INTRODUCTIONAND COMPUTER SIMULATION
Process Modeling and Simulation
CMRL/TR-01-09 NRRI/TR-2001/20 Project #5600114
Coleraine Minerals Research Laboratory P O Box 188
One Gayley Avenue Coleraine, Minnesota 55722
University of Minnesota Duluth Natural Resources Research Institute
5013 Miller Trunk Highway Duluth, Minnesota 55811
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TABLE OF CONTENTS
Page No. List of Tables iv List of Figures v List of Appendices vii References viii 1. Introduction 1 2. An Assessment of USIM PAC Mineral Processing Software 3 2.1 Mass Balancing 3 2.2 Simulation 4 Figure 1 5 Figure 2 6 Table 1 7 Figure 3 7 3. Model Development 9 3.1 Liberation Modeling 9 3.2 Magnetic Separator Modeling 10 3.2.1 The Analysis of Available Plant Data 10 Cobbers 11 Figure 4 12 Figure 5 12 Table 2 13 Figure 6 13 Roughers 13 Figure 7 14 Figure 8 14 Table 3 15 Finishers 15 Table 4 16 Figure 9 16 Figure 10 16 3.2.2 Minntac Data 17 Cobbers 17 Figure 11 18 Figure 12 18 Roughers 19 Figure 13 19 Figure 14 20 Finishers 20
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Figure 15 20 Figure 16 21 Cleaners 21 Figure 17 21 Figure 18 22 General Evaluation of the Minntac Data 22 Figure 19 23 Figure 20 23 3.3 Hydroseparator Modeling 23 Table 5 24 Table 6 24 Figure 21 25 Figure 22 25 3.4 Fine Screen Modeling 26 Figure 23 26 Figure 24 27 4. Performance Analysis of Taconite Plants 28 Table 7 28 4.1 The Evtac Plant 29 4.2 The Ispat Inland Plant 29 4.3 The Minntac Plant 30 4.4 The National Steel Plant 30 5. Taconite Plant Simulation 31 Figure 25 31 Figure 26 32 Table 8 32 Figure 27 33 Figure 28 33 Table 9 34 Table 10 34 6. Conclusion 36 Appendix A – A Comparison of Mass Balancing Software: 37-40 USIM PAC, JKSIMMET and MATBAL Appendix B – A Summary and Evaluation of Schneider’s 41-54 Liberation Model Appendix C – Performance Summary for the Magnetic 55-63 Separators at the Minntac Plant Appendix D – The Evtac Plant Performance Summary 64-73 Appendix E – The Ispat Inland Plant Performance Summary 74-83 Appendix F – The Minntac Plant Performance Summary 84-95
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Appendix G – The National Steel Plant Performance Summary 96-105 Appendix H – The Ispat Inland Plant Simulation Report 106-110 Appendix I – The Minntac Plant Simulation Report 111-117
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LIST OF TABLES
Table 1. Component Recoveries Used in Modeling the Performance of a Cobber Magnetic Separator
Table 2. Cobber feed and performance data Table 3. Rougher feed and performance data Table 4. Finisher feed and performance data Table 5. Hydroseparator operating conditions Table 6. Hydroseparator performance data Table 7. Typical fine screen oversize characteristics. Table 8. Simulated performances of the current and original flow sheets of the
National Steel plant. Table 9. A comparison of the simulated performances of the current flow sheet and
flow sheet 1. Table 10. A comparison of the simulated performances of the current flow sheet
and flow sheet 2.
