-pass of undersize material to the screen overflow and thus reduces efficiency. Screening systems are generally limited to particle size separations coarser than approximately 250 µm due to these limitations associated with capacity and blinding. For example, fine particle screening has a 45% probability of retention after 10 events, whereas coarse screening has less than 1% for an x/a ratio of 0.5 using typical wire diameters. Increasing m by applying high frequency and longer screens provides efficiency improvement; however, throughput capacity remains significantly lower than coarse screens. The open area of a screen, which is the ratio of the aperture to the total area of the screen mat, determines the underflow rate. Fine coal screening mesh typically have an open area around 30% and a corresponding mass flux of 2 tph/m2, which is an order of magnitude less than coarse screens. To increase the capacity, the wire diameter can be decreased. However, screen wear rate requires an increasing amount of maintenance as the wire diameter is decreased, thereby significantly elevating costs.
Sieve bends are less subject to screen blinding than other screens and have been employed to achieve 150 µm cutsizes on feed containing up to 45% solids. Screening takes place on a concave screen surface made up of horizontal wedge-wires, which are orientated in such a way that the open slots are perpendicular to the flow of the slurry. The slurry flow across the concave surface creates a centrifugal force. This force combined with retardation due to frictional drag of the fluid in contact with the screen surface results in a shaving off the fluid through the open slots to the screen underflow. This layer may have a thickness of 0.25 to 0.5 the slot width. Wills (1988) reports that size separations down to 50µm and screen capacities up to 180 m3/h can be achieved using sieve bends. At low feed rates however, sieve bends are still susceptible to blinding. Screen blinding effectively reduces the total open area of a screen and therefore reduces efficiency by causing undersize particles to report to the overflow stream. On the
other hand, high volumetric flows typical of plant operations limit the proportion of feed solids subjected to the sizing action. As a result, the efficiency of sieve bends for screening is limited by the by-pass of -150µm material to the oversize (Osborne, 1988). In addition, screen wear deteriorates screening performance by reducing the achievable separation size. Screen blinding, lack of open area due to build-up of material, high maintenance cost and mesh wear commonly result in sub-par performance for the sieve bends (Buisman and Reyneke, 2000).
The Krebs Varisieve (Figure 2.1) is a modified sieve bend design that improves on the performance of traditional sieve bends for fine particle screening. The Varisieve incorporates a manually adjustable head box feed throat as well as a variable position screen frame into the traditional sieve bend design. The modified feed head box regulates the feed rate to the Varisieve in such a way that flow rate fluctuations on the plant do not adversely affect screen performance. As a result, the low feed rates that lead to screen blinding and high feed rates, which in turn, result in feed box overflow and insufficient particle screening time, are avoided. The variable screen frame reduces the effects of screen wire wear by allowing the gradual lowering of the screen frame to expose new sharp screen wires during operation. Previous test work performed by Mohanty et al (2002) showed that the Varisieve could be used to achieve size separations in the 138µm size range with an imperfection of 0.15. However, this imperfection value was still accompanied by a significant by-pass of 22 % of fine ash material, which typically reported along with the sieve bend overflow, thereby significantly lowering the product quality. High frequency vibrating screens have also been employed to reduce the problems of screen blinding and low capacity typically associated with fine particle screening by allowing maximum repeated contact of particles with the screen surface. This increases the probability of fine particle passage through the screen thus improving screening efficiency.
Figure 2.1 Schematic of the Krebs Varisieve.
Vibrating screens, however still have several drawbacks. According to Mohanty (2001), these drawbacks include inefficiency, short life of screen mesh due to shaking and continuous feeding into one part of the screen, high maintenance cost, excessive noise and large footprint.
The high frequency Derrick Multifeed system (Figure 2.2) developed by the Derrick Corporation in Buffalo, New York takes advantage of the benefits provided by vibrating screens thereby maximizing screening capacity. It is composed of three 40 x 30 in2 independent screening sections supported within a single frame and connected to a vibrating unit. These sections are fed by a single feed source via distribution boxes located at the head of each screen.
Figure 2.2 Schematic of the Derricks Multifeed System.
An oversize manifold situated beneath the screens allows the oversize from the first two screens to by-pass and rejoin the oversize from the third screen into a discharge hopper. This quick removal of the oversize prevents the overloading of the screen surface thus maximizing screen area. Derrick (1984) reports that the Multifeed system increases capacity by approximately 2.5 times as compared to typical values from a 4 x 8 ft2 feed single deck derrick screens.
Derrick Stack Sizers or stacker units (Figure 2.3) represent a further improvement to the Derrick Multifeed design. They operate on the principle that screen width is of primary importance for separation performance. Therefore, stacker units employ screen decks that are wider than they are long.
A Derrick stack sizer consists of five screen decks stacked on top of each other and inclined downwards at angles between 15-25%. The screen stack is connected to a vibrating motor that supplies a high frequency vibrating motion uniformly throughout the entire width and length of the screen decks.
Figure 2.3 Schematic of the Derrick Stack Sizer.
The screens vibrate linearly extending the effective fluidized zone of the feed slurry enhancing separations over a larger area of screen surface and convey screened material out of the way of incoming feed Kelly (2005). A wash trough may also be added to maximize fine particle removal. Kelly, 2005 reports data from a western Pennsylvanian coal processing plant, which suggests that stack sizers can be used to achieve size cuts of 150 µm at feed rates of 80 tons per hour while providing efficiencies of 88-91%.
A recent technology development reported by Praczkajlo (1998) has reduced the occurrence of screen blinding by the use of a patented sandwich screen. When used in the high frequency Derrick Multifeed system, 94% overall screen efficiency values were obtained when achieving particle size separations as small as 100 m. Derrick(1984) reports Multifeed screen efficiency values prior to the addition of the sandwich screen at about 89%. A commercial installation of the technology processed about 45 tonnes/hr of fine coal using a unit requiring a floor space of about 4.6 m2.
A recently commercialized technology that addresses both capacity limitations and blinding is the Pansep Screen (Buisman, 2000).
The PANSEP screen (Figure 2.4)
efficiently achieves fine particle size separations at high capacity values by utilizing both sides of a moving deck of pans that hold the screens. The screen has slotted openings, which allow a relatively high open area that exceeds 40%. Patented spray bars are used to rinse the screen upon removal of the coarse material in an effort to reduce or eliminate blinding. A recently completed study involved an in-plant evaluation of a 0.5 m2 Pansep unit for achieving a 150 m particle size separation (Kroeger and Mohanty, 2004). To achieve the separation, the slot-size of the screen openings was 100 x 400 m. At a volumetric capacity of around 350 lpm, the particle size separation averaged around 160 m. More importantly, the classification efficiency as measured by the imperfection
value, I , was found to be about 3 times better than the existing classifying cyclone bank ( I = 0.14 versus 0.43). Furthermore, the amount of by-passed undersize material to the oversize stream was only 4.3% as compared to 33% for the classifying cyclones. However, capital and operating costs as well as plant footprint of the Pansep screens are higher than that of classifying cyclones. Also previous tests conducted by Mohanty (2002) suggest that, at high feed rates, the pansep screen is still susceptible to fine particle by-pass. In addition, the spray water, which does not pass through the screens, runs off the overflow carrying fine particles to the coarse particle stream. High by-pass values of about 35% using a smaller unit at a relatively high feed rate of 210 lpm have been observed (Mohanty, 2001).
Figure 2.4 Schematic diagram of the Pansep screen.
2.1.3 Industrial Practice
Fine particle screening in industry serves to provide a uniformly sized feed to a unit process and/or to generate a product. In coal operations, fine screening may be employed to provide the required feed consist to a flotation process. Figure 2.5 represents a flowsheet in which Derrick Multifeed screens are used. The underflow from a set of 30cm (12-inch) cyclones that receive the fine circuit feed is fed to a set of sieve bends. The oversize is subsequently dewatered in a centrifuge as the circuit product whilst the undersize is combined with the classifying cyclone overflow as feed to the flotation circuit.
Figure 2.6 represents a fine coal circuit that uses screening to deslime a clean coal product stream from spiral concentrators. In this circuit, the underflow from the classifying cyclones is fed to a set of triple start spirals. The spiral product is screened using a set of Derrick Multifeed screens with the objective of providing a particle size cutpoint of around 150 microns. The screen overflow is dewatered using screen bowl
centrifuges while the screen underflow is combined with the cyclone overflow and screen bowl effluent as circuit rejects which report to a thickener.
Overflow
Classifying Cyclone
Underflow Distribution Box
Derrick Multifeed Oversize
Undersize
Thickener Froth
Screen Bowl Centrifuge
Thickener
Clean coal
Circuit rejects
Figure 2.5 Flowsheet of a fine coal cleaning circuit, which uses Derrick Multifeed screens.
-1mm Feed Classifying Cyclone Underflow
Refuse Screen Refuse Distribution Box
Spiral Product
Derrick Stack Sizers Oversize Screen Bowl Centrifuge
Thickener Overflow Undersize
Effluent Clean Coal
Thickener Underflow
Figure 2.6 Flowsheet of a fine coal cleaning circuit, which uses Derrick Stack Sizers.
2.1.4 Problems
Problems associated with ultrafine particle screening typically result from lack of op en area and screen blinding. An increase in the amount of near cutsize particles within the feed increases the extent of blinding and further reduces screen performance. Increasing open area by reducing wire diameter results in increased screen wear, which increases cost. Solutions to such problems include vibrating screens, using a stacked arrangement of several screens and applying wash water to the screen surface to reduce screen blinding. The flow of wash water however, should be controlled to prevent run-off of fine particles from the screen surface.
2.2 Classification
2.2.1 Fundamentals
Classification is the separation of particles based on differences in their settling velocities in a fluid medium. The fluid medium usually has an upward velocity such that particles with settling velocities higher than the medium velocity settle while particles with lower settling velocities are carried upwards with the rising current of fluid medium. In this manner, a coarse particle size product and a fine particle sized product are obtained. Because separation is based on relative velocity, classification does not separate particles according to size directly but indirectly. Relative velocity is obtained by allowing different forces to act upon the particles. As a result, classification is considered an indirect means of size separation.
The free settling of a particle within a fluid is due to the resultant effect of three fundamental forces (Figure 2.7). The gravitational force, F g that influences the downward motion of a particle is opposed by upwards acting drag and buoyancy forces, F d and F b due to the fluid.
F d
F g
Direction of Particle Movement
F g
Figure 2.7 Force balance diagram of a particle settling at terminal velocity.
The particle initially accelerates until the force balance on it due to the effect of these opposing forces is zero. From then onwards, it settles at a uniform velocity known as the terminal settling velocity. The balance of forces around the particle is: F g Fd F b
(2.2)
For a spherical particle of diameter d p and density, s the mass can be defined as: 3
m
d p s
6
(2.3)
The force due to gravity, g acting on the particle can therefore be determined to be: 3
F g
d p s g
6
(2.4)
F b , which is a function of the fluid displaced by the particle can also be expressed as
3
F b
d p s g
6
(2.5)
where f is the fluid density. For a fine particle settling within a fluid under laminar conditions, the drag force results only from viscous forces. These viscous forces depend on the particle diameter d p , the terminal settling velocity of the particle, V t and the fluid viscosity as shown below: Fd 3d pV t
(2.6)
By substituting equations 2.4, 2.5 and 2.6 into equation 2.2 and solving for V t , Stoke’s law is obtained: V t
d P2 g P f 18
.
Based on Eq. (2.7) the free settling ratio
(2.7) d a d b
of two different particles to be separated by
classification can be expressed by:
b f d b a f
d a
1/ 2
(2.8)
where d a and d a are the effective diameters of the two particle types, a and b their respective densities and f is the density of the fluid medium. This equation clearly indicates that density has an effect on fine particle classification. Sometimes classification takes place under hindered settling conditions. These conditions usually occur at high particle volume concentrations. Hindered settling magnifies the effect of density on classification.
2.2.2 Techniques
Hydraulic classifiers, both static and centrifugal, are still generally the devices of choice for particle size separations below 250 m. This is mainly due to the deterioration in efficiency of prevailing screening techniques at fine particle sizes due to reduced open
area and screen blinding. In addition, current classification techniques employed in industry have higher throughputs and smaller plant footprints than most sizing devices. Furthermore, screen wear increases the operating cost of screening devices over that of existing classifiers.
In contrast to screening, particle size separation using classification principles is based on differences in the settling rates of the particles in the system. These rates ultimately determine the size of the classification units and their overall effectiveness. Although classifiers are generally considered to be more effective than screens for fine sizing, the conventional techniques employed for sizing ultrafine
particles (i.e., - 150 m) in the
coal and mineral processing industries have inherent inefficiencies that negatively impact separation performance and production costs. These inefficiencies occur because of viscosity, Brownian motion forces and particle interaction effects. Consequently, classifiers commonly suffer from by-pass, which occurs when a portion of the ultrafine particles (slimes) are misplaced by hydraulic carryover into the oversize product. The unwanted misplacement can have a large adverse impact on downstream separation processes.
Osborne (1988) groups classifiers into three broad types based on the mechanism by which particle separation is effected, i.e., settling classifiers, mechanical classifiers and centrifugal classifiers. Settling classifiers employ quiescent reservoirs of fixed volume to achieve separation. Separating conditions are varied by continuously or intermittently varying the underflow flowrate (Osborne 1988). Experiments conducted by Thompson and Galvin (1996) on a laboratory-scale settling classifier called the Counter-Flow settler yields promising results
for fine particle separations in terms of reducing by-pass.
Particle size separations in the range of 2.6 µm to 116µm with ultrafine by-pass values as low as 5% were achieved for the feed solids concentration range of 2.4% to 21.7% tested. Partition curve data provided for 10% feed solids at varying feed rates of 1.686 g/s to 6.809 g/s reveal that separation efficiency increases with increasing feed rate within the range tested. The best classification performance was obtained at a feed rate of 6.809 g/s
at which a separation size of 78 µm was achieved with an imperfection value of 0.40 with 5% by-pass. However, at low feed rates, both imperfection and by-pass were very high. Moreover, industrial scale up of settling classifiers can be difficult.
Fluidized-bed separators used for particle size separations employ the use of an upward flow of water to carry particles having a settling velocity lower than the fluid velocity into an overflow stream. The coarser material settles into the underflow stream. However, as the particle size decreases to below 150 microns, the settling rates become too slow to allow acceptable throughput capacity. The application of a centrifugal field accelerates the particle movement and provides the potential for ultrafine size separations to be achieved at relatively high mass flow rates. The Knelson Concentrator is essentially a fluidized bed system operating in a mechanically applied, enhanced gravity field. As shown in Figure 2.8, the unit consists of a rotating cone into which water is introduced through a series of fluidization holes located in the concentrate ring. The feed enters through the central inlet. When the slurry reaches the bottom of the cone, it is forced outward and driven up the cone wall towards the fluidizing ring. The slurry fills the ring against the inward movement of elutriation water, which creates a fluidized particle bed. Pinch valves are actuated allowing the coarsest material to be drawn from the ring at a controlled rate into a dedicated launder. The ultrafine particles flow out the top of the cone into an overflow launder.
Honaker et al (2005) evaluated the ability of the Knelson Concentrator to achieve ultrafine particle size separations by installing a pilot-scale, continuous discharge model in an eastern U. S. coal preparation plant. The unit was fed a split stream from the underflow of a secondary classifying cyclone bank, which was nominally 150 x 44 m. The solids concentration averaged around 22% by weight in the classifying cyclone underflow stream.
Feed Overflow Product
Overflow Product
Underflow Tailings
Underflow Tailings
Tailings Elutriation Water
Figure 2.8 Schematic diagram of a continuous Knelson Concentrator (Honaker et al , 2005)
The performance curves produced under the conditions listed in Table 2.1 are provided in Figure 2.9.The volumetric feed rate and solids concentration were maintained at 74 lpm and 25% by weight, respectively. A significant finding was that the separator provided relatively low quantities of ultrafine by-pass. In all tests, ultrafine by-pass to the coarse underflow stream was less than 10% with a low value of 3%. The cutsize (D50) decreased with an increase in the applied centrifugal force (i.e., rotational speed), which was expected due to enhanced particle settling rates. A similar trend was also observed with a reduction in the fluidization water rate.
Although the overall efficiency appears to be inferior to the typical performance of a classifying cyclone, the concentrator has adequate effectiveness while decreasing the amount of ultrafine by-pass. Under the maximum rotation speed for the unit and a moderate fluidization water rate, a relatively low cutsize of about 45 microns was achieved. Decreasing the rotational speed improved classification efficiency substantially as indicated by a respectable I value of 0.34.