LIST OF FIGURES
Figure 1. Measured and model fitted size distributions of a rod mill product Figure 2. Measured and simulated size distributions of typical ball mill discharge
and hydrocyclone overflow products. Figure 3. Typical partition curves used in modeling fine screens. Figure 4. Magnetite recovery vs. particle size relationships for cobber magnetic
separators. Figure 5. Waste recovery vs. particle size relationships for cobber magnetic
separators. Figure 6. The relationship between % tail rejected and mag iron loss for cobbers. Figure 7. Magnetite recovery vs. particle size relationships for rougher magnetic
separators Figure 8. Waste recovery vs. particle size relationships for rougher magnetic
separators Figure 9. Magnetite recovery vs. particle size relationship for finisher magnetic
separators Figure 10. Waste recovery vs. particle size relationship for finisher magnetic
separators Figure 11. Magnetite recovery vs. particle size relationship for cobber magnetic
separators at Lines 8 and 11 Figure 12. Waste recovery vs. particle size relationship for cobber magnetic
separators at Lines 8 and 11 Figure 13. Magnetite recovery vs. particle size relationship for rougher magnetic
separators at Lines 8 and 11 Figure 14. Waste Recovery vs. particle size relationship for rougher magnetic
separators at Lines 8 and 11 Figure 15. Magnetite recovery vs. particle size relationship for finisher magnetic
separators at Lines 8 and 11 Figure 16. Waste recovery vs. particle size relationship for finisher magnetic
separators at Lines 8 and 11 Figure 17. Magnetite recovery vs. particle size relationship for cleaner magnetic
separators at Lines 8 and 11 Figure 18. Waste recovery vs. particle size relationship for cleaner magnetic
separators at Lines 8 and 11 Figure 19. Actual waste recoveries vs. Davis tube waste recoveries obtained from
feed samples of each drum Figure 20. The relationship between fine (-500 mesh) waste and water recoveries.
The data includes all magnetic separation stages and drums. Figure 21. Partition coefficients of hydroseparators.
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LIST OF FIGURES (cont’d)
Figure 22. Waste recovery into concentrate vs. particle size relationship for hydroseparators.
Figure 23. Partition coefficients of fine screens Figure 24. Fine screen partition coefficients for magnetite and waste (Inland data) Figure 26. The original flow sheet of the National Steel plant. Figure 27. Simulated flow sheet 1: The secondary screen oversize is fed to the
rougher magnetic separator. Figure 28. Simulated flow sheet 2: The secondary screen oversize is fed to the
hydrocyclone, cyclone undersize to the ball mill and oversize to the primary screens.
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LIST OF APPENDICES
Appendix A: A comparison of Mass Balancing Software: Usim Pac, JKSimmet and Matbal.
Appendix B: A Brief Summary and Evaluation of Schneider’s Liberation Model Appendix C: Performance Summary for the Magnetic Separators at the Minntac
Plant Appendix D: The Evtac Plant Performance Summary Appendix E: The Ispat Inland Plant Performance Summary Appendix F: The Minntac Plant Performance Summary Appendix G: The National Steel Plant Performance Summary Appendix H: The Ispat Inland Plant Simulation Report Appendix I: The Minntac Plant Simulation Report
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REFERENCES
1. Simulation of Magnetic Taconite Concentration Processes, by R. L. Wiegel, Ph.D. Thesis, Department of Mining and Metallurgical Engineering, University of Queensland, Brisbane, Australia, 1976.
2. National Steel Pellet Company’s Secondary Grinding Circuit Modifications, by J. E. Wennen, W. J. Nordstrom, and D. L. Murr, in Comminution Practices, ed. S. K. Kawatra, p19-25, SME, 1995.
3. Measurement and Calculation of Liberation in Continuous Milling Circuits, by C. L. Schneider, Ph.D. thesis, Metallurgical Engineering Department, University of Utah, 1995.
4. Development of an Approach to the Simulation of Size Reduction/Mineral Liberation for Magnetic Taconite Ore in Tumbling Mills and Its Implementation in a BASIC Computer Program, University of Minnesota- Duluth, Coleraine Minerals research Laboratory Technical Report, #CMRL/TR-0016, by R. L. Wiegel, 2000.
5. Process Optimization: Small Steps to Large Gains, by J. Maki, presented at the 74th Annual Meeting, Minnesota Section, SME, April 10-12, 2001.
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1. INTRODUCTION
In 1997, under the auspices of the Iron Ore Cooperative Research Program, iron ore mining companies operating on the Iron Range decided to work as a consortium in establishing expertise in the development of math models of individual taconite concentration operations and their use to simulate portions of the integrated concentration process. This led to the establishment in 1998 of the Concentrator Modeling Center within the Coleraine Minerals Research Laboratory (CMRL) of the University of Minnesota - Duluth. Following discussions on the type of software to be used by the Center, Usim Pac, mineral processing software developed by BRGM of France was selected due to the availability of a larger number of models, and model incorporation capability to add those to be developed in the future. The Center became fully operational when Dr. Salih Ersayin started to work as the program director on Nov. 1, 1999.