Table 2.1 Test conditions for the particle size separation experiments with the Knelson fluidized bed. Test No.
Bowl Speed (rpm)
Fluid Rate (lpm)
Valve Open (sec)
Valve Close (sec)
3
1100
7.5
0.16
4.5
4
1100
10
0.16
4.5
5
1100
12
0.16
4.5
6
700
5
0.16
4.5
7
700
10
0.16
4.5
1.00 Test 3
0.90
Test 4 0.80
Test 5
w o 0.70 l f r e d 0.60 n U o 0.50 t
Test 6 Test 7
y t i l i 0.40 b a b o 0.30 r P
0.20 0.10 0.00 1
10
100
1000
Particle Size (microns)
Figure 2.9 Classification performance curves achieved by the Knelson Concentrator (Honaker et al , 2005)
However, the cutsize increased significantly. The data therefore indicates that the fluidized Knelson unit may be an effective tool for desliming in combination with a classifying cyclone.
Mechanical classifiers employ some sort of mechanical action to effect separation. They include Spiral and Rake classifiers. Feed is introduced at the central part of the trough of the spiral or rake classifier and flows towards the discharge weir. Coarse particles settle within the trough before reaching the weir and are drawn upwards using rakes or scrolls to a coarse product discharge outlet whilst fine material overflow a weir or launder as a fines product.
The spiral classifier is a mechanical classifier, which consists of an inclined round bottom trough and a raking spiral. The lower end of the trough forms a pool with an overflow weir, which allows the discharge of fine particles. The ratio between the pool depth and the spiral diameter is called submergence. Spirals may be grouped based on submergence as low weir, high weir or submerged. Low weir spirals have the entire length of the spiral exposed above the pulp surface and are considered to be 90% submerged. High weir spirals are 120% submerged while spirals located deep below the pulp surface are 150% submerged. The extent of submergence also determines the separation size. According to Heiskanen (1993), low weir spirals achieve size cuts in the 840µm to 210µm range while high weir and submerged spirals achieve separations within the ranges of 300µm to 74µm and 200µm to 50µm, respectively. Heiskanen (1993), Salter (1959), Salter and King (1957) report ultrafine by-pass and imperfection values of about 15% and 0.25-0.5 respectively for spiral classifiers. Water jets can be directed at the coarse product to further reduce by-pass and limit the amount of fine particle misplacement. Spiral classifiers are however prone to sanding, which is the excess build up of coarse material within the spiral trough. Furthermore, a decrease in sand raking capacity or increase in pulp density leads to surging which results in poor separation efficiencies and an increase in cutsize.
Centrifugal classifiers employ forces that magnify the gravitational force on particles to effect the separation of coarse particles from fine particles. They include classifying cyclones and various centrifugal classifiers such as the screen bowl centrifuge and the Counter-flow centrifugal classifier. Bickert et al (1996) report that the counter-flow centrifugal classifier developed mostly in Germany as capable of performing ultrafine classification with separation sizes less than 10 µm (Figure 2.10). The slurry feeding the classifier is introduced into the classifying chamber a, under pressure. Due to restriction of the coarse product outlet, the slurry is forced into the spaces, d between rotor blades c, where it experiences centrifugal accelerations in the range of 200 to 1800 times g-force (Heiskanen, 1996). The particles within the spaces d are therefore under the influence of inward radial forces and outward centrifugal forces. The net effect of centrifugal forces on coarse particles causes them to move outwards back into the classifying chamber, while the fine particles are drawn by an inward flow into the hollow shaft f , through the spaces in the rotor and flow out as a fines product. Even though this design is described as versatile and capable of handling feed percentages up to 40% by volume (Heiskanen, 1996), it has poor separation efficiency. This is because only the portion of the feed that actually enters the rotor spaces is classified. This significant by-pass of feed material causes the product to be similar to the feed.
A counter flow classifier developed by TU Clausthal (Heiskanen, 1996) can be used to achieve sharp cuts and low by-pass. As shown in Figure 2.11, it is made up of a bowl centrifuge into which clean fluid is introduced through a porous media. Comparison of this classifier with a conventional 10mm cyclone (Vesanto et al ., 1992) shows cutsizes of approximately 2.0µm and 7.0µm and imperfections of 0.36 and 0.42, respectively. Classification at such fine particle sizes usually involves the use of banks of small diameter cyclones fed by a distributor. More often than not, the feed to these individual cyclones vary due to inefficiencies in the slurry distribution process resulting in a reduced overall classification performance from the cyclone bank.The TU Clausthal classifier could therefore provide the benefit of allowing ultrafine particle separations within a single classifier.
Figure 2.10 Schematic diagram of the Counter-flow centrifugal classifier (Heiskanen, 1996)
Figure 2.11 Schematic diagram of the TU Clausthal classifier (Heiskanen, 1996)
The classifying cyclone is one of the most widely used classifiers in the coal and mineral processing. Its high capacity, versatility and small plant footprint make it the ideal tool for classifying the fine particulate material, which comprise the fine circuits of most mineral and coal processing circuits. The classifying cyclone has relatively simple design and operation and has no moving parts. Figure 2.12 shows a diagram of a conventional classifying cyclone. It consists of a cylindrical upper section and a conical lower section. Feed is introduced into the cyclone at the cylindrical section through a tangential feed entry, which forces it into a spiraling flow within the cyclone. The coarse particles move under the effect of the resulting centrifugal force towards the wall of the cyclone and spiral via the fluid motion towards the constricted apex, which limits the amount of feed volume reporting to the underflow stream. As a result, a portion of the stream is forced to reverse direction and is caught up in a zone of low pressure that carries it up to a cylindrical tube, which extends some distance into the cylindrical section called the vortex finder. Coarse material is discharged through the spigot or apex.
The classifying cyclone has existed as an important unit operation for over a century and has been a subject of numerous studies. Previous studies have involved developing mathematical models to predict the cutsize as well as investigating the effects of various geometric and operational parameters on the cutsize. As a result, various theories, which predict classifying cyclone performance, have been proposed. These include the equilibrium orbit theory, retention time theory, the turbulent diffusion theory, and the crowding theory. Among these, the equilibrium orbit theory is the most widely accepted for predicting cyclone performance. Various researchers such as Yokioka and Hotta (1995), Lilge (1962), Bradley (1958, 1960, 1965), Smith and Coghin (1984), Pericleous et al (1984), Kawatra et al (1996) describe this theory as that which best predicts cyclone
performance. The theory assumes that each particle within a cyclone is in equilibrium under the effect of two opposing forces. These are an outward acting centrifugal force due to the tangential flow within the cyclone and an inward acting drag force that is due to radial, inward velocity.
Pressure Gauge
Overflow Stream (fine particle stream)
Vortex Finder Cylindrical Feed Chamber
Feed
Cylindrical Section
Feed Inlet
Conical Section
Apex
Underflow (coarse particle stream)
Figure 2.12 Schematic diagram of the Krebs gMax cyclone (Anon, 2003)
Particles of different size will therefore have different equilibrium orbit radii. Particles near the inner wall of the cyclone report to the apex whilst particles close to the axis of the cyclone report to the vortex finder. This suggests that the vertical velocities of these particles are opposite to each other. Therefore, at some point within the cyclone there exists a plane where the vertical velocity of particles is zero. This plane is called the envelope of zero velocity (Kawatra et al , 1996). A particle at the envelope of zero velocity therefore has an equal probability of reporting to the apex or vortex finder. The particle size at the envelope of zero velocity is referred to as the cutsize. This theory assumes that flow conditions within the cyclone are laminar (Trawinski, 1972 ).
Most theoretical methods of predicting the performance of cyclones are based on the equilibrium orbit theory. A more successful approach however, relies on relationships derived from empirical data. The most popular relationships were developed by Plitt (1971, 1976), Plitt et al . (1980) and Lynch and Rao (1975), Lynch (1977), Rao (1966). Lynch and Rao (1975) and Plitt (1976) using data from large diameter hydrocyclones and high percent feed solids. The models provide versatility in the prediction of cutsize in actual plant situations. The Lynch equation was based on a series of regression parameters K 1 to K 6 and feed percent solids by weight, V and is of the form log d 50( c ) K1 Do K 2 Du K 3Di K 4 DcV K 5Q K 6
(2.9)
where Do is the vortex finder diameter, Du the apex diameter, Di the feed inlet diameter and Dc and Q are the cyclone diameter and the volume flow rate to the cyclone in m3/hr respectively. For the different size distributions, values for the different K values fall within the range of 0.0344 to 0.0637 for K 1 , 0.0190 to 0.0712 for K 2 , 0.0220 to 0.0513 for K 3 ,0.0255 to 0.0390 for K 4 , 0.00005 to 0.000008 for K 5 and -0.06623 to 0.0806 for K 6 .
Lynch and Rao used weight percent solids to develop their model whilst Plitt used volume percent solids, which provides a more accurate estimate of slurry rheology than
weight percent solids (Plitt, 1976; Plitt and Kawatra, 1979; Kawatra, 1996). The Lynch model is described as: 0.46
d 50( c)
14.8 Dc
0.71 u
D
h
Di0.6 Do1.21 exp 0.063V
0.38
Q
0 .45
s
m
0.5
(2.10)
Where s are the average density of the feed solids and m the density of the medium. while V is the percentage volumetric solids content and h is the distance from the top of the apex to the bottom of the vortex finder.
Increasing Dc increases the separation cutsize by increasing particle travel distance to the cyclone wall. The resultant centrifugal forces on fine particles are therefore insufficient to allow them to move through this increased distance to the cyclone wall and therefore, they are carried inwards and caught up within the vortex created at the apex of the cyclone and report to the cyclone overflow. Di influences the cutsize because of its effect on the feed inlet velocity. A large Di decreases the inlet velocity resulting in a reduced centrifugal force, which results in coarser cutsize. Changing D o changes the diameter of the vortex created within the cyclone. A larger Do results in a larger vortex which captures greater amount of fine particles to the cyclone overflow resulting an increased separation cutsize. Du controls the removal of the coarse particle product from the cyclone. Reducing Du results in the reversal of more fine material towards the vortex finder of the cyclone thus yielding a coarser product. Increasing V results in increased viscosity effects that limit the movement of fine particles towards the cyclone wall and results in a coarser cutsize. Increasing h , decreases cutsize by increasing particle retention time within the cyclone, which allows fine particles sufficient time to report to the cyclone wall. An increase in Q increases the centrifugal force of separation and allows fine particles to reach the cyclone wall resulting in a finer cutsize while an increase in m leads to hindered settling effects which limits the settling of fine particles and results in an increase in particle cutsize.
Inherent inefficiencies in classifying cyclone operation such as energy losses in the cyclone body and misplacement of particles to the wrong product streams also affect cyclone performance. This misplacement can be due to the short-circuiting of coarse particles to the overflow stream or commonly the hydraulic entrainment of fine particles to the underflow. In coal processing plants, the short-circuiting of coarse particles, results in the loss of coarse coal or a reduction in the efficiency of downstream unit processes. Hydraulically entrained ultrafines commonly contain high levels of clay and/or pyrite that can greatly reduce the quality of the underflow product. In addition, high solids concentrations near the cyclone apex leads to hindered settling conditions. Hindered settling increases the density effect on particle classification and results in an increased recovery of fine, high ash mineral matter and or pyrite.
Recent studies, which were initiated by advances in computational fluid dynamics modeling have focused on classifying cyclone design (Rong and Napier-Munn, 2003; Olson and Ommon, 2004). Rong and Napier-Munn (2003) at the Julius Kruttschnitt Mineral Research Center have developed a new classifying cyclone called the JKCC. The JKCC has certain unique features that distinguish it from the conventional cyclone. Unlike the conventional cyclone which has both a cylindrical vortex finder and upper section, the vortex finder of the JKCC tapers downwards and outwards while the upper section angles inwards. As a result, feed flow within this upper section is accelerated, thereby increasing the centrifugal force. Hence, unlike conventional cyclones, effective particle separation can be achieved within the upper section of the JKCC. The JKCC vortex finder has a thicker wall than that of the conventional cyclone. This, combined with its unique shape increases the tangential velocity gradient within the JKCC and reduces particle misplacement. The apex is designed with a shoulder directly above it. This feature in addition to other benefits, decreases ultrafine by-pass. Data form tests conducted by Rong and Napier-Munn (2003) on both JKCC and conventional cyclones show that the α-value for the JKCC lies in the range of 4.0 to 6.0 while the α-value for the conventional cyclone was less than 4.0. Water recovery to the underflow for the
JKCC was in the range of 18-24% at a corrected cutsize of 40-50 µm compared to 26% in the conventional cyclone at a 53-µm cutsize.
Research conducted by Honaker et al (2001) on a modified cyclone called the Cyclowash provided encouraging results for the improvement of cyclone performance. This cyclone, which was developed by Kelsall and Holmes (1960), incorporates a water injection component that lies above and near the apex of the cyclone and below a truncated cone as shown in Figure 2.13. Feed initially enters into the section of the cyclone above the truncated cone where primary classification takes place. The classified coarse size fraction of the feed then enters the chamber below where tangential water addition is used to displace the medium that entered in the feed stream and the hydraulically entrained particles. In this way, the Cyclowash achieves a two-stage classification in a single stage operation. Results of a parametric study conducted by Honaker et al. (2001) revealed that use of the Cyclowash results in significant ultrafine by-pass reduction. However, this reduction was accompanied by increased cutsize. The performance provided by the Cyclowash also compared favorably with a conventional 10-cm cyclone at the same cutsize (Table 2.2)
Overflow
Vortex Finder Feed Inlet
Truncated Cone Water injection
Truncated Cone Spigot
Underflow
Figure 2.13 Schematic diagram of the Krebs Cyclowash (Krebs, 2003).
Table 2.2 Comparison of conventional classifying cyclone and Cyclowash test results.
Parameter
D50C = 19 microns
D50C = 26 microns
Conventional
Cyclowash
Conventional
Cyclowash
Apex Diameter (cm)
1.75
1.27
1.75
2.54
Vortex Finder Diameter (cm)
1.95
1.91
2.54
2.54
Feed Pressure (Kpa)
172
207
138
172
Truncated Cone Diameter (cm)
-
1.91
-
2.54
Cyclowash Water Rate (lpm)
-
31.0
-
46.5
By-pass (%)
35
19
15
7
Imperfection
0.404
0.411
0.399
0.324
The JK three-product cyclone is another innovative cyclone design developed and tested by the Julius Kruttschnitt Mineral Research Center (Obeng and Morrell, 2003). It was designed with an aim to reduce the density effect in cyclones that results in small dense particles reporting to the cyclone underflow. This effect, which has been observed by researchers such as Lynch(1977), Plitt et al (1980), Firth et al (1998), Firth and O’Brien(2003), results in contamination of the cyclone product and overgrinding in ball
mill circuits. This cyclone makes use of both an inner and outer vortex finder in addition to an apex to generate three products from a single cyclone. The internal vortex finder descends some distance beyond the external vortex finder into the conical section of the cyclone. The JK three product cyclone has a small apex that serves to limit the flow of material out of the cyclone. The feed stream to the cyclone contains a relatively large amount of solids. In tests conducted by Obeng and Morrell (2003) on a 15-cm three product cyclone, a feed content of 52% solids by weight was used. This creates a high solids concentration near the cyclone apex, which results in hindered settling conditions. The coarse, dense particles are able to migrate to the apex as coarse product while a bed of fine dense particles forms above them. The fine, dense particle bed is then carried
upwards by the vortex created by the inner vortex finder as inner overflow whilst the fine, light particles are carried out through the outer vortex finder.
In addition to the aforementioned novel classifying cyclone designs previously discussed, different cyclone circuits have also been tested to maximize classifying cyclone efficiency. The circuits have primarily involved using two or more cyclones in series whereby the underflow stream is retreated in subsequent stages. Results from a classifying cyclone circuit achieving a particle size separation o f about 100 m revealed a 60% decrease in ultrafine by-pass and 12% increase in classification efficiency when using a circuit arrangement as compared to a single classifying cyclone unit (Firth and O’Brien, 2003).
2.2.3 Industrial Practice
Classification in industry may be used to produce a particular sized feed to the next stage in a process or to generate saleable product. In the coal industry, fine particle cleaning processes generally require pre-classification which is commonly achieved using cyclones. These include spiral and flotation feeds as well as tailings material sent to thickeners. The typical location of classifying cyclones within a coal cleaning circuit is shown in Figure 2.14.
The cyclone underflow stream serves as the feed to the spiral circuit while the overflow stream provides a fine sized feed to the froth flotation. Figure 2.15 represent a classification circuit in which a two-stage cyclone arrangement is used to generate a circuit product. The 38-cm diameter cyclone underflow stream is the feed to the spirals, while the overflow serves as the feed to a two-stage 15-cm diameter classifying cyclone circuit. The underflow of the primary 15-cm cyclone bank provides the feed to the secondary cyclone.
Froth Flotation Cells Circuit Reject Feed 38-cm Classifying Cyclone
Underflow
Spirals Circuit Product
Centrifugal Dryer Spiral Product Spiral Reject Sieve Bend
Oversize Effluent
Undersize
Figure 2.14 Schematic flowsheet of a fine coal classifying circuit which uses classifying cyclones, spirals and froth flotation.
Overflow
To thickener
Overflow Feed
15-cm Classifying Cyclone
38-cm Classifying Cyclone
Overflow
15-cm Classifying C clone
Underflow 1mm x 150 µm
Spirals
Underflow Spiral Product Effluent
(a) Spiral Reject
Clean Coal
Figure 2.15 Schematic diagram of a fine coal classifying circuit, which uses spirals, and classifying cyclone circuit.
The primary cyclone overflow serves as the two-stage cyclone reject whilst the secondary underflow is the circuit product. The purpose of the two-stage cyclone circuit is to achieve a particle size separation of around 25 microns that would lead to the generation of a clean coal product after dewatering.
2.2.4 Problems
Factors that can have a negative effect on classification performance are viscosity and yield stress (Agar and Herbst, 1966; Klimpel, 1982, 1983; Kawatra et al., 1996). Both of these factors increase sharply as the particle size decreases and particle population (solids content) increases. Yield stress has the ability to prevent particles from moving independently within a non-Newtonian fluid and may account for the by-pass of ultrafines typically observed in classifying cyclones. Ultrafine particle suspensions having solids contents as low as 10% by volume may be subject to yield stresses, which may be overcome by applied vibration and chemical treatments. This by-pass is a result of the inability of this fine material to move independently of the fluid medium. Hence, fine particles are trapped by the water associated with the coarse product. Such particles therefore report to the underflow of the cyclone without undergoing classification thereby contaminating the coarse product. Particle density effects result in low-density coal reporting to the overflow stream despite having a particle size that is greater than the overall particle cutsize ( D50).
This is because such particles have sufficient density to settle to the apex of the cyclone and report with the coarse product. The turbulence and instability of flows within cyclones also contribute to reduced efficiency. It is even more undesirable in settlers since settlers require quiescent flow of water in order to achieve good separation. Insufficient retention time also limits the ability of classifiers used in industry to achieve optimum separations.
2.3 Potential Industrial Applications
Previous studies have found that density-based processes provide higher efficiencies for treating coals with high middlings concentrations than froth floatation. However, the ability of gravity separators to treat fine particles is limited by the lack of particle inertia relative to the surface drag forces (Honaker et al , 1996). These surface drag forces can however be overcome by the use of an enhanced gravitational field. Based on this knowledge, several enhanced gravity separators capable of achieving density-based separations on ultrafine particles have been developed and tested for their potential as coal cleaning units. These centrifugal units include the Knelson and Falcon concentrators, the Altair and Kelsey jigs and the Mozely Multi-Gravity separator. Among these separators, the Falcon concentrator indicates the most potential for near term use in coal processing plants.
The Falcon concentrator consists of vertically inclined, open-topped cylindrical bowl supported on a revolving shaft (Figure 2.16). Feed slurry is introduced through a central feed inlet to the bottom of the revolving bowl. An impeller propels the slurry to the walls of the bowl and causes the differential acceleration of the particles within the slurry. As a result, the particles stratify along the lower section of the bowl referred to as the migration zone. Two force components govern particle movement within the stratification zone. One of them is a strong concentrating gravity, which provides the strong gravity forces while the other is a weak force parallel to the wall of the bowl that moves particles upwards along the bowl surface. Thus, a particle layer is formed which is composed of an outer layer of coarse and dense particles in contact with the bowl surface and an inner layer of fine particles. The angle of inclination of the rotor surface changes near the top of the he bowl making it parallel to the axis of rotation. This eliminates the weak upwards driving force on the moving particle. As a result, dense and coarse particles migrate into a slot, which exists around the circumference of the bowl where they are withdrawn using pinch valves. Fine, light particles move with their previously acquired momentum across the slots and report in the overflow stream as fines product.
Figure 2.16 Schematic illustration of the operating principles of the Falcon Concentrator (Honaker et al , 1996)
Previous work done by Honaker et al (1996) on a 25-cm diameter Falcon concentrator provided a low density cut point of 1.6 for a 210 x 37 µm size fraction at feed rates of up to 2.2 tonnes per hour. In addition, ash rejection values of between 60% to 75% at combustion recovery values in excess of 85% and organic efficiencies of around 90% were achieved. Ash rejection in the -37µm fraction however was negligible suggesting that the high centrifugal force provided by the Falcon concentrator was insufficient to influence particle motion at such fine size ranges. Other enhanced gravity concentrators have varying levels of efficiency in treating fine coal. Table 2.3 shows the performance of various enhanced gravity separators for treating 210 x 37µm particle size coal.
Table 2.3 Typical probable error values and specific gravity cut points achieved by various enhanced gravity concentrators for the treatment of 210 x 37-µm size fraction of various coal samples (Honaker et al, 1996). Parameter
Falcon
Knelson
MGS
Kelsey
Max. Centrifugal Force
300
60
30
60
Gravity Cur Point
1.5 - 1.8
1.9
2.1
2.0
Probable Error
0.10 - 0.15
0.10
0.10
0.12
The principles of density-based separators are well understood by plant operators, which is in contrast to flotation. Given an effective unit, operators prefer the density based unit especially considering the differences in operating costs. However, an inherent problem with enhanced gravity units is the recovery of colloidal particles in the clean coal product stream, which significantly suppresses the product grade. An efficient classifying performance would allow enhanced gravity units to produce a clean coal product.
3. EXPERIMENTAL
3.1 Laboratory Classifying Circuit
The laboratory classifying cyclone circuit tests were conducted using a 10-cm diameter Krebs classifying cyclone with a 5.5-cm2 inlet area, a 4-cm diameter vortex and a 12-degree cone angle. Inlet pressure was maintained at 140 kPa. Three different apex diameters of 1.27-cm, 1.66-cm and 2.05-cm were used under various test conditions studied. Figure 3.1 is a schematic diagram of the experimental set-up used for the tests. Both the overflow and underflow streams of the classifying cyclone were collected were collected in the feed sump. A part of the cyclone feed was by-passed to the feed sump at a high flowrate to enhance mixing within the sump through continuous agitation of the feed sump contents.
During all tests, underflow and overflow samples were collected at precisely the same time. A time interval of 15 minutes was allowed between sample collections to ensure steady state conditions and to allow the complete dispersal of the added modifiers within the slurry. The samples collected were analyzed for ash content using one gram representative samples put through an ash furnace. The samples were also wet screened or analyzed using a Cilas Quantum particle size analyzer to obtain the size distributions of the feed, underflow and overflow material. These analyses were used to generate partition curve data for the various test conditions.
The laboratory classifying cyclone tests was conducted in three phases. The initial tests were conducted using a coal sample from an Illinois based processing plant that treats Illinois No 6 coal. Upon arrival, the coal sample was crushed and screened to -150 µm.
Overflow
Classifying Cyclone
Underflow Fine Coal
Feed Sump
Pump
Figure 3.1 Schematic diagram of the laboratory classifying-cyclone set-up.
A three-parameter Box-Behnken statistical experimental design was employed to conduct a series of tests whose objectives were to:
Study the effect of three previously identified parameters namely the apex diameter, feed solids concentration and dosage of the viscosity modifier on classifying cyclone performance for treating fine coal using a range of parameter values which were chosen with aim of providing measurable differences in the response variables as shown in Table 3.1.
Obtain experimental data that could be used to develop a model, which describes the effect of the aforementioned parameters on selected response variables used to describe classifying cyclone performance.
The second phase of tests was conducted using samples of Illinois coal. The aim of these tests was to investigate the effect of viscosity modifier type and their ability to maintain a high level of classifying efficiency when the cyclone was operated under roping or near roping conditions.
The third set of tests was conducted using feed slurry obtained from the Supreme Energy Coal processing plant located in Knott County. In order to produce a high solids content slurry, which could be used to prepare feed of different solids concentrations at constant ash content to the classifying cyclone, the coal sample was taken through a first stage of classification in the laboratory. This was done using a 10-cm classifying cyclone fitted with a 1.27 cm apex at a feed pressure of 15psi (103 kPa). The cyclone underflow was then collected and used to carry out the test work. Samples of this underflow product were diluted and used to prepare feed slurry of different percent solids, which were used as feed for different tests.
Table 3.1 Range of parameter p arameter values evaluated in the laboratory classification test program. Independent Variables Apex diameter (cm) Feed solids (%) Viscosity modifier (Kg/t)
Range -1 level
0 level
+1 Level
12.7 5 0
16.6 7.5 0.5
20.5 10 1
3.2 In-Plant Classifying Circuit
The in-plant classifying circuit tests were performed within the fine coal circuit at the Supreme Energy coal processing plant located in Knott County in Eastern Kentucky. A two-stage classifying cyclone circuit comprised of 15-cm diameter Krebs G-max cyclones were installed and evaluated for the treatment of the overflow of a bank of
38-cm classifying cyclones (Figure 3.2). The 38 cm cyclones were used to effect a 150µm (100 mesh) size cut from a nominal -1mm particle size size coal .The 1mm x 0.15mm size fraction reporting to the underflow underflow was treated using spirals while the -0.15 mm size fraction reporting to the cyclone overflow provided the feed to the 15-cm classifying cyclone circuit. Two U6-gMax cyclones were used in the primary cyclone stage. The goal of the primary 15-cm 15 -cm diameter cyclones was to maximize the rejection of the -37 m material to the overflow stream while recovering nearly 100% of the +37 m coal to the underflow stream. The primary cyclone underflow stream was retreated retreated in a Secondary 15-cm diameter gMax cyclone, which was operated to maximize the removal of the -37 m material from the final underflow product stream. The objective of the in-plant tests
was to produce a clean coal product by achieving an efficient 37 m separation. The physical dimensions of the inlet and outlet ports of the G-max G -max units used in this study as well as the operating pressures are provided in Table 3.2.
Circuit Overflow - 0.15 mm - 1mm Feed
15-cm G-max Classifying Cyclone
38-cm Classifying Cyclones Dilution Water
Feed sump Spiral Feed 1 x 0.15 mm
15-cm G-max Classifying Cyclone
Circuit Produc
Figure 3.2 Schematic flowsheet of the two-stage two -stage Classifying cyclone circuit set-up.
Table 3.2 Dimensions and operating conditions used in the study for the primary and secondary classifying cyclones. Parameter
Primary Cyclone
Secondary Cyclone
No. of U6-gMax Units
2
1
Inlet Area
9.7-cm2 (1.5-in2)
9.7-cm2 (1.5-in2)
Apex or Spigot Diameter
1.25-cm (0.5-in.) or 0.635-
0.635-cm (0.25-in)
cm (0.25-in) Vortex Finder Diameter
5.1-cm (2.0-in)
3.8-cm (1.5-in)
Volumetric Feed Rate
42 m3/hr (185 gpm)
50 m3/hr (222 gpm)
Inlet Pressure
193 kPa (28 psi)
207 – 241 241 kPa (30 – 35 35 psi)
Incremental samples of all the different process streams were taken at 5-minute intervals for a period of 30 minutes for each of the test conditions evaluated. To ensure steady state conditions, a time interval of 30 minutes was allowed between subsequent tests. Pulp density readings were measured using a Marcy Density gauge previously calibrated to provide a reading of 1(one) for the density of water. Solid concentration was determined from the weight of the slurry before and after filtering it and drying the filter cake at 100°c. The samples were then wet screened and the material in each size fraction analyzed for ash content. Mineral matter content was estimated using the the Parr formula. Due to the high solid concentrations in the primary underflow stream, dilution water was added in some tests prior to being fed to the secondary cyclone. However, the amount of water could not be measured which resulted in the inability to quantify a circuit water flow balance.
3.3 Classifying Efficiency Determination
Classifying cyclone performance is characterized by a partition curve obtained by plotting the probability of particles to report to the underflow of the cyclone as a function of particle size. The particle size analysis results obtained from the sample were used to generate partition curves for the various test conditions evaluated. From these curves, the cutsize ( d 50 ), corrected cutsize ( d 50( c ) ), imperfection value ( I ) and the ultrafine by-pass associated with the classification were quantified. d 50( c )
The cutsize is the particle size corresponding to the particle size, having a 50% probability of reporting to the underflow stream. The corrected cutsize was determined from the corrected curve which is developed by adjusting the actual partition values to eliminate the effect of ultrafine by-pass using Equation 3.1 ., i.e.,
Y '
Y R1 1 R1 R2
(3.1)
in which Y ' is the corrected partition number, Y is the actual partition number, R1 is the fraction of ultrafine by-pass to the underflow stream and R2 is the fractional amount of by-passed coarse particles to the overflow stream. The slope of the classification curve describes the efficiency of the classification and is referred to as alpha value (α). An efficient classification is characterized by a high α-value. Another efficiency measurement is the imperfection value, ( I ) which is determined from the corrected partition curve according to the following expression: I
d 75 d 25 2d 50( c)
(3.2)
where d 75 and d 25 are the particle sizes that have a 75% and 25% chance of reporting to the underflow stream. As such, a perfect separation corresponds to a value of zero (0) and increases with declining efficiency. The yield to the underflow stream can be determined based on ash analysis of the feed, underflow and overflow stream material using the following relationship:
Yield (%)
f
o x100 u o
(3.3)
where f , u and o are the ash contents of the feed, underflow and overflow respectively.
Other performance parameters used were combustible recovery R determined from Equation 3.4 as: R
o f
Y
(3.4)
and volumetric yield to the underflow stream: y volume
Qu Q f
f o u o
(3.5)
where f , o and u are the pulp density values of the feed, overflow and underflow streams, respectively. The pulp density values were determined using a Marcy density gauge.
3.4 Coal Characteristics
The coal used for the first and second phase laboratory plant tests was obtained by crushing and grinding run-of-mine coal obtained from the Illinois No.6 seam. The -100 mesh (-150 microns) coal was prepared using a laboratory jaw crusher and hammer mill. The particle size weight and ash distribution of the coal are provided in Table 3.3.
The subsequent group of tests was conducted using coal obtained from the Supreme Energy coal processing plant located in Knott County in Eastern Kentucky. Samples of the feed to the two stage circuit was collected as a 5% solids content slurry ,which was further classified using a 10-cm laboratory Krebs cyclone fitted with a 1.27 cm apex from which a slurry of about 24 % solids was obtained after initial classification.
Particle size analyses of the feed samples were achieved by wet sieve analysis and the data presented in Table 3.4.
Table 3.3 Particle size analysis of the phase 1 and 2 laboratory classifying cyclone tests feed. Weight (%)
Ash (%)
150 x 105
26.45
27.95
105 x 75
17.37
37.36
75 x 63
10.71
44.21
63 x 44
3.45
47.33
44 x 37
4.02
48.06
37 x 25
4.35
49.59
-25
33.65
66.47
100.00
46.71
Particle Size (Microns)
Table 3.4 Particle size analysis of Phase 3 laboratory classifying cyclone tests feed.
Particle Size (Microns)
Weight (%)
+212
0.25
Ash (%) 3.92
212 x 150
0.83
2.70
150 x 75
7.29
2.92
75 x 45
7.37
6.87
45 x 37
2.56
12.58
37 x 25
4.39
23.89
-25
77.32
69.84
100.00
56.12
The -500 mesh material contained 66% ash, which implied that the fraction has the greatest amount of ash forming material. It was therefore realized that the solution to generating a low ash product was the removal of this high ash content fraction by achieving an efficient size separation. Further analysis of the size-by-size data indicated that complete removal of this size fraction would generate a 9.4 % ash content product.