While the application of modeling and simulation has provided significant benefits in the processing of base metal ores, its application to the processing of magnetic taconite has been hindered. This was caused by the need to incorporate the modeling of mineral liberation into the comminution models for size reduction steps, which occur between several stages of magnetic separation. An initial effort at integrating the modeling of size reduction, mineral liberation was carried out by Wiegel1 for the Erie Mining Company process in 1976. Plant scale implementation of the combined use process modeling and plant testing was reported for the National Steel Company secondary grinding section modifications 2 . Recently, Schneider 3 developed a mineral liberation model based on liberation characterization by scanning electron microscopy measurements. He validated his model using plant data obtained from the Fairlane Plant of Eveleth Taconite. For simulation purposes, he integrated his liberation approach into a ball mill grinding model. He also presented magnetic separator and hydrocyclone model structures compatible with the type of data produced by the liberation model.
Despite of these developments, there still was a need to develop a simplified approach to the integrated size reduction/liberation model for taconite processing, models for magnetic separators, hydroseparators and fine screens, which would take into account the significant operating and design parameters. Therefore, the initial efforts of the Center were concentrated on development of simplified integrated mineral liberation/size reduction and magnetic separator models using funds allocated by the Permanent University Trust Fund (PUF), while providing a simulation service to taconite plant operators with the available software. Data from four plants in the Range taken as a part of an earlier Iron Ore Coop project was analyzed using the existing capabilities of the software. The data was first mass balanced and performance of individual pieces of equipment was examined. Results were presented to the plant engineers; their implications and potential improvements were discussed. To illustrate the capabilities of the software and potential benefits from the use of simulation, some modifications in plant flow sheets and operating/design conditions were simulated using the same data as a basis. These simulation results were also presented to the relevant engineers.
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Along with the benefits to the plant operators, this work provided an insight for the Center into better understanding of the separation processes at taconite plants, and a database for model development efforts. Extensive experience with the simulator highlighted its pros and cons. Need for development of improved models became more obvious.
In this report, an assessment of Usim Pac mineral processing software together with the results of the work undertaken by the Center are presented. The data from the PUF projects are kept brief, since their details are reported separately by the CMRL.
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2. AN ASSESSMENT OF USIM PAC MINERAL PROCESSING SOFTWARE
Usim Pac has two main functions: Mass balancing and simulation. Models form the infrastructure of simulation. An assessment of mass balancing and simulation capabilities, together with models available in the software are presented below.
2.1 Mass Balancing
A large number and variety of mass balancing calculations were carried out using the mass balancing capability of Usim Pac. The assessment is based on these and past experiences with other software, and a comparative study of mass balancing the same data set using three different mass balancing program, namely Usim Pac, JKSimmet and Wiegel`s MATBAL.
Advantages of Usim Pac: 1. User-friendly flow sheet drawing and data entry. 2. It allows a large variety of data to be entered, e.g. head grades, size distributions,
size by size chemical assays, water flow rates, etc. 3. It can handle very large data sets and complex circuits.
Disadvantages of Usim Pac: 1. Complex file system and difficult house keeping. Every step of a particular work
is filed separately. Unnecessary files cannot be deleted directly from the software’s user interface. When a file is copied to a floppy disk, it may be deleted from its current location.
2. The streams are numbered, rather than using a descriptive name for each particular stream. This creates a minor difficulty when stream data is viewed.
3. It does not use % solids data directly in mass balance calculations. Although it balances water flow rates, this does not have any direct bearing on solids flow rates.
4. It creates problems when using certain data combinations, e.g. size distribution and head grades combination. Although it appears that the software is capable of handling this type of data, it gives an error message when such data is mass balanced.
5. It creates problems when a size fraction present in a feed stream does not appear in any of the product streams or vice versa. It is up to the user to find out if this is the cause of the problem when mass balancing does not converge.
6. There appears to be a limit to the data sets/circuit complexity that can be handled by the software. The vendor claims that the computer used in calculations imposes such limits.
7. It requires initial estimates of flow rates. This necessitates node by node estimation of flow rates before mass balancing of a complex circuit is attempted.