3.5 Viscosity Modification Tests
A major problem in ultrafine particle classification is the significant amount of submicron particle by-pass to the coarse particle stream. The amount of solids reporting to the underflow stream due to hydraulic entrainment is proportional to the amount of water recovered. Submicron particle by-pass to the underflow stream can therefore be reduced by decreasing water recovery. Past studies have shown that such a reduction can be realized when the feed solids concentration is increased or the apex diameter is reduced. However, both adjustments tend to have a negative impact on slurry rheology by increasing viscosity. Slurry viscosity is known to have a negative impact on the performance of classification processes (Laskowski, 2001). This results in an increase in particle size cut point and negatively affects classification efficiency.
The classifying cyclone model developed by Bradley (1965) indicates that an increase in viscosity elevates the particle size cutpoint ( d 50) as indicated by the expression: d 50
K
Dc3
0.5
(3.6)
Q s m
where is the medium viscosity, K a geometry constant, Dc cyclone diameter, Q feed volumetric flow rate, s the solid density and m the apparent medium density. Several other models also describe an increase in d 50 as a function of the square root of medium viscosity (Agar, 1996, Plitt 1980). Kawatra et al (1996) investigated the effect and found that the relationship was 0.35 and, in subsequent studies, discovered that increasing the medium temperature could counter the viscosity effect (Kawatra et al , 1988). A rise in
temperature however requires a large energy input that is generally not cost effective or practical for commercial circuits. A more practical adjustment to a given classifying cyclone would be to increase inlet pressure. However, the higher pressure would elevate maintenance requirements and would not significantly negate the viscosity effect on efficiency.
A more promising approach for reducing slurry viscosity is to employ a surfactant to alter slurry rheology (Zaman et al ., 2000). Previous studies conducted by Klimpel (1982, 1983) found that viscosity modifiers can reduce the cyclone separation cutsize (d 50). This approach can be particularly cost effective if the surfactant employed is already used by downstream unit operations.
An investigation was performed to evaluate the feasibility of reducing hydraulic entrainment to the underflow stream by minimizing water recovery and using viscosity modifiers to counter the negative effects of high solid concentrations within the separation zone of the classifying unit. This investigation was carried out as an integral part of the laboratory classifying circuit tests previously described. The modifiers used were sodium dodecyl sulphate and Nalco 9762 provided by Nalco Company Limited. The amount of modifier was varied while also changing the feed solids concentration and apex diameter according to a statistically designed test program in an effort to identify the conditions providing the optimum classification performance. . The froth flotation tests followed a standard ASM procedure for determining flotation rate. A laboratory Denver flotation unit with a 1-liter cell was used for the tests. The feed solids concentration was 5% by weight. Methylisobutylcarbinol (MIBC) and fuel were used as the frother and collector, respectively, at concentrations of 30 ppm and 1 lb/ton.
The viscosity of the feed slurry was measured using a Cannon LV-2000 Rotary Viscometer. The system consists of a removable cylindrical spindle connected to a
measuring device and suspended at the axis and above the bottom of a hollow cylinder. A spindle was used that allowed the detection of viscosity changes as low as 1 centipoise. A rotational speed of 60 revolutions per minute was employed in all measurements. Upon initiating spindle rotation, the dispersed sample was quickly transferred into the space between the spindle and the cylinder. Each reading was taken five (5) seconds after the introduction of the sample to ensure that all measurements were taken while the particles were in complete suspension. The sample readings were reasonably stable, provided good repeatability and provided sufficient basis for comparing the viscosities of the different samples.
4. RESULTS AND DISCUSSION
4.1 Classifying Cyclone Parameter Evaluation
Many parameters influence classifying cyclone performance. These include among others, feed rate and feed percent solids, pressure drop, cyclone diameter, apex diameter and vortex finder diameter. However, the aim of the classifying cyclone parameter evaluation was to identify and evaluate the effect of parameters that could be easily adjusted in an existing classifying circuit with minimum effort on the part of plant operators. Feed percent solids and apex diameter were therefore chosen as the parameters to be evaluated. Dosage of the viscosity modifier sodium dodecyl sulphate (SDS) was also included in this study to determine the effect of changing viscosity on classification performance.
The data obtained from the study is presented in Table 4.1. A general look at the data suggests that increasing percent solids within the range studied decreased solids yield and increased both cutsize and the imperfection value. This was due to the higher solids population and the resulting hindered settling effect created within the cyclone as a result of these conditions. The result was a decreased ability of fine particles to move to the coarse product stream. The higher imperfection value was because of increased viscosity and particle interaction effects occurring at high solids concentrations.
Decreasing feed solids concentration and increasing apex diameter resulted in a general increase in both solids yield and a reduction in cutsize and imperfection value. This was due to the reduction in viscosity and other effects under these conditions. However, ultrafine particle by-pass to the underflow stream was increased as a result of increased water recovery to the coarse product stream. The addition of viscosity modifier resulted in a general reduction in cutsize, by-pass and imperfection with a corresponding increase in yield. The decrease in cutsize is much more prominent in the higher solids concentration and smaller apex diameter case with the addition of the viscosity modifier.
Table 4.1 Test results obtained from the experimental program evaluating the effect of apex diameter, modifier addition and feed solids on classifying cyclone performance.
Test No.
Apex Diameter (mm)
SDS (kg/t)
Feed Solids (%)
d50(c) (µm)
By pass (%)
Imperfection
52.00
Yield to Underflow (%) 61.27
1
12.7
0
7.5
9.80
0.490
2
12.7
1
7.5
42.50
67.61
8.50
0.277
3
16.6
0
5
36.00
68.98
12.5
0.361
4
16.6
0.5
7.5
43.80
66.78
9.90
0.457
5
16.6
0
10
43.00
67.40
11.5
0.454
6
20.5
0.5
5
29.00
79.61
22.2
0.379
7
12.7
0.5
5
37.50
69.57
8.50
0.300
8
16.6
0.5
7.5
42.80
66.36
10.5
0.491
9
12.7
0.5
10
39.00
68.51
11.00
0.324
10
16.6
0.5
7.5
45.00
65.96
10.00
0.433
11
20.5
0.5
10
25.50
84.14
30.00
0.422
12
20.5
0
7.5
31.10
79.66
17.50
0.389
13
16.6
0.5
7.5
44.00
65.51
14.50
0.398
14
20.5
1
7.5
29.20
80.22
22.70
0.428
15
16.6
1
10
40.50
66.83
10.00
0.386
16
16.6
0.5
7.5
44.00
66.04
11.00
0.455
17
16.6
1
5
35.00
68.99
7.50
0.278
Previous research conducted by Williamson et al (1984), Williamson and Bott (1984) as well as Kawatra et al (1988, 1996) support the above observations. It was proposed that this reduction in cutsize was due to a reduction in the viscosity of the slurry that enhanced particle movement relative to the fluid and allowed fine particles to move outwards to the cyclone walls as well as improving their settling kinetics within the fluid. As a result, the ability of fine particles to report to the apex of the cyclone was increased.
However, not all past research investigating the effect of viscosity observed the same trends. Klimpel (1981, 1982) using an anionic polymer to modify pulp viscosity obtained a small increase in cutsize with decreasing viscosity. This increase was due to particle aggregation, which reduced particle population within the cyclone. Yopps (1986) and Yopps et al (1987), on the other hand, did not observe any change in cutsize with reducing viscosity. Therefore, the observed effects of viscosity modification might be specific to the modifier.
The experimental results obtained from the statistical test program together with the operating variable values were entered into a commercial statistical analysis software package called Design Expert .This was used to develop empirical models that describe the effect of the parameter values on the cutsize, solids yield to the underflow of the cyclone, ultrafine by-pass and imperfection value. . The 17 tests that were conducted involved five repetitive tests (Test No. 4, 8, 10, 13, and 16) which corresponded to the central parameter values. These values were used to quantify the normal error associated with the experimental procedure and sample analysis and show the degree of repeatability of the test results. Table 4.2 shows the variability in the various response variables resulting from experimental error. The data revealed the existence of a small degree of randomness in the measured response variables. This is evident in the measured d 50(c) values of tests 8 and 10, which marginally fall out of the determined confidence interval corresponding to 43.92 µm 0.97. The other measured responses however show a lower degree of variability.
Table 4.2 Statistical evaluation of the experimental error realized from the Classifying Cyclone parametric test. Test Number
D50(c) (microns)
Yield (%)
By-pass (%)
Imperfection
4
43.80
66.78
9.90
0.457
8
42.80
66.36
10.50
0.491
10
45.00
65.96
10.00
0.433
13
44.00
65.51
14.50
0.398
16
44.00
66.04
11.11
0.455
Mean
43.92
66.13
11.18
0.4468
95 % Confidence Interval
0.97
0.59
2.37
0.0425
Quadratic expressions were found to be suitable for the prediction of the response variables. The forms of the quadratic models for the various response variables considered were: d 50( c) 86.90 1143 . AD 25.63 MC 15.29 PS 0.47 AD 2 7 .99 MC 2
117 . PS 2 0.97( AD)( MC) 0.21( AD)( PS) 0.30( MC)( PS )
(4.1)
. 1169 . AD 1152 . MC 501 . PS 0.36 AD 2 8.21 MC 2 Bypass 11326
0.20 PS 2 083 . ( AD)( MC) 014 . ( AD)( PS) 0.70( MC)( PS )
(4.2)
Imperfection 0.59 0.061 AD 0.54 MC 0.15 PS 0.0021 AD 2
0.0750 MC 2 0.0093 PS 2 0.032 ( AD)( MC )
(4.3)
in which AD is apex diameter in millimeters (mm), MC modifier concentration in kilograms per tonne (kg/t) and PS the feed solids concentration by weight (%).
To develop a statistical model, it is first necessary to prove that the observed results are a direct product of the changing parametric values rather than experimental error. This suggests that the variances associated with experimental procedures should be less than the variance in the response variables for all tests. Anything other than this would render the model meaningless. Evaluating the effects of parameter changes on a response variable involves the use of the F-statistic. The F-statistic is used to test the null hypothesis that the variance observed for all tests can be explained by pure error variance and is expressed as: SS yy F
df r SSE df e
(4.4)
where SS yy is the sum of squares residuals for the response variables, df r the degrees of freedom associated with the calculation of SS yy , SSE is the sum of squares of the experimental error and df e is the degrees of freedom associated with the determination of the SSE value which is obtained by subtracting the
number of measured
responses(n=17), from the parameters(k=9). In order for the model to be accepted, the null hypothesis should be rejected. The calculated F-value is therefore compared to an F 0.05, which corresponds to the 95% confidence interval for the calculated value. The F-
value is determined from the F-Table for (k=9) and (n-k+1). If the F-value exceeds the calculated F 0.05 , the model is accepted. All the F-values shown in Table 4.3 are greater than the calculated F-value of 3.68 determined from the models considered and therefore the models were considered adequate.
The model was also tested using the probability Prob >F value, which represents the probability of falsely rejecting the null hypothesis and is used as the indicator of level of significance.
Table 4.3 ANOVA table generated for the response variables associated with the laboratory classifying cyclone tests. Degree of
Source
Freedom
F Value
Prob > F
R 2
Adjusted R 2
By-pass Model Model
9
8.33
<0.0053
Lack of Fit
3
3.46
0.1307
0.92
0.81
0.88
0.78
0.98
0.96
Imperfection Model Model
9
9.32
<0.0017
Lack of Fit
3
0.76
0.6210
Cut Point Model Model
8
49.13
<0.0001
Lack of Fit
2
6.64
0.0535
Low Prob > F values correspond to a lower probability of falsely rejecting the null hypothesis and hence suggest a strong relationship between the parameter changes and the response variables. Prob > F values around 0.10 or less are associated with terms which that are considered to have a significant effect on the response variable. As shown in Table 4.3, all the models had Prob > F values less than 0.10 suggesting a strong relationship between the parameter effects and response variables.
The significance of the parametric effects and associated interactions can be determined by using the model lack of fit test, which tests the null hypothesis that coefficients present in the model are all equals to zero. The test statistic employed for this analysis can be described as: SSE F
SSR
k
n k 1
(4.5)
where SSE is the model sum of squares ,SSR the sum of squares of the residuals, n is the number of data points and k the number of model parameters. For a model to pass the lack of fit test, the calculated F-value should be less than the F 0.05. The F-value is determined from the F-table for n-k-m (3) and m-1(4) degrees of freedom. The calculated F-values for the by-pass and imperfection were less than 6.59 with high Prob > F suggesting adequate model fit. Some outliers were discovered during the analysis of the cutpoint model which resulted in an F-value of 28.70 and a low Prob > F-value of 0.0036 suggesting some lack of fit. However, when the data was re-analyzed without the two outliers, the model provided a better fit and the lack-of-fit significance disappeared. The yield to underflow model however, did not pass the lack of fit test and is thus not presented as a viable model for prediction in this study.
A measure of the ability of a model to accurately predict the response parameter values is the coefficient of determination, R 2
value. The R 2 value was determined
using the following expression:
R 1
2
^ yi yi
yi y
2
2
(4.6)
^
where yi is the observed value, y is the predicted response variable value for each test
and y is the mean experimental value obtained from all available data. An R 2 value of 1.0 means that 100 % of the response parameter variability within the range of parameter values tested can be explained by or more of the parameters and parameter interactions considered in the model. As shown in Table 4.3 the by-pass, imperfection and cutsize models have high R 2 values of 0.92, 0.88 and 0.98 respectively.
A high R 2 value, however, does not adequately quantify the ability of the model to predict the response variable. This is because the R 2 value can be high due to the presence of added parameters or interaction terms that do not contribute to the model’s
ability to predict. The adjusted coefficient of determination Radj 2 is to account for the presence of excessive terms within the model and is calculated as: Radj 2 1
n1
1 R2
n k 1
(4.7)
where n is the number of data points and k the number of parameters in the model. It is evident from Eq.(4.7) that Radj 2 must always be always less than R 2 with a high relative difference between the two values indicating the presence of excess parameters. As shown in Table 4.3 the Radj 2 values were 0.81, 0.78 and 0.96 for the by-pass, imperfection and cut point models respectively, which are considered reasonable. As shown in Figures 4.1, 4.2 and 4.3 the final models obtained provided good prediction of the experimental data.
The significance of a model parameter is indicated by Prob > F-values less than 0.100. As such, the values in Table 4.4 indicate that the viscosity modifier has a significant effect in the determination of the particle cutpoint (d 50(c)) and the classification efficiency as measured by the imperfection value. A significant interactive effect was noted between modifier concentration and apex diameter, which is likely due to the apex diameter effect on the underflow solids concentration. Interestingly the modifier did not interact with feed solids concentration in the same significant manner. The reason may be that the change in apex diameter in the test program resulted in a greater range of underflow solids concentrations as compared to the fairly narrow range of feed solid concentrations. However, both apex diameter and feed solids concentration were found to have a significant effect on particle cutsize. In terms of ultrafine by-pass reduction, only apex diameter had a statistically significant effect.
As shown in Table 4.4 some variables in the imperfection model where considered insignificant and thus where removed in order to arrive at the final model presented in this study.
Table 4.4 Classification performance models and associated model fitness values ; AP =apex diameter, MC=viscosity modifier concentration and PS =feed solids
concentration by weight. Parameter
By-pass (%)
Imperfection
Cut Point (µm)
F Value
Prob > F
F Value
Prob > F
F Value
Prob > F
8.33
0.0053
9.32
0.0017
42.68
< 0.0001
AP
49.85
0.0002
6.32
0.0331
228.60
< 0.0001
MC
0.11
0.7466
12.96
0.0058
15.74
0.0074
PS
2.33
0.1709
8.81
0.0157
26.88
0.0020
AP2
17.02
0.0044
4.27
0.0687
98.44
< 0.0001
MC
2.37
0.1674
1.44
0.2612
7.53
0.0335
2
0.88
0.3803
14.09
0.0045
100.50
< .00010
AP x MC
1.41
0.2733
15.58
0.0034
8.19
0.0287
AP x PS
0.94
0.3647
4.94
0.0679
MC x PS
0.41
0.5425
0.32
0.5927
Lack of fit
3.46
0.1307
6.64
0.0535
Intercept
2
PS
0.76
0.6210
Simulations of the various parameter effects represented by Figures 4.4, 4.5 and 4.6 showed that the response variables were dependent on the parameters considered and revealed the trends in the effects of the parameters on classifying cyclone performance. As shown in Figure 4.4 decreasing apex diameter had a positive effect in limiting ultrafine particle by-pass. Ultrafine by-pass decreased from about 28% to 11% with decreasing apex diameter within the range of 13-mm to 21-mm considered in the model.
52.00
) 45.38 m µ (
2
0 5
D 38.75 d e t c i d e r P 32.13
25.50 25.50
32.13
38.75
45.38
52.00
Actual D50 (µm)
Figure 4.1 Comparison of the experimental and empirical models for cutsize(D50).
30.00
) % ( s s 23.96 a p y B d e 17.92 t c i d e r P 11.89
5.85 5.85
11.89
17.92
23.96
30.00
Actual Bypass (%)
Figure 4.2 Comparison of the experimental and empirical models for ultrafine by-pass.
0.49
n o i t 0.43 c e f r e p m I 0.38 d e t c i d e r P 0.32
0.26 0.26
0.32
0.38
0.43
0.49
Actual Imperfection
Figure 4.3 Comparison of the experimental expe rimental and empirical models for imperfection.
This finding was in agreement with fundamental knowledge and was due to the reduced water recovery to the classifying cyclone underflow. Increasing solids concentration and decreasing apex diameter both have the negative effect of increasing particle cutsize. However, due to the greater range of change in underflow solids concentration with changing apex size than to changing feed solids concentration the effect of changing apex diameter was more pronounced (Figure 4.5). Furthermore, reducing apex diameter as shown in Figure 4.6 had the added effect of depreciating classification efficiency. In a subsequent section of this thesis, the addition of a viscosity modifier to eliminate or reduce the negative impacts of a decreasing apex diameter will be discussed in greater detail.
in-plant test program. Figure 4.7 shows the partition curves obtained from the laboratory classification tests using feed coal obtained from the Knott County Coal processing plant under different operating conditions. Solids concentration to the classifying cyclone was varied at 5% and 10% solids by weight using two apex diameters of 1.27-cm and 1.66-cm. As shown in Figure 4.8, lower cutsizes of 20 µm and 36 µm where achieved using the 5% solids as compared to the 30 µm and 73 µm values obtained when employing 10% feed solids feed content for apex diameters of 1.66-cm and 1.27-cm, respectively. Cutsize also increased for the same feed solids concentrations when apex diameter was reduced from 1.66-cm to 1.27-cm. This was due to higher solids population at the cyclone apex under both high solids content and reduced apex conditions resulting in increased viscosity effects and an elevated hindered settling environment. These conditions resist particle movement within the slurry and favor the settling of coarse particles.
Ultrafine by-pass decreased with an increase in feed solids concentration and a decrease in apex diameter. Ultrafine by-pass values of 27% and 11% were achieved at 5% solids feed concentration when using 1.66-cm and 1.27-cm apex, respectively, while values of 15% and 9% were obtained for the same apex diameters at 10% feed solids concentration. Imperfection at 5% solids increased from 0.49 to 0.53 as apex diameter was reduced thereby indicating a suppressed classification efficiency. This trend however was not observed at 10% solids where imperfection reduced from 0.64 to 0.46 at 10%. As expected, the changes in operating conditions also affected the yield of solids to the underflow stream. Yield decreased with an elevation in feed solids concentration from 75.4% to 69.8 % with the 1.66-cm apex and 70.9% to 51.9% using the 1.27-cm apex. A comparison of these values also reveals that yield was depressed with a decrease in apex diameter. A summary of the results obtained from laboratory testing is presented in Table 4.5.
100 90 80 ) % ( 70 e u 60 l a V50 n o 40 i t i t r 30 a P 20
1.66-cm Apex
10
1.27-cm Apex
0 1
10
100
1000
Particle Size (Microns) b
100 90 80 ) % ( 70 e u 60 l a V 50 n o 40 i t i t r 30 a P 20
1.66-cm Apex
10
1.27-cm Apex
0 1
10
100
1000
Particle Size (Microns) Figure 4.7 Performance curves showing the effect of apex diameter on classifying cyclone performance under (a) 5% and (b) 10% feed solids concentration by weight.
(a)
100 90
) % ( e u l a V n o i t i t r a P
80 70 60 50 40 30 20
10% Feed Solids
10
5% Feed Solids
0 1
10
100
1000
Particle Size (Microns) (b)
100 90
) % ( e u l a V n o i t i t r a P
80 70 60 50 40 30 20 10% Feed Solids
10
5% Feed Solids
0 1
10
100
1000
Particle Size (Microns) Figure 4.8 Performance curves showing the effect of solids concentration on classifying cyclone performance using (a) 1.27-cm and (b) 1.66-cm apex diameters.
Table 4.5 Laboratory classifying cyclone test results showing the effect of feed solids concentration and apex diameter. Percent Solids by weight (%)
5
10
Apex Diameter (cm)
1.66
1.27
1.66
1.27
Cutsize (µm)
20
36
30.
73
By-pass (%)
27
11
15.
9
Imperfection
0.49
0.53
0.64
0.46
Yield (%)
75.4
70.9
69.8
51.9
The conditions evaluated under the laboratory tests conditions were repeated in an in plant study conducted on a two-stage 15-cm classifying circuit. Two apexes with diameters measuring 0.64-cm and 1.5-cm were evaluated under varying rates of dilution water addition to the feed to the secondary cyclone in two different circuitry arrangements. The circuitry arrangements involved one in which the secondary cyclone overflow was recycled back to the feed to the primary cyclone and another case when there was no recycle.
The feed to the circuit was the overflow of a bank of 38-cm classifying cyclones, which was used to achieve a 150 m separation. The goal of the primary 15-cm diameter cyclone in this circuit was to maximize the rejection of the -37 m material to the overflow stream, while recovering nearly 100% of the +37 m coal to the underflow stream. The primary cyclone underflow stream was retreated in a secondary 15-cm diameter cyclone, which was operated to provide a near complete removal of the -37 m material from the final underflow product stream. To minimize ultrafine by-pass due to entrainment, water recovery to the underflow was restricted by the use of a 1.27-cm diameter apex, which is significantly smaller than the typical size.
The feed pressure to each cyclone was maintained at around 160 kPa. The initial test performed on the circuit did not add dilution water to any process stream within the circuit. As a result, the feed stream to the secondary cyclone, which was supplied by the primary underflow, had a relatively high solids concentration of 23.59% by weight.
As shown in Table 4.6, the ash content was reduced significantly from 47.14% in the feed to 28.65% in the circuit product stream, which is the secondary cyclone underflow. With a mass yield to the secondary underflow of about 34%, the amount of ash material rejected to the circuit reject stream (i.e., primary overflow) was about 79% of the total that entered in the circuit feed. The overall corrected cutsize, D50(c) for the circuit determined from partition curve data shown in Figure 4.9(a) and (b) was about 37 m with an imperfection value of approximately 0.473. This represented an efficiency improvement of about 14% over that of the primary cyclone, which had an efficiency of 0.549. The corresponding increase in the α-value was from 2.00 to 2.32. In addition, ultrafine by-pass was reduced significantly from about 25% to less than 9%. As such, the objective of obtaining a 37µm cutsize was achieved.
Although the D50(c) achieved was very close to the target cutsize and the ash reduction was significant, two concerns were identified. A density differential effect had a significant impact on the final product ash content. The density effect is evident in Table 4.6 by the elevated ash contents in the 44 x 37 m, 37 x 25 m and -25 m particle size fractions from the initial feed to the primary underflow and the secondary underflow. Somewhat unexpected was the magnitude of the increase in the -25 m fraction from 65.38% to 89.14%.
.
Table 4.6 Two-stage classifying cyclone circuit with no secondary cyclone feed dilution data.
Feed
Primary Underflow Wgt (%) Ash (%)
Secondary Underflow Wgt (%) Ash (%)
Primary Overflow Wgt (%) Ash (%)
Secondary Overflow Wgt (%) Ash (%)
Particle Size (m)
Wgt (%)
Ash (%)
+ 212
0.60
2.93
1.26
2.33
1.79
2.72
0.00
0.00
0.00
0.00
212 x 150
2.16
1.94
5.08
1.78
5.91
2.08
0.00
0.00
0.00
0.00
150 x 75
13.04
2.48
25.13
2.34
39.46
2.65
0.53
3.16
5.84
2.30
75 x 44
9.52
5.90
20.24
6.72
23.67
8.14
2.84
1.85
1.51
1.56
44 x 37
4.44
13.29
5.20
19.35
6.14
27.80
2.69
3.79
6.17
3.15
37 x 25
5.14
23.87
6.89
36.33
6.75
61.43
3.95
4.48
6.21
5.53
-25
65.08
60.97
36.20
66.78
16.28
83.82
89.99
58.58
80.27
56.96
Total
100.00
42.44
100.00
29.75
100.00
22.64
100.00
53.06
100.00
46.42
Solids (%)
6.37
(a)
14.48
43.21
3.09
1.17
100 90 80
) % ( e u l a V n o i t i t r a P
70 60 50 40 30
Primary Cyclone
20
Secondary Dilution No dilution Secondary
10 0 1
10
100
1000
(a)
100 90 80
) % ( e u l a V n o i t i t r a P
70 60 50 40 30
Primary Cyclone
20
Secondary Dilution No dilution Secondary
10 0 1
10
100
1000
Particle Size (Microns)
(b)
100 90 80
) % ( e u l a V n o i t i t r a P
70 60 50 40 30
Primary Cyclone
20
Secondary Dilution
10
No dilution Secondary
0 1
10
100
1000
Particle Size (Microns)
Figure 4.9 Classification performance curves based on (a) actual data and (b) corrected values obtained from the primary cyclone and the circuit with and without dilution.
This finding indicates that the ultrafine, high-density particles have adequate mobility to concentrate toward the apex area of the cyclone. The unfortunate result is that the product ash content of the circuit product is sensitive to the incremental recovery of the finest fractions. It is believed that the high ultrafine by-pass amounts realized from the secondary cyclone was due to the high feed solid concentration, which provided an elevated viscosity environment and hindered-settling conditions throughout the cyclone. Under typical conditions, the recovery of the hydraulically, entrainable solids to the underflow stream is limited as a result of an inward movement of fluid toward the center of the cyclone as a result of coarse particles reporting to the outer cyclone wall. Under hindered-settling conditions, the amount of solids moving toward the cyclone wall and, thus to the apex, reduces. As a result, the amount of ultrafine by-pass increases. Based on this hypothesis, the feed solids concentration to the secondary cyclone was reduced by the addition of water into the process stream.
As shown in Table 4.7, the solids concentration of the secondary feed was reduced from 23.59% to 14.48% as a result of the water injection. This resulted in a reduction in the product ash to 22.64%. The amount of ash rejected to the circuit tailings was improved to 81% while the mass yield to the circuit product slightly increased to 34.9%. Figure 4.9 shows that the overall classification performance of the circuit resulted in a D50(c) of about 37 m and an improved classification performance relative to the no
dilution circuit represented by an imperfection value of 0.466 and α-value of 2.36 while still maintaining a low ultrafine by-pass value of less than 9%. Thus, the primary goal of achieving a 37µm cutsize from the two-stage circuit was achieved. Furthermore ultrafine particle by-pass was reduced to below 10%. It was also evident that circuit with the secondary feed dilution was the more efficient circuit, likely due to a higher degree of particle free settling. However, approximately 6 weight units of + 25 micron (+500 mesh) material with an ash content less than 4% reported to the cyclone overflow stream which would be rejected to the fine coal waste stream in a typical operating plant.
This
observation is critical for a number of plants that are currently using a single stage of classifying cyclones to deslime flotation feed.
Table 3.7 Two-stage classifying cyclone circuit with secondary cyclone feed dilution data.
Feed
Primary Underflow Wgt (%) Ash (%)
Secondary Underflow Wgt (%) Ash (%)
Primary Overflow Wgt (%) Ash (%)
Secondary Overflow Wgt (%) Ash (%)
Particle Size (m)
Wgt (%)
Ash (%)
+ 212
0.60
2.55
1.33
2.36
2.12
2.41
0.00
0.00
0.00
0.00
212 x 150
2.69
1.69
6.16
1.75
10.04
1.74
0.00
0.00
0.00
0.00
150 x 75
11.69
2.68
27.67
2.73
43.79
2.88
0.71
3.09
3.39
1.81
75 x 44
9.34
7.93
17.13
9.32
13.61
14.46
3.50
2.24
19.47
2.12
44 x 37
3.35
16.27
5.11
28.57
4.76
58.50
2.17
3.14
5.93
4.37
37 x 25
5.24
30.90
6.82
51.84
8.21
83.26
3.72
6.00
7.13
9.36
-25
67.09
65.38
35.78
72.22
17.47
89.14
89.90
62.53
64.08
63.30
Total
100.00
47.14
100.00
33.33
100.00
28.65
100.00
56.61
100.00
41.96
Solids (%)
8.02
23.59
51.21
5.28
3.41
86
The economical significance can be realized by considering a 1000 tph plant being fed coal containing 8% -150 micron (-100 mesh) material. Using a 15-cm (6-in) diameter Gmax cyclone under the same conditions of this study would result in a loss of nearly 5 tph of 4% ash coal. Assuming 5000 hrs annually for plant operating time and a sales price of $50/ton, the annual revenue loss is $1.25 million. A previous study found that the addition of a classifying cyclone to retreat the primary overflow stream has the potential to recover a significant portion of the by- passed coal (Firth and O’Brien, 2003).
When the circuit was operated without the recycle stream very little change in performance was observed. This finding was in contrast to fundamental predictions provided by classifying cyclone circuit simulation studies conducted by Honaker et al (2006) which indicate that circuits which do not incorporate recycle streams have lower
The economical significance can be realized by considering a 1000 tph plant being fed coal containing 8% -150 micron (-100 mesh) material. Using a 15-cm (6-in) diameter Gmax cyclone under the same conditions of this study would result in a loss of nearly 5 tph of 4% ash coal. Assuming 5000 hrs annually for plant operating time and a sales price of $50/ton, the annual revenue loss is $1.25 million. A previous study found that the addition of a classifying cyclone to retreat the primary overflow stream has the potential to recover a significant portion of the by- passed coal (Firth and O’Brien, 2003).
When the circuit was operated without the recycle stream very little change in performance was observed. This finding was in contrast to fundamental predictions provided by classifying cyclone circuit simulation studies conducted by Honaker et al (2006) which indicate that circuits which do not incorporate recycle streams have lower efficiencies than circuits that do. However, the higher feed pressure to the secondary cyclone may have provided a near 100% recovery of the particles having a size greater than about 40 microns to the underflow stream.
As such, recycling would not be
beneficial to circuit performance.
The overall classification performance of the circuit resulted in a D50(c) of about 40 m and an imperfection, α-value and by-pass values of 0.394, 2.78 and 9% respectively. A relatively small increase in the corrected particle cutsize was realized, i.e., 35 to 40 microns for the no-recycle circuit. As shown in Table 4.8, product ash was reduced from 43.09% to 22.73%. The tailings ash rejection was 79.4% while the mass yield to the circuit product was slightly reduced to 33.9%.
The overall reduction in ash content did not meet the targeted goal despite the achievement of a particle cutsize of 37 microns, excellent ultrafine classification efficiency and a relatively low ultrafine by-pass amount of 9%. Two reasons accounted for this. These included the recovery of a significant amount of -25 micron (-500 mesh) material into the secondary underflow stream and a solid density effect on classification performance as previously described. 89
Table 4.8 Two-stage classifying cyclone circuit with secondary cyclone feed dilution and no recycle data.
Primary Secondary Primary Secondary Underflow Underflow Overflow Overflow Wgt (%) Ash (%) Wgt (%) Ash (%) Wgt (%) Ash (%) Wgt (%) Ash (%) Wgt (%) Ash (%)
Particle Size (m)
Feed
+ 212
0.55
2.63
1.27
2.28
1.93
3.64
0.00
0.00
0.00
0.00
212 x 150
1.96
1.64
4.24
1.95
6.64
1.98
0.00
0.00
0.00
0.00
150 x 75
13.11
2.42
29.07
2.47
40.09
2.48
0.69
2.32
1.79
2.17
75 x 44
10.24
6.27
19.54
7.00
22.26
7.84
3.43
2.24
5.60
2.43
44 x 37
3.18
13.01
5.75
20.48
6.32
31.32
2.75
6.41
4.72
3.30
37 x 25
5.06
23.87
6.22
40.39
4.62
61.08
3.56
5.07
6.21
6.32
-25
65.91
61.39
33.91
66.88
18.14
82.59
89.56
59.77
81.67
58.42
Total
100.00
42.68
100.00
28.90
100.00
22.73
100.00
53.13
100.00
48.43
Solids (%)
6.38
15.41
40.51
4.10
0.90
89
The recovery of ultrafine material to the underflow stream of a classifying cyclone is typically described to occur as a result of hydraulic entrainment. However, it is apparent from the data obtained that true classification occurs on a portion of the -25 micron material. The ash content of this fraction increased from around 60.97% in the feed to 83.82% in the secondary cyclone underflow. In fact, the ash contents of all particle size fractions below 75 microns increased in the same manner. The elevated ash contents played a significant role in limiting the ability to achieve an acceptable product ash value. If the ash contents for each size fraction in the secondary underflow stream were the same as those in the feed, the circuit product ash content would be 14.9% rather than 22.6%, which would likely be acceptable after drying using a screen-bowl centrifuge assuming an additional loss of -25 micron material through the process.