8. Mass balanced flow rates are too sensitive to the initial flow rates and their estimated accuracies. This requires large accuracy definitions and repeated mass
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balancing calculations, each time using calculated flow rates as new estimates, until the difference between them becomes negligible.
9. Since users are not warned about the above problems, the efficient use of mass balancing requires expert knowledge and/or a large variety of experience with the software.
The details of a mass balancing study carried out to compare the capabilities of three mass balancing programs are presented in Appendix A. In the study, size distribution data from a complex grinding circuit was used for estimating flow rates in each stream based on a measured feed flow rate of 120 t/h. The findings of this study are summarized below:
1. Both the Usim Pac and JKSimmet are user friendly and able to handle large data sets, but Matbal is not.
2. Mass balanced flow rates calculated by the three were close to each other. The differences between the calculated flow rates were attributed to differences in defining the data accuracy in each of the programs. It was possible to lower the differences by manipulating the data accuracies.
3. The most significant difference separating the commercial mass balancing programs, i.e. Usim Pac and JKSimmet, from Matbal was the fact that Matbal had a tendency to accumulate errors in the screen undersize fraction when stream data contained large errors, since this size fraction is not considered in the calculations. On the same basis, JKSimmet provided a slightly better fit than Usim Pac.
4. JKSimmet and Matbal did not require initial flow rates, but Usim Pac did, and several iterative mass balance calculations were needed, until the difference between the initial estimates and calculated flow rates became very small.
2.2 Simulation
As noted above, the major hurdle in obtaining a realistic simulation of taconite plants is the lack of an integrated size reduction/liberation model. Usim Pac is not an exception. However, irrespective of the availability of a liberation model, complete simulation of a taconite plant requires that mathematical models of each piece of equipment used in taconite processing must exist in the software. Fortunately, Usim Pac has all the models needed. The accuracy of simulation, however, depends on how realistic the models are.
Common pieces of equipment used in taconite processing are rod mills, ball mills, hydrocyclones, magnetic separators, hydroseparators and fine screens. An assessment of the models available in Usim Pac for these pieces of equipment is given below. Although flotation is used as a last stage of concentration in some plants and there are flotation models available in the software, this subject is not included in the following discussions.
Despite of the fact that rod mills are known to have selective grinding properties for the coarse size fractions as a feature, which is different from the random grinding behavior of ball mills, the same model structures are offered by Usim Pac to define both types of grinding. Of the five different ones available, the highest-level model is based on a
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combined kinetic and energetic approach, which does not take the effects of % solids and rod/ball size into account. Apart from this deficiency, its adequacy for rod milling is questionable, since the selective grinding of coarse fractions is not incorporated into the model structure. As expected, fitting plant data to this model created problems. The model fitting produced a size distribution containing higher amounts of coarse and lower amounts of fine particles, as compared to the plant data. A typical example of this is illustrated in Figure 1. Simulation of flow rate changes creates similarly shaped size distribution and its accuracy is largely questionably. Since this is a first step of integrated grinding and concentration operations, such differences in size distributions cause unacceptably large deviations in flow rates and component grades in the downstream flows.
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Figure 1. Measured and model fitted size distributions of a rod mill product.
Although the same model structure is suitable for ball mill grinding, another deficiency of the model became apparent when the model was fitted to a number of plant data. The model did not produce a smooth size distribution curve for ball mill products. This feature of the model, which was also discernible in rod mill product size distribution, was amplified when closed circuit ball mill grinding was simulated. When non-smooth ball mill discharge size distribution was coupled with a hydrocyclone model, the size distributions of hydrocyclone overflow became extremely non-smooth. It also resulted in large differences between measured and simulated size distributions and very high simulated circulating loads, despite the fact that the model fit function provided satisfactory fit to the plant data for each piece of equipment. Although the Objective Driven Simulation (ODS) option of the software, which provides model fit by
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considering both pieces of equipment parameters simultaneously, offered partial relief to some of the problems, it was up to the operator to find a more satisfactory fit by arbitrarily changing some of the model parameters. This eventually provided a satisfactory fit to mass balanced flow rates and size distributions, but it did not solve the problem completely. A typical example of size distributions of ball mill discharge and hydrocyclone overflow streams obtained by this type of work is presented in Figure 2.