The recovery of ultrafine material to the underflow stream of a classifying cyclone is typically described to occur as a result of hydraulic entrainment. However, it is apparent from the data obtained that true classification occurs on a portion of the -25 micron material. The ash content of this fraction increased from around 60.97% in the feed to 83.82% in the secondary cyclone underflow. In fact, the ash contents of all particle size fractions below 75 microns increased in the same manner. The elevated ash contents played a significant role in limiting the ability to achieve an acceptable product ash value. If the ash contents for each size fraction in the secondary underflow stream were the same as those in the feed, the circuit product ash content would be 14.9% rather than 22.6%, which would likely be acceptable after drying using a screen-bowl centrifuge assuming an additional loss of -25 micron material through the process.
In an effort to reduce ultrafine by-pass from the overall circuit, the apex size on the primary cyclone unit was reduced from 1.27-cm (0.5-in) to 0.635-cm (0.25-in). Water recovery and thus hydraulic entrainment to the underflow stream of the primary cyclone was significantly reduced. Figure 4.10 shows that the ultrafine by-pass was decreased from the 25% value obtained from the primary cyclone using the larger apex to 14%.As a result, the amount of by-pass achieved from the 2-stage circuit without recycle reached a low value of 6% while recycling produced a by-pass of 9%. In this case, however, the recycling of the secondary cyclone overflow stream to the circuit feed had a significant impact on circuit efficiency.
The imperfection and -values for the 2-stage circuit without recycle were 0.47 and 2.32, while the imperfection and values for the circuit with recycle were 0.41 and 2.67 respectively, which is a 15% efficiency improvement. The particle cutsize was 37 microns for the circuit with recycle and 56 microns without recycle, which is a negative impact associated with the no recycle circuit.
89
100
(a)
2-Stage with Recycle Primary
90
) % ( w o l f r e d n U o t y t i l i b a b o r P
2-Stage Circuit with Recycle 80 2-Stage Circuit without Recycle 70 60 50 40 30 20 10 0 1
10
100
1000
Particle Size (microns)
(b) 100 2-Stage with Recycle Primary
90
) % ( w o l f r e d n U o t y t i l i b a b o r P
2-Stage Circuit with Recycle 80 2-Stage Circuit without Recycle 70 60 50 40 30 20 10 0 1
10
100
1000
Particle Size (microns)
Figure 4.10 Classification performance curves based on (a) actual values and (b) corrected values for two-stage classifying cyclone with 0.635 cm apex in primary and secondary cyclone. 90
The particle cutsize and the classification efficiency provided by using a smaller apex in the primary cyclone were nearly equal to those obtained by the larger apex and the amount of ultrafine was significantly reduced. The expectation from the performance improvement was a decrease in the circuit ash content reporting from the secondary cyclone underflow stream. However, the circuit ash content increased to around 29.00% in both cases as shown in Tables 4.9 and 4.10. This finding was due to an elevation in the amount of -25 micron material in circuit feed, a greater quantity of mineral matter in the same size fraction and thus an increase in the overall feed ash content to 57.33%.
Table 4.9 Particle size-by-size analysis data of overall circuit process stream samples obtained using an apex diameter of 0.635 cm in the primary and secondary classifying cyclones with no recycle.
Feed
Circuit Product
Circuit Reject
Size Fraction (microns)
Wgt %
Ash %
Wgt %
Ash %
Wgt %
Ash %
+ 212
0.25
3.92
3.64
8.64
0.00
0.00
212 x 150
0.83
2.70
5.10
6.66
0.00
0.00
150 x 75
7.29
2.92
32.90
4.20
0.57
2.26
75 x 45
7.37
6.87
24.04
10.31
3.49
2.79
45 x 36
2.56
12.58
6.72
33.88
2.45
4.56
36 x 25
4.39
23.89
6.58
65.53
3.03
7.32
-25
77.32
69.84
21.02
86.98
90.46
68.72
Total
100.00
56.12
100.00
29.39
100.00
62.61
91
Table 4.10 Particle size-by-size analysis data of overall circuit process stream samples obtained using an apex diameter of 0.635 cm in the primary and secondary classifying cyclones with recycle.
Feed
Circuit Product
Circuit Reject
Size Fraction (microns)
Wgt (%)
Ash (%)
Wgt (%)
Ash (%)
Wgt (%)
Ash (%)
+ 212
0.24
3.61
1.12
4.36
0.00
0.00
212 x 150
0.72
2.48
3.29
3.32
0.00
0.00
150 x 75
6.46
2.78
29.20
2.88
0.29
2.40
75 x 45
7.72
6.28
27.81
7.71
2.19
2.76
45 x 36
3.71
12.40
7.08
22.36
1.72
4.02
36 x 25
3.18
24.11
6.98
52.49
3.21
6.51
-25
77.97
71.07
24.52
85.20
92.58
70.46
Total
100.00
57.33
100.00
29.28
100.00
65.58
The ash rejection achieved by the circuit was increased to slightly over 90% but the recovery of ultrafine material and the density effect resulted in the high circuit product ash value. The overall mass yield to the secondary cyclone underflow was 19.5% with no recycle and 22.7% with recycle, which is lower than the previously reported values in earlier sections of this document, due in part, to the higher amounts of mineral matter in the feed.
As shown in Figure 4.10, the use of a 0.635-cm (0.25-in) diameter apex in the primary and secondary cyclones provided lower circuit ultrafine by-pass values in the range of 6-7% as compared to 9-10% obtained using a 1.27-cm (0.50-in) in the primary cyclone apex and 0.635-cm (0.25-in) apex in the secondary unit. Also, a slightly higher classification efficiency was realized using the smaller apex. The lower yield and 92
elevated ash rejection values are reflective of a greater amount of -25 micron mineral matter in the feed coal as compared to the feed coal provided during the tests involving the 1.27-cm (0.5-in) diameter apex.
A study to investigate the effect of varying the level of feed dilution to the secondary classifying cyclone was incorporated into test program. The classification performance curves shown in Figure 4.11 indicate that diluting the feed to the secondary cyclone provided a better classification performance and a lower separation cutsize than when no dilution water was added. This was likely due to an improved degree of free settling among the particles at the lower solids concentration as well as reduced viscosity effects.
Performance comparisons of the various circuits and conditions studied is provided in Table 4.11. As shown in Table 4.11, the use of a 0.635-cm (0.25-in) diameter apex in the primary and secondary cyclones tended to provide lower ultrafine by-pass and slightly higher classification efficiency. The lower yield and elevated ash rejection values are reflective of a greater amount of -25 micron mineral matter in the feed coal as compared to the feed coal provided during the tests involving the 1.27-cm (0.5-in) diameter apex.
93
(a)
100 Medium Dilution With Recycle
90
Medium Dilution No Recycle
80
High Dilution With Recycle
r 70 e b m60 u N n 50 o i t i 40 t r a P 30
High Dilution No Recycle
20 10 0 1
10
100
1000
Particle Size (microns)
(b)
100 Medium Dilution With Recycle
90
Medium Dilution No Recycle
80
High Dilution With Recycle
r 70 e b m 60 u N n 50 o i t i 40 t r a P 30
High Dilution No Recycle
20 10 0 1
10
100
1000
Particle Size (microns)
Figure 4.11 Classification performance curves based on (a) actual values and (b) corrected values achieved using an apex diameter of 0.635-cm for different two-stage classifying cyclone circuit configurations at different dilution levels. 94
Table 4.11 Summary of classification circuit performances achieved under various operating conditions. Apex Diameter (primary x secondary)
0.5 x 0.25
0.25 x 0.25
D50(c) microns
Imp. Value
Value
By-Pass (%)
Circuit U/F Yield ( %)
Primary Only
35
0.549
2.00
25
45.5
68.1
Dilution-No Recycle
40
0.394
2.78
10
34.9
81.6
DilutionRecycle
37
0.466
2.36
9
34.9
81.4
No DilutionRecycle
37
0.473
2.32
9
33.9
79.4
Primary Only
35
0.497
2.21
14
26.4
81.1
High Dilution Recycle
37
0.387
2.84
7
22.4
88.8
Med. Dilution Recycle
56
0.334
3.29
6
15.1
90.1
Hi Dilution – No Recycle
47
0.386
2.84
6
19.3
90.6
Med. Dilution – No Recycle
50
0.359
3.07
6
21.0
88.9
Classification Performance Condition
Circuit Ash Reject (%)
4.2.2 Coal and Mineral Matter Partitioning
To provide a clearer understanding of the particle density effect, the data the data obtained from the various tests were used to determine the mineral matter and coal distributions and subsequently their respective classification partitioning. Mineral matter content for each size fraction was estimated using the Parr formula defined as: MineralMatter (%) 108 . Ash(%) 0.55Sulfur (%)
95
(4.8)
By definition the amount of pure coal can be determined by subtracting the percentage mineral matter (Eq.(4.8)) from 100. This methodology assumes that the coal and mineral matter are completely liberated. Although this assumption may not be 100% correct, liberation below 100 microns is nearly complete.
Classification efficiency achieved on the coal particles was exceptional as indicated by the high I -value and -values obtained from each test. Figure 4.12 shows that, in the tests using the 0.635-cm apex in the secondary cyclone, low imperfection and high -values of 0.274 and 4.01, respectively, were achieved. Efficiency achieved on the coal particles was 44% better than the overall classification performance described by the curves in Figure 4.9.
The data for the mineral matter has some randomness in the middle size fractions due to the relatively small amounts of mineral mass and the elevated sensitivity to experimental error. However, a comparison of the mineral matter and coal classification curves provided useful information in understanding the effect that these two components had on classification performance.
The particle cutsize ( D50(c)) for the mineral matter (< 25 microns) was significantly lower than the cutsize achieved on the coal particles (around 40 microns). This finding is in agreement with fundamental reasoning. According to the Plitt equation, the difference in the cutsize between the coal and mineral matter in a given classifying cyclone is: D50( c ) MM D50( c )COAL
COAL MM
1 1
0.5
40
0.3 1.7
96
0.5
17 microns
(4.9)
(a)
100 90 80
) % ( e u l a V n o i t i t r a P
70 60
Primary Cyclone - Coal
50
Primary Cyclone Mineral Matter " Circuit With Recycle Coal Circuit With Recycle Mineral Matter Circuit Without Recycle Coal Circuit Without Recycle Mineral Matter
40 30 20 10 0 1
10
100
1000
Particle Size (Microns)
(b)
100 90 80
) % ( e u l a V n o i t i t r a P
70 60
Primary Cyclone - Coal
50
Primary Cyclone Mineral Matter Circuit With Recycle Coal Circuit With Recycle Mineral Matter Circuit Without Recycle Coal Circuit Without Recycle Mineral Matter
40 30 20 10 0 1
10
100
1000
Particle Size (Microns)
Figure 4.12 Coal and mineral matter size separation achieved by the classification circuit according to the (a) Actual and (b) Corrected performance curves; Primary apex = 1.27cm, Secondary apex = 0.635cm. 97
In Equation 4.9 previously described, COAL is the relative density of coal (=1.3) and MM the relative density of the mineral matter (=2.7). In addition, the recovery of mineral
matter to the underflow stream was significantly greater than coal for all particle size fractions below 75 microns.
In addition to increased viscosity effects, a transition from Newtonian to non Newtonian conditions occurs as solids concentrate towards the apex as a direct result of the classification process. This implies the presence of a yield stress within the slurry, which opposes the relative movement between the particle and the fluid. The ability to overcome the yield stress is subject to the gravitational or centrifugal force exerted on the particles, which is a function of particle size and density. As such, a critical particle size exists below which particles of a given density cannot supercede the yield stress and thus become hydraulically entrained and unclassifiable. An expression that can be used to determine the critical size is:
d c
3 o
(4.9)
2 g c m
where c is the particle density m the medium density and 0 is the yield stress of the medium and g is the acceleration due to gravity.
As stated in the above paragraph, a particle having a size below the critical value will be entrained and thereby be recovered to the cyclone underflow stream at a rate corresponding to water recovery. Assuming a yield stress of 20 centipoise, the critical particle size for pure coal (c = 1.3) is 32 microns and the value for mineral matter ( c = 2.65) is 6 microns. Particles having a size below these critical values would therefore be hydraulically entrained under this rheological condition and report with the water into the cyclone output streams.
It is therefore evident that this critical particle size is strongly dependent on particle density with the critical size for high-density particles being smaller than that of low98
density particles. The data provides evidence of this by showing that coal is entrained at a coarser size than mineral matter, a finding, which is in agreement with fundamental theory.
A comparison of the coal and mineral
partition curve data achieved under various
circuit conditions indicated the following trends (Table 4.12). The efficiency of the coal particles as compared to the overall partition curves was higher in the tests in which the 1.27-cm apex in the primary and 0.635-cm apex in the secondary was used. This circuit provided efficiencies that were up to 66% better than the overall circuit efficiency. Using the 0.635-cm apex in both the primary and secondary classifying cyclone gave efficiencies that were up to 34% better than the overall classification curve. This suggests that increased viscosity and other particle interaction effects brought about by increased solids concentration within the cyclone at the smaller apex diameter had significant negative impact on coal classification performance. Furthermore particle cutsize for coal was generally higher in the circuit in which the 0.635-cm apex was used both cyclones than in the 1.27-cm primary and 0.635-cm secondary apex circuit.
99
Table 4.12 Summary of classification circuit performances achieved for coa l and mineral matter under various operating conditions. Apex Diameter (primary x secondary)
0.5 x 0.25
0.25 x 0.25
Coal Condition
Mineral matter
D50(c) microns
Imp. Value
Value
By-Pass (%)
D50(c) microns
By-Pass (%)
Primary Only
40
0.388
2.84
20
8.5
27
Dilution-No Recycle
43
0.274
4.01
3
8.8
13
DilutionRecycle
41
0.287
3.83
2
8.6
12
No DilutionRecycle
51
0.284
3.86
1
9
12
41
0.293
3.75
2
9
8
58
0.275
3.99
3
11
8
48
0.297
3.70
2
10
7
49
0.265
4.14
3
10
8
High Dilution Recycle Med. Dilution Recycle Hi Dilution – No Recycle Med. Dilution – No Recycle
4.2.3 Circuit Modeling
Studies conducted in the minerals industry have shown that the performance of classifying cyclones can be improved using multi-stage circuits. The capabilities of these circuits can be mathematically simulated using an empirical partition model. The model is used to calculate the probability ( P ) that a particle of diameter ( D) reports to the oversize product. A popular partition model for classifying cyclones is given by:
exp{ D / D(50C ) 1 exp{ D / D50( ) } exp{ } 2 C
P (1 )
100
(4.10)
in which D50C is the particle cutsize, α is the sharpness of the separation and is the fractional by-pass of fines to the cyclone underflow stream. The value of α is typically in the range of 1-6 for mineral classification circuits. The value of is usually assumed to be equal to the fraction of feed water reporting to the underflow. Once the partition function is defined, the sizing performance of circuits incorporating multiple units can be mathematically calculated if the partitioning behavior of each unit is the same. This provision dictates that all particles of the same size have the same probability of reporting to the oversize stream in all units.
The total by-pass (*) for the actual circuit can be calculated for any number (N) of downstream units using: * N .
(4.11)
The ultrafine by-pass values for the individual classifying cyclone units in the 1.27-cm primary apex with 0.635-cm secondary apex circuit were first determined. Then using Eqn.(4.11), the by-pass for the overall circuit
with recycle and no recycle were
calculated. The calculated by-pass values were 10 % in both cases, which were in close agreement with the 10% and 9% obtained using the recycle circuit and no recycle circuits, respectively. The partition curves for the individual classifying cyclones units were then determined and used to model various circuitry arrangements using linear analysis (Table 4.13).
Models were first developed for the no recycle circuit in order to compare the theoretical performance of that circuit with actual performance. The effect of recycling on this circuit was then also modeled. The recycle circuit was modeled using the relationship below: P* P1 P 2
(4.12)
in which P 1 and P 2 are the partition factors for the single stage primary unit and single stage secondary unit respectively while P * is the partition factor obtained from the two-stage circuit involving both units.
101
Table 4.13 Corrected partition numbers for 1.27-cm and 0.635-cm classifying cyclone units. Partition number (%) 1.27-cm apex
0.635-cm apex
100
100
100
100
96
98
76
87
50
67
45
49
0
0
The no recycle circuit was modeled using the following relationship: P *
P1 P 2 1 P1 P1 P 2
(4.13)
Results of the simulation and experimental test work presented in Figure 4.13 showed that use of two stage cyclones in place of a single cyclone resulted in higher cutsizes and lower by-pass values and improved efficiency. A cutsize of 47 µm was achieved for the no recycle circuit which is comparable to the of 48µm cutsize predicted by the model. Even though the model predicted a better classification performance with an imperfection of 0.370 and an
-value of 2.97, these values were still sufficiently close to the
imperfection of 0.394 and
-value of 2.78 achieved in experimental test work.
According to Honaker et al (2006), using a recycle stream has the benefit of improving the overall sharpness of the partition curve and lessens the impact of the circuit on increasing particle cutsize.
102
(a)
100 90 80 % ( 70 e u 60 l a V 50 n o i t 40 i t r a P 30
20
Recycle No Recycle
10 0 1
10
100
1000
Particle Size (Microns) (b) 100 90 80 % ( 70 e u 60 l a V 50 n o i t 40 i t r a P 30
20
Recycle No Recycle
10 0 1
10
100
1000
Particle Size (Microns) 4.13 Performance curves of the two-stage circuit models based on (a) actual values and (b) corrected values.
103
The circuit simulation that involved recycling the secondary overflow back to the primary cyclone had a positive effect on classification performance. Imperfection values of 0.360 and
-values
of 3.05 as well as a cutsize of 43µm were obtained from the
model. This represents an improved performance over that of the no recycle circuit while allowing a lower separation cutsize. It was believed that the difference between the experimental and model results was influenced by the density effect, which according to Firth and O’Brien, 2003 may lead to misleading results in circuit simulations if neglected.
However, the analysis showed that the simulated results provided trends, which were in agreement with the observed data.
The performance advantages in employing three stage circuits was also simulated and evaluated. The three circuits shown in Figure 4.14 were modeled. The three-stage circuit with no recycle, Figure 4.14(a) was modeled using the relationship: P* P1 P2 P 3
(4.14)
Another three-stage circuit configuration, incorporating the recycle of the overflow stream to the feed of the previous unit (Figure 4.14(b)) was modeled using the relationship: P *
P1 P2 P 3 1 P1 P1 P2 P 3
(4.15)
According to Firth and O’Brien (2003), the three-stage counter flow circuit (Figure
4.14 (c)) appears to be capable of achieving reasonable coal recovery while at the same time restricting the amount of ultrafine mineral matter. This circuit employs a rougher, scavenger and a cleaner unit with recycle. A simulation of the classification performance achievable by using this circuit was also modeled using the relationship described below as: P *
P1 P 2
1 P1 1 P3 P1 P 2
104
(4.16)
(a)
(b)
(c)
Figure 4.14 Three Stage Circuits with (a) No Recycle (b) Recycle (c) Countercurrent.
The simulation results shown in Figure 4.15 indicate that three-stage circuits should provide an improved performance over that achieved by two-stage circuits. The threestage circuit with no recycle configuration would result in a lower by-pass of 4% but higher cutsize of 54 µm and an better efficiency represented by an imperfection of 0.263 and
-value
of 4.18 when compared to the two-stage no with no recycle configuration.
The three-stage circuit with recycle, according to the simulation provided even better results by reducing by-pass to 4 % while at the same time improving the imperfection and -value
to 0.255 and 4.30 respectively at a cutsize of 46µm. The counter current circuit
provided the benefit of providing the lowest cutsize of all the circuits of 32 µm. However, it provided an imperfection of 0.318 and an
-value
of 3.46 making it the least efficient
of the three-stage circuits. The results obtained from the simulation models are summarized Table 4.14.
105
100 90 ) 80 % ( 70 r e b60 m u N50 n o40 i t i t r 30 a P 20
No Recycle Recycle Counter current
10 0 1
10
100
1000
Particle Size (Microns) 100 90 ) 80 % ( 70 r e b60 m u N50 n o40 i t i t r 30 a P 20
No Recycle Recycle Counter current
10 0 1
10
100
1000
Particle Size (Microns) Figure 4.15 Simulated performance curves of the three-stage circuit models based on (a) actual values and (b) corrected values.
106
Table 4.14 Summary of the results obtained from classifying cyclone circuit modeling. Model Results 2-Stage
2-Stage
3-Stage
3-Stage
Counter
No Recycle
Recycle
No Recycle
Recycle
Current
D50C
47
43
54
48
32
Imp
0.370
0.360
0.263
0.255
0.318
-Value
2.97
3.05
4.18
4.30
3.46
By-Pass
10
10
4
4
4
4.3 Viscosity Effects
The magnitude of the viscosity effect on classification efficiency is not very clear based on published data. If elevated medium viscosity affects all particles in the same manner regardless of particle size, no impact on efficiency would occur. In tests investigating the impact of temperature on classification performance, increasing temperature reduced viscosity and thus d 50. However, efficiency was not improved (Kawatra, 1988). On the other hand, it is well documented that the separation efficiency achieved by particles settling in a dense medium varies according to the following expression (Scott, 1988):
I
d 75 d 25 2d 50
k d in
(4.17)
in which I is the imperfection value for particle size d i which has a value of zero for perfect separations, k a function of medium viscosity and cycle geometry, n a constant, and d 75 and d 25 the particle sizes having a 75% and 25% chance of reporting to the underflow stream, respectively. As such, an increase in medium viscosity reduces
107
efficiency by an amount that is subject to the characteristics of the feed solids and medium.
An additional effect of an increase in solid concentration is the development of a shear stress when the system is not subjected to shear, which marks the transition from a Newtonian to a non-Newtonian suspension. According to an expression developed by Laapas (1983), the critical solid concentration by volume (c) corresponding to the transition is a function of the particle size, surface area and d ensity, i.e.,
.
.
38.03d 0 39 0 87 c
(4.18)
s2.23
where d is the mean particle size in mm and the particle sphericity. In coal applications, kaolin clay is commonly the major mineral present in the ultrafine size fraction, which typically represents greater than 60% of the feed solids. If a relative solid density of 2.7 and a sphericity factor of 0.1 are assumed, the critical solids concentration for a kaolin suspension with a mean particle size of 5 microns is 7.4% by volume or 17.5% by weight. The typical classifying cyclone feed has a solids content between 5% to 10% by weight while the underflow solids content varies from 30% to 50% by weight. As such, a typical classifying cyclone in the coal industry experiences a transition from Newtonian to non-Newtonian characteristics during the classification process.
As a result of the presence of a yield stress (o), particles having a size below a critical value (d c) will not be able to penetrate the fluid and thus will bypass the classification process. This implies that the transition to non-Newtonian flow will result in an increase in the amount of material reporting to the cyclone product streams as a function of the water split. The critical particle size can be determined using the expression: d c
3 o
(4.19)
2 g c m
108
in which g is gravitational acceleration. Assuming a yield stress of 20 centipoise, the critical particle size for pure coal ( c = 1.3) is 32 microns and the value for mineral matter (c = 2.65) is 6 microns. Particles having a size below these critical values are hydraulically entrained under this rheological condition and report with the water into the cyclone output streams. It was therefore expected that the use of the modifier would limit the yield stress and enhance the movement of these fine particles.
A test performed using a relatively low feed solids concentration and large apex diameter resulted in a particle size cutpoint (d 50) of around 38 microns while bypassing approximately 16% of the ultrafine material to the underflow stream as shown in Figure 4.16. Adding the viscosity modifier under this condition provided a reduction in by-pass by 4 absolute percentage points and improved efficiency but had minimal effect on d 50.
100
) % ( 75 w o l f r e d n U 50 o t y t i l i b a b o r 25 P
5% Solids, Apex Diameter = 16.6 mm No Chemical SDS 1.0 kg/t
0 10
30
50
70
90
110
130
150
Particle Size (microns)
Figure 4.16 Effect of viscosity modifier addition on the classification performance achieved with a feed solids content of 5% by weight and a relatively large apex diameter.
109
However, when the apex diameter was reduced to limit the by-pass to around 10%, the modifier addition converted the roping of the underflow stream to a more effective condition, significantly improved efficiency and decreased d 50 as shown in Figure 4.17.
The models developed based on the data acquired indicated that the addition of the viscosity modifier decreased d 50 and the amount of ultrafine by-pass while significantly decreasing the imperfection value, which indicates an improvement in classification efficiency.
100
) % ( 75 w o l f r e d n U 50 o t y t i l i b a b o r 25 P
7.5% Solids, Apex Diameter = 12.7 mm No Chemical SDS 1.0 kg/t
0 10
30
50
70
90
110
130
150
Particle Size (microns)
Figure 4.17 Effect of viscosity modifier addition on the classification performance achieved with a feed solids content of 7.5% by weight and a small apex diameter.
110
The cutsize (d 50) reduction was consistent with existing cyclone models and a previous study that used elevated medium temperatures (Kawatra.1988) to reduce viscosity. As described by Equation (4.19), high solid concentrations results in a non-Newtonian slurry that possesses a yield stress. As a result, critical particle size below which hydraulic entrainment occurs thereby causing an increase in the amount of bypassed solids to the underflow stream. The by-pass model indicates that the addition of the viscosity modifier reduces the amount of by-pass, which may be due to a reduction in the yield stress and thus the critical particle size.
To assist in better understanding the impact of viscosity modifiers the experimental models described in Eqs.(4.1)-(4.3) were used. As indicated previously, viscosity modifier concentration was significant in the determination of the particle cutsize, imperfection and by-pass. As shown in Figure 4.18, the viscosity modifier had minimal affect on d 50 when a large apex diameter was used, which was likely due to the low solid concentrations in the underflow stream (i.e., ~30% by weight). Decreasing the apex diameter from 20 to 12 mm restricted the underflow stream, which increased the underflow solid concentration to values exceeding 50% by weight and elevated d 50. The elevated solids concentration within the cyclone increased viscosity and depressed classification efficiency as indicated by a rise in the imperfection value shown in Figure 4.19.
When the viscosity modifier is added, the d 50 decreased from about 50 to 40 microns while improving efficiency by nearly 200%. As a result of using the small apex, ultrafine by-pass was limited to about 5%, which equates to a significant decrease from the 20% value achieved using the larger apex sizes.
111
leads to a condition that is commonly referred to as roping, which results in the elimination of the air core and low classification efficiencies.
The addition of two
viscosity modifier types, i.e., sodium dodecyl sulfate (SDS) and NALCO 9762, was investigated in an effort to reduce the negative effects of the high-density underflow stream. In these tests, feed solid concentrations greater than 10% by weight were used along with a small apex (12.7-mm). The experimental conditions, feed slurry viscosity values and the underflow solids concentrations are shown in Table 4.13.
Solids concentrations greater than 50% by weight were generated in the cyclone underflow stream during each test. However, when no viscosity modifier was added, roping conditions were observed which result in poor classification efficiencies as indicated by the slopes of the partition curves in Figures 4.20 and 4.21.
Table 4.13 Experimental conditions used to generate a high-density classifying cyclone underflow; apex diameter = 12.7 mm.
113
Modifier
Feed
Underflow
Feed
Dosage
Solids
Solids
Viscosity
(kg/t)
(% wght)
(% wght)
(cp)
None
0.0
10
50.71
2.16
None
0.0
12.5
51.58
3.12
None
0.0
15
52.59
3.96
None
0.0
18
53.48
4.26
SDS
0.8
18
53.83
4.09
SDS
0.8
15
52.86
3.71
SDS
0.8
12.5
51.78
3.02
SDS
0.8
10
50.85
2.01
None
0.0
10
50.67
2.12
None
0.0
12.5
51.86
3.20
None
0.0
15
52.77
3.78
None
0.0
18
53.51
4.33
Nalco 9762
0.5
18
54.72
3.95
Nalco 9762
0.5
15
53.65
3.57
Nalco 9762
0.5
12.5
51.93
2.66
Nalco 9762
0.5
10
50.97
1.98
Modifier
114
100
100
75
75
) % ( e u l a V50 n o i t i t r a P
) % ( e u l a V 50 n o i t i t r a P
10% Solids No modifier SDS: 0.8 kg/t
15% Solids No modifier SDS: 0.8 kg/t
25
25
0
0 10
60
110
10
160
60
110
160
210
260
Size (microns)
Size (microns)
Figure 4.20 Partition curves for two different feed solids concentration with a nd without addition of sodium dodecyl sulfate. 100
100
75
75
) % ( e u l a V 50 n o i t i t r a P
) % ( e u l a V 50 n o i t i t r a P
12.5% Solids
25
18% Solids
25
No modifier
No modifier
NALCO 9762: 0.5 kg/t
NALCO 9762: 0.5 kg/t 0
0 10
60
110
160
210
260
Size microns
10
60
110
160
210
Size microns
Figure 4.21 Partition curves for two different feed solids concentration with and without addition of NALCO 9762 viscosity modifier.
115
260
After adding the viscosity modifier, the roping condition was eliminated and an air core established. As a result, classification efficiency improved significantly. Ultrafine by-pass to the underflow stream was maintained within the range of 5% to 10% for all tests. Using the viscosity modifier, excellent classification efficiencies were realized at elevated feed solids concentrations, which indicates the potential to increase mass throughput per cyclone.
The effect of the feed slurry viscosity on the particle size cutpoint under the conditions described in Table 4.13 is shown in Figure 4.22. The logarithmic plot yielded a straight line with a slope of about 0.62. This value is in contrast to the results of an earlier study Kawatra et al (1996) where the reported slope was 0.35. The difference may be explained by the significantly different test conditions between the two studies. The results reported in this publication were generated under conditions providing very high underflow solid concentrations whereas the previously reported data was obtained over a broader range of conditions.
2.5
2.0
) 0 5 d ( g o l 1.5
1.0 0.25
0.35
0.45
0.55
0.65
log(viscosity)
Figure 4.22 Feed viscosity effect on the particle size cutpoint.
116
0.75
(min) The downstream concentration processTime used to treat classifying cyclone product 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
streams is typically froth flotation. The effect of the two viscosity modifiers on flotation 0.0
performance was evaluated on the flotation feed from two preparation plants. As shown in Figure 4.23 and 4.24, the flotation kinetics were not significantly changed due to the -0.5
addition of the viscosity modifiers. In the case of West SDSVirginia addition Coal at a concentration of 0.8 -1 kg/ton, the initial rate constant changes from 1.78 No minModifier with no addition to 1.58 min-1. -1.0 The final rate ) changes from 0.42 min-1 with no addition to 0.31 min-1 (Figure 2.23). In the
case of
R 1 ( NALCO n l
SDS: 0.8 kg/t
9762, the initial rate remained same at 3.63 min-1 (Figure 2.24).
-1.5 However, the final rate changes from 0.05 min-1 with no addition to 0.12 min-1 with 0.5
kg/t of modifier addition. -2.0
Time (min)
-2.5 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0.0
-0.5
-1.0
) -1.5 R 1 ( n l -2.0
Illinois Coal
No Modifier Nalco 9762: 0.5 kg/t
-2.5
-3.0
-3.5
Figure 4.23 Effect of Sodium dodecyl sulphate on the flotation rate of a West Virginia coal sample.
117
Figure 4.24 Effect of NALCO 9762 on the flotation rate of an Illinois coal sample.
118
5. CONCLUSSIONS AND RECOMMENDATIONS
5.1 Conclusions
The objective of this project was to find novel ways to improve classification performance in the ultrafine particle size range. The study involved the evaluations using both laboratory scale and in-plant tests. The laboratory tests were conducted in three phases using two coal samples and a 10-cm classifying cyclone. The effects of different apex diameters were evaluated as well as different feed solids. Tests were also conducted to evaluate the potential benefits to be achieved with rheology modification at high feed solids concentrations. The plant tests involved a two-stage classifying cyclone circuit at an Eastern Kentucky coal processing plant. Different circuitry arrangements were tested using different apex diameters. Using linear analysis, different circuit arrangements were simulated and evaluated to determine any potential classification benefits.