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Ball Mill Disc. (Measured) Ball Mill Disc. (Simulated) Cyclone O/F (Measured) Cyclone O/F (Simulated)
Figure 2. Measured and simulated size distributions of typical ball mill discharge and hydrocyclone overflow products.
The highest level of model offered for hydrocyclone modeling is known as Plitt’s model, which has the capability of simulating the effects of geometrical and operating variables. It provides a good fit to plant data even when the feed is characterized by several components of different densities.
The same model structure is provided for modeling both magnetic separators and hydroseparators. It assumes that recoveries of each component from a given size fraction and device are constant. Therefore measured size by size recoveries of each component form the model parameters (Table 1). It may be classified as primitive, since it does not take into account the effects of any operating conditions and changes in liberation characteristics. Despite its simplicity, it produces reasonable simulation results when simulated circuit modifications do not alter the liberation characteristics of their feed streams. Otherwise, a subjective modification of size by size recoveries is needed to account for such changes. No quantitative information is available to estimate the effects of changes in operating conditions.
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Table 1. Component Recoveries Used in Modeling the Performance of a Cobber Magnetic Separator Size Fraction (mesh) Magnetite Recovery (%) Gangue Recovery (%)
-3+ 6 95.9 66.3 -6+10 95.8 64.9
-10+20 94.9 46.5 -20+35 95.1 43.1 -35+65 95.8 36.6 -65+100 95.7 34.6
-100+200 96.0 21.4 -200+270 96.2 20.2 -270+325 96.4 14.5 -325+400 95.6 10.4 -400+500 94.0 9.6
-500 93.5 6.9
The performance of fine screens can be defined very well by a partition curve model available in the software (Figure 3). This forms a good base for modeling, but it does not provide capability to simulate the effects of operating conditions, as is. The relationship between partition curve parameters and operating conditions is needed for its more efficient use.
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Figure 3. Typical partition curves used in modeling fine screens.
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As briefly noted above, some problems were encountered while using model fitting and ODS functions of the software. Occasionally, model-fitted size distributions showed very large deviations from plant data when model parameters for a mill were calculated. Although it is not exactly known why it happens, a plausible explanation is that model fitting works in such a way that the accuracy of the component grade of each size fraction has higher weight than size distribution in determining the best-fit model parameters. Eventually, the outcome is high accuracy size by size component grades with poor accuracy size distribution. Users do not have control over which criteria should have higher accuracy when model fit function is used.
Despite the deficiencies listed above, the software provides a user-friendly interface with a data structure capable of handling large data sets needed for taconite plant simulation. It also produces reasonable simulation results when moderate changes are made in operating conditions and/or the plant flow sheet. It can be used reliably in the search to improve performance of a given circuit. The accuracy of its predictions is yet to be validated. Within a very short term, it requires better grinding models. Preliminary work indicated that incorporation of grinding models developed by JKMRC could overcome all the grinding simulation deficiencies noted above. The Center is planning to accomplish this objective as a first step toward model incorporation into the software. The next and biggest step will be the addition of a liberation model. Final touches toward a perfect simulator for taconite plants require the development of improved magnetic separator, hydroseparator and fine screen models. With regard to these objectives for the Center, the work completed so far is summarized in the following section.
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3. MODEL DEVELOPMENT
A search carried out immediately following the establishment of the Center indicated that the available software packages for mineral process simulation were lacking two important items, which were essentials for taconite plant simulation. These were (1) a suitable technique for handling the description of mineral liberation as a function of changes resulting from size reduction steps in the process, and (2) a magnetic separator model capable of predicting the performance as a function of design and operating conditions. Therefore, initial efforts of the Center were concentrated on these two items. A realistic simulation of a magnetic concentration circuit also required improved models for hydroseparators and fine screens.
With regard to model development, the progress made so far is presented below.
3.1. Liberation Modeling
Modeling of mineral processing operations reached a mature stage by the early 90’s. Commercial software packages became widely available and found application in a variety of mineral processing plants. However, there remained an obstacle limiting the accuracy of simulations in concentration circuits, i.e. the lack of liberation modeling. During the 90’s, various mineral processing research centers around the world devoted considerable effort in deriving liberation data and developing models. Although some progress was made, the models were complex, the derivation of model parameters required expensive electron microscopy measurements and, despite their complexity, their accuracy was questionable even for a binary type ore structure. One of the pertinent achievements in this field was Schneider’s liberation model 3 . This work had significance for taconite process simulation, since the model validation involved a set of data from one of the plants in the Iron Range.
Although it was presented as one of the significant modeling achievements in recent years, the model had all the deficiencies listed above. A detailed description of the model and its assessment is presented in Appendix B. Awareness of these deficiencies, and the presence of Ronald Wiegel at the CMRL, who had done some of the pioneering work in the field of mineral liberation, stimulated work toward the development of a simplified and easily applicable approach. He eventually developed an integrated size reduction/mineral liberation model written in BASIC computer language using joint funding provided by the Iron Ore Coop and Permanent University Trust Fund 4 .
The model was developed primarily for taconite processing. Its liberation parameters, consisting of volumetric abundance, grain size and barren rock dilution, are derived from size by size Davis tube test data, which is a much simpler and more expensive procedure as compared to elaborate techniques involving linear electron microscopy measurements. It assumes 12 volumetric liberation classes for each size fraction, which is a common feature of all liberation models. Further assumptions concern how progeny particles will
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be distributed among the daughter fragments and liberation classes. The model uses mathematically derived “directional coefficients” to follow gradual transfer of material from locked assemblages of dissimilar mineral grains of ore composition to other locked particle compositions and eventually to liberated particles of magnetite and waste as particle size is reduced. The details of this work are presented elsewhere 4 . Wiegel is now translating this program into an appropriate FORTRAN program for use with the Usim Pac mineral processing software. It will then be used in concert with other models to simulate the entire taconite concentration process.
3.2 Magnetic Separator Modeling
Research was then directed toward development of an improved magnetic separator model and included the analysis of available plant data, and performance analysis of the magnetic separators operating at two different lines of the USX Minntac plant.
In 1997, four plants on the Iron Ore Range were sampled as part of an Iron Ore Coop project. Raw data including size distributions and size by size chemical analysis was available when the Center was established. Data from each plant was first mass balanced using the software. The size by size performance of each piece of equipment was examined. This allowed an insight for the Center to have better understanding of the processes involved in taconite processing, their strengths and weaknesses. The data was later used as a basis for preliminary simulation studies to investigate possible improvements in plant performances and illustrate the capabilities of the software. The data was also analyzed to study the type of relationships existing between particle size and recoveries of magnetite and waste. Such information would be useful in constructing a mathematical structure describing the relationship. Additional information on magnetic separator design and operating conditions could be used to devise a mathematical model describing their effects upon the performance upon the performance defined as particle size recovery relationship.
A second set of detailed magnetic separator data was obtained through a project funded by PUF. Magnetic separators in two parallel lines at the Minntac plant were sampled on a drum by drum basis. The samples went through size analysis and size by size Davis tube tests. Davis tube products were assayed for total and Satmagan iron. Such data provided both liberation information and a basis for performance analysis, as they represented an ideal case similar to the heavy liquid test data for gravity separation. Since two circuits were operating at different flow rates and there were some differences in terms of operating conditions, it was expected that the data could eventually lead to valuable information regarding how the operating parameters affect their performances.
3.2.1 The Analysis of Available Plant Data
The four plants that were sampled as a part of the IOC research project in 1997 were Evtac, Inland, Minntac and National Steel. Sampling surveys included all the streams
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from primary grinding to magnetic concentrate. Only at Evtac, three tailing streams from the hydroseparator, dewatering drum separator and finisher magnetic separators were not sampled. For these streams, estimated size distributions and component values were used as initial values for mass balance calculations. At the Minntac plant, there are four stages of magnetic separation as compared to three at the others. The third stage is called finishers and fourth stage cleaners. Data from the cleaners is not used for comparison. Performance of the third stage of separation is compared to the finisher magnetic separator data from the others.