The objective of the laboratory studies was to evaluate the effect of apex diameter, feed solids concentration and viscosity modifier concentration on classification performance. A Box- Behnken statistical
test program, which resulted in 17 tests, was
designed and used to evaluate the various parameter effects considered. Based on the results of the statistical tests, a model was developed which described the effects and interactions of the various parameters on the particle size cutpoint (d 50), imperfection value and ultrafine by-pass. Test results showed that adding the modifiers resulted in lower separation cutsizes and improved efficiencies and lower ultrafine by-pass. These improvements were more significant at high solids concentrations and small apex diameters were high particle populations results in non-Newtonian slurry that posses a yield stress which restricted the ability of fine particles to move independently of the fluid. Using a smaller apex reductions in by-pass from about 20% to 5% were achieved Using viscosity modifiers reduced particle cutsize by about 20% from 50 to 40 µm while improving efficiency by 200%. A possible explanation is the reduction in yield stress due to the addition of the viscosity modifier. Data obtained from viscosity measurements
119
conducted on feed slurries with varying solid concentrations show that the modifiers reduce the viscosity of the slurry, thereby, decreasing the particle size cutpoint.
A second phase of laboratory tests involved efforts to reduce water recovery to the underflow stream using high solids concentrations and a small apex diameter, which resulted in roping conditions that negatively affected classification performance. The potential benefit of rheology modification under such conditions was evaluated using sodium dodecyl sulfate and the commercial modifier Nalco 9762 to alter the slurry rheology. The viscosity modifiers eliminated the roping conditions and significantly improved classification efficiency while maintaining low ultrafine by-pass values.
The aim of the in-plant classifying cyclone tests was to achieve an efficient 25 µm cutsize separation and, as a result, generate a low ash classifier product. To achieve this aim, two 15-cm gMax classifying cyclones where employed under various conditions, including the addition of dilution water to the secondary feed at different rates and the variation in the apex diameter. Recycling of the secondary overflow to the primary feed was also investigated.
The particles having a size greater than 25 microns in a flotation feed stream had ash content less than 5% and an energy value of about 14000 Btu/lb on a dry basis. As such, efficient classification to achieve a particle cutsize of around 25 microns could potentially result in a clean coal product without the use of froth flotation. The inherent problem of ultrafine particle by-pass to the underflow of classifying cyclones and the relatively large amount of ultrafine material in the flotation feed stream are significant issues that hinder the ability to achieve the objective in a single classification stage.
The 2-stage circuit provided a particle cutsize ( D50(c)) under various conditions that was consistently in the range of 37 to 40 microns while reducing the ultrafine particle by-pass to less than 10% with a low value of 6%. Classification efficiency was also improved by 40% over the single stage unit. The imperfection and alpha values were
120
typically in in the range of 0.400 and 2.75, respectively. The classification partitioning achieved on coal and mineral matter separately revealed that the performance achieved on the coal particles was exceptional. The average imperfection and alpha values were 0.281 and 3.91, respectively, which represents a very sharp particle size separation. Ultrafine by-pass was also significantly lower than the overall performance with values less than 3%.
Despite the excellent classification efficiency, a significant portion of the coal particles reported rep orted to the cyclone overflow stream. This finding is important for current coal preparation plants that use single-stage classifying cyclones to deslime flotation feed. Based on the classification performances obtained in this study, the amount of high quality coal reporting to the overflow stream could amount to 5 tph for a preparation plant having a capacity of 1000 tph. The estimated value of the lost coal is $1.25 million annually in today’s market.
The ability to achieve an acceptable product grade in the circuit coarse particle stream was hindered mostly by a density affect that resulted in a coal cutsize of 40 microns and an estimated mineral matter cutsize of 17 microns. As a result, the ash contents of the particle size fractions below 75 microns (200 mesh) increased significantly from the feed stream to the secondary cyclone underflow stream, which was the circuit coarse product. The trend was especially apparent for the -25 micron (-500 mesh) fraction for which the ash content increased from 61% to 84%. As such, true classification does occur on a portion of the -25 micron fraction rather than the common opinion that the fraction is hydraulically entrained and thus reports with the water to the c yclone output streams.
A significant amount (80% - 90%) of ash-forming material was rejected using the 2stage cyclone circuit, which resulted in ash content reductions from feed values of 40%50% to values in the secondary underflow between 20%-30%. The mass yield to the secondary underflow stream ranged from 15% to 35%. Interestingly, if the density effect was eliminated and the secondary underflow ash contents in each size fraction was equal
121
to those in the secondary underflow stream, the ash content of the circuit coarse product would be around 15%. Reducing hydraulic entrainment further further has the potential to significantly lower this value.
Using linear analysis, various circuitry arrangements were modeled and simulated. These circuits included the two-stage circuit with recycle and no-recycle conditions evaluated in the experimental program as well as the simulation of the potential benefits of adding a third stage of classification. The results of the simulations showed that multiple stage circuits provide the benefit of reduced ultrafine by-pass. An evaluation of the 2-stage circuit with recycle and no recycle models show revealed the same trends observed in experimental data. The two-stage circuits provide better efficiencies than single stage units do while providing elevated particle cutsizes. A third stage of classification under these conditions resulted in improved efficiency over both the twostage circuits and single stage units with the three-stage circuit with recycle providing the best performance. Circuit modeling of the three-stage counter cou nter flow circuitry ci rcuitry arrangement revealed that it had potential to provide the lowest cutsize among all the circuits considered.
5.2 Recommendations for Future Work
Based on a review of the previous investigations and findings of the research described in this thesis, the recommendations for future study are as follows:
Verifying the test results obtained in the rheology modification study using a 15cm classifying classifying cyclone as practiced in industry.
Exploring the potential benefits of adding viscosity modifiers to the feed to the secondary cyclone in a two-stage classifying cyclone circuit to improve classification performance.
122
Evaluate the benefits of using the three-stage counter flow circuit for classifying coal fine circuit feed.
Evaluate the potential benefits of tangential water addition via small apertures at the lower conical section of the classifying cyclone in order to reduce solids concentration at the apex therefore reducing the effect of increased cutsize due to viscosity and hindered settling as well as the density effect. The tangential addition of water could possibly reduce the effect of particle size increase experienced by other water injection systems. s ystems.
123
References
Agar, G.E., and Herbst, J.A., 1966, “The Effect of Fluid Viscosity on Cyclone Classification”, Transactions of AIME , Vol. 235, pp., 145 – 149.
Bradley, D.,”1958,”A theoretical study of the hydraulic cyclone”, Ind. Chem, Vol.34,
pp.473. th
Bradley, D., 1960,”Design and performance of Cyclone thickeners”, Proceedings 5
International Mineral Processing Congress, Institution of Mining and Metallurgy,
London, Paper 7, pp. 129-44.
Bradley, D., 1965,The Hydrocyclone, Pergamon Press, New York, USA.
Bickert,G.,Stahl,W.,Bartsch,R and Müller,F.,1996,“Grinding circuit for fine particles in liquid suspensions with a new counter-flow centrifugal classifier, International Journal of Mineral Processing, Vol. 44-45, pp.773-741.
Buisman, R., 2000 “Fine Coal Screening Using the New PANSEP Screen,” Proceedings, th
17 International Coal Preparation Conference, Lexington, Kentucky, pp.69 – 85.
Buisman, R and Kobus, R., 2000,”Fine Coal Screening using the New Pansep Screen, Proceedings”, 17th International Coal Preparation Conference, Lexington, Kentucky, pp.
71-85
Derrick, J., 1984,”Multifeed screen increases coal recovery in fine circuits”, Coal preparation conference and exhibition, Lexington, Kentucky. Erol, M., Colduroglu, C and Aktas, Z., 2003,”The effect of reagent mixtures on froth flotation of coal fines”, International Journal of Mineral Processing, Vol. 71 issues 1-4,
pp.131-145
124
Firth, B. and O’Brien, M., 2003, “Hydrocyclone Circuits,” Coal Preparation: An International Journal , Vol. 23, pp. 167 – 183.
Plitt, L.R., Finch, J.A., and Flintoff, B.C., 1980, “Modeling the Hydrocyclone Classifier”, Proceedings of the European Symposium on Particle Technology, Amsterdam, pp. 790 –
804.
Kawatra, S.K., Bakshi, A.K., and Rusesky, M.T., 1996, “Effect of Viscosity on the Cutsize of Hydrocyclone Classifiers”, Minerals Engineering , Vol. 9, No. 8, pp.881-891.
Kawatra, S.K., T.C. Eisele., Zhang, D and Rusesky,M.,1988, “Effects of Temperature on Hydrocyclone Efficiency’’, International Journal of Mineral Processing , Vol. 23, pp.205 – 211.
Scott, I.A., 1988 “A Dense Medium Cyclone Model based on the Pivot Phenomena”, Ph. D. Thesis, University of Queensland.
Laapas, H.R., 1983, “Measurement and Estimation of the Rheological Parameters of Some Mineral Slurries”, DSc Thesis, Helsinki University of Technology.
Praczkajlo, J., 1998, “Fine Coal Wet Sizing and Dewatering Using High Frequency th
Vibrating Screening Machines,” Proceedings, 15 International Coal Preparation Conference, Lexington, Kentucky, pp. 207 – 221.
Luttrell, G.H., Honaker, R.Q and Phillips, D.I, 1995,”Enhanced gravity separators: new alternatives for fine coal cleaning”,Proceedings,12th International Coal Preparation Conference, Lexington, Kentucky, pp. 281.
125
Klimpel, R. R., 1981,”The influence of selective dispersants on the sizing performance of hydrocyclones”, American Institute of Mining, Metallurgical and Petroleum Engineers meeting , Chicago, Illinois pp.81-85.
Klimpel, R. R., 1982,”Slurry Rheology Influence on the Performance of Mineral/Coal Grinding Circuits”, Mining Engineering , pp.1665-1669 .
Klimpel, R. R., 1983,”Slurry Rheology Influence on the Performance of Mineral/Coal Grinding Circuits - Part 2”, Mining Engineering , pp. 21-26.
Wills, B.A .,1988,Mineral Processing Technology, Pergamon Press, New York, USA.
Osborne,D.G.,1988, Coal Preparation Technology Vol.1,Graham and Trotman Limited, London, pp. 199-460.
Rong, R. and Napier-Mann, T. J., 2003, “Development of a More Efficient Classifying Cyclone,” Coal Preparation: An International Journal , Vol. 23. pp 149 – 165.
Kroeger, B. and Mohanty, M., 2004, “On-Site Demonstration of the Pansep Screen,”
Final Report, Illinois Clean Coal Institute, Carterville, Illinois, Project No. 03-1/8.1B-2.
Firth, B. and O’Brian, M., 2003, “Hydrocyclones Circuits,” Coal Preparation, Vol. 23,
pp. 167 – 183.
Heiskanen K, 1993, Particle Classification, Chapman & Hall, London, United Kingdom.
Heiskanen, K.G.H., 1996,“Developments in Wet Classifiers,” International Journal of Mineral Processing , Vol. 44-45, pp. 29 – 42.
126
He, Y.B., Laskowski, J.S., Klein, B., 2001,” Particle movement in non-newtonian slurries: the effect of yield stress on dense medium separation”, Chemical Engineering Science, Vol.56, pp.2991-2999. Honaker, R.Q., Wang D. and Ho,K.,1996”,Application of the Falcon Concentrator for fine coal cleaning”, Minerals Engineering Vol.9,No. 11,pp.1143-1156.
Honaker,R.Q.,Das,A.,Boaten,F.E.,Luttrell,G.H and Yoon,R.H.,2005,“Improved ultrafine classification performance“,SME Annual meeting,Saltlake City,Utah.
Kawatra, O.k. and Eisele, T.C., 1988,”Rhelogy effects in grinding circuits,”16 th International mineral Processing Congress. Elsevier, Amsterdam, pp 195-207.
Kelly, C., 2005,”Derrick Corporations latest advances in high capacity fines screening for the coal industry”, World Coal .
Kelsall, D.F., and Holmes., J.A.,1960,”Impr ovement of classification efficiency in th
hydraulic cyclones by water injection”, Proceedings 5 International Mineral Processing Congress, Institution of Mining and Metallurgy, London, pp. 209 -228.
Laskowski J. S., 2001, “Rheological Measurements in Mineral Processing Related Research”, Proc. IX Balkan Mineral Processing Congress, Istanbul.
Lilge, L.O., 1962,”Hydrocyclone fundamentals”, Transactions Institute of Mining and Metallurgy, Vol. 71,pp.C285-C337.
Lynch, A.J. and Rao, T.C.,1975, “Modeling and Scale-Up of Hydrocyclone Classifiers,” Paper No. 9, 11th International Mineral Processing Congress, Cagliari, Italy ,pp 245-269.
127
Mohanty, M., 2001, “Evaluation of new coal sizing technologies to improve plant profitability,” Final Technical Report, Southern Illinois University, Illinois, Project No. 00-1/4.1A-2.
Mohanty, M. K., Palit, A. and Dube, B., 2002,“A Comparative Evaluation of New Fine Particle Size Separation Technologies,” Minerals Engineering , Vol. 15, pp. 727 – 736.
Obeng, D. P. and Morrell, S., 2003, “The JK Three-Product Cyclone-Performance and Potential Applications,” Inter. Journal of Mineral Processing , Vol. 69, pp. 129 – 142.
Olson, T., and Omman, R. V., 2004, “Optimizing Hydrocyclone design using advanced CFD model,” Minerals Engineering , Vol. 17(5), pp. 713 – 720.
Periccleous, K.A., and Rhodes, N. and Cutting, G.W.,1984”,A mathematical model for nd predicting the flow field in a hydrocyclone classifier”,2 International Conference on
Hydrocyclones, Bath, British Hydromechanics Research Association, Cranfield, pp. B1,27-40.
Plitt L. R., 1976,” A Mathematical Modeling of the Hydrocyclone Classifier”, CIM
Bulletin 69, 776, pp. 114-123.
Plitt, L.R., Kawatra, S.K., 1979,”Estimating the cut (d50) size of classifiers without product particle- size measurement,” International Journal of Mineral Processing , Vol. 5, pp 364-378.
Rong, R. and Naper-Munn, T. J., 2003, “Development of a More Efficient Classifying Cyclone,” Coal Preparation, Vol. 23, pp. 149 – 165.
128
Salter, P., and King, E.J., 1957,”Cyclone practice in Arizona”, Minerals engineering, pp 883-889.
Salter, P., and Childs, G., 1984,”Application and operation of fine cyclone circuits”, nd
Proceedings, 2 International Conference on hydrocyclones, Bath, BHRA, pp.D4, pp.
135 – 152.
Shi, F.N., Napier-Munn, T.J., 1996,” A model of slurry rheology”, International Journal of Mineral Processing , Vol. 47, pp. 103-123.
Smith, M.R and Gochin, R., 1984”Classifiers.Part 1: An introduction to the theory and practice”, Mining magazine, pp.27-39.
Thompson, P.D and Galvin, K.P., 1996,”An empirical description for the classification in
an inclined counter-flow settler”, Minerals Engineering , Vol. 1, No.1, pp. 97-109.
Trawinski, H., 1972,”Aufstromklassierer Ullmanns ,” Ecyclopädie der techn. Chemie, 4
Aufl, pp. 70-80.
Trawinsky, H., 1972,”Zentrifugen und Hydrozyklone,” Ecyclopädie der techn. Chemie,
4 Aufl, pp. 204-224. Vesanto, A., Heiskanen,K., and Kukri,P.,1992,”A novel small diameter hydrocyclone for th fine particle separations”,17 International Mineral Processing Congress, Dresden, pp.
189-200.
Williamson, R.D. and Bott, T.R., 1984,” Small particle separation in a hydrocyclone”, Proceedings
nd
2 International
conference
on
Hydrocyclones,
Hydromechanics Research Association, Cranfield, Paper J3, pp.371-380.
129
Bath,
British
Williamson, R.D., Bott, T.R and Newson, I.H, 1984,”Small Particle separation in a Hydrocyclone”, AERE , Harwell, Paper J3.
Yoshioka, N. and Hotta,Y.,1955,”Liquid cyclone as a hydraulic classifier, ”Chemical
Engineering,Japan,Vol.19,pp.632-40(translated).
Zaman A. A., El-Shall H., and Moudgil B. M.,2000,”Rheological Evaluation and Control of Flow Behavior of Concentrated Mineral Slurries”, SME Annual Meeting, Utah.
130
LIOGRAPHY
131
132