Although all the samples and their size fractions were assayed for total iron, Satmagan iron and silica, magnetic separator performances are presented in terms of magnetite and waste recovery. Following mass balancing using all the available assay and size distribution data, mass balanced Satmagan iron assays were converted to magnetite on the basis of atomic weights by dividing them by 0.7236. The rest was considered as waste. The performances of magnetic separators are evaluated in terms of particle size vs. recovery relationship separately for each stage and presented below. It should be noted that the data presented here represent the conditions and performances prevailing when the samples were taken in 1997. Since then, changes in flow sheets, mineralogy, and separator design/operating conditions have taken place in the plants. As a matter of fact, the recent data from some of the plants shows that such changes affected performance considerably.
Cobbers
The recovery vs. particle size relationships for magnetite and waste are presented in Figures 4 and 5 respectively. Magnetite recovery decreases at both very coarse and fine particles. Apart from the National Steel plant, this particle size dependency does not seem to be strong indicating that fluctuations in the primary grinding mill performance would not have significant effect on the magnetic iron losses. Mag iron recoveries of cobbers at National Steel were unusual because losses were higher at the fine end, implying the finer the feed the higher the losses. As expected, waste recovery shows strong dependence on particle size, steadily increasing as particles become coarser. This shows that the fineness of grind will have a significant effect on the amount of waste separated at this stage, although mag iron recovery is expected to stay almost constant.
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Figure 4. Magnetite recovery vs. particle size relationships for cobber magnetic separators.
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Figure 5. Waste recovery vs. particle size relationships for cobber magnetic separators.
However, when the cobber performances of the plants are compared, it appears that waste recoveries follow the same trend as mag iron recoveries. Plants with higher mag iron recoveries have higher waste recoveries. This could be an indication that variations are mostly due to the design and operating differences of magnetic separators rather than mineralogical. For example, higher magnetic field strength would attract some poorly liberated low grade magnetite particles thereby increasing recovery in the expense of decreased grade/higher waste recovery into concentrate irrespective of particle size.
Overall performance of cobbers at the four plants is summarized in Table 2. The data shows that each plant has a different strategy as far as the fineness of primary grinding is
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concerned, hence the amount of waste to be separated at this first stage of separation. This strategy could partially depend on their feed grade. The fineness of grind expressed as 80% passing size ranged from 590 to 2640 micron. As expected, cobber losses were well correlated with the amount of waste discarded at this stage (Figure 6).
Table 2. Cobber feed and performance data Feed Tails Plant
% Mag Fe P80 (μm) Flow Rate (%) Mag Fe loss (%) Concentrate % Mag Fe
Evtac 21.8 1980 35.4 3.3 32.5 Inland 27.6 1890 27.9 1.9 37.5 Minntac 20.0 2640 42.5 4.7 33.0 NSPC 20.1 590 51.9 7.6 38.5
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Mag Iron Loss (%)
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Figure 6. The relationship between % tail rejected and mag iron loss for cobbers.
From the point of mathematical structure needed for modeling, waste recovery vs. particle size relationship curves have identical shapes parallel to each other. The difference between them could be correlated mostly to the operating and design parameters, and partially to mineralogical differences. The shape of magnetite recovery curves, however, is more complex and requires detailed study for understanding the variations.
Roughers
Although all the plants have rougher magnetic separators, the position of roughers in their flow sheets is different from one plant to another. Evtac and Inland had their roughers immediately following their secondary ball mills within the closed grinding circuits. They were used to process the primary cyclone overflow at the Minntac plant, while National Steel used them to process primary screen oversize. These differences most likely created a large variation in feed material characteristics. This is somewhat reflected in mineral
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recovery vs. particle size relationships (Figure 7 and 8). Therefore, some of the differences may be partially attributed to variations in feed characteristics, particularly to liberation properties. It is also known that low magnetite recoveries at the Evtac plant were due to design and maintenance problems. Their magnetic separators were later replaced by more efficient magnetic separators.
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Figure 7. Magnetite recovery vs. particle size relationships for rougher magnetic separators
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Figure 8. Waste recovery vs. particle size relationships for rougher magnetic separators
In general, magnetite recoveries had a peak for medium sized particles decreasing toward both fine and coarse ends. Despite the differences in feed material characteristics, the shapes of waste recovery curves were similar to each other and resembled those of cobbers. It appears that the separation of waste is less efficient when the roughers are
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operated within a closed circuit, due most probably to the accumulation of unliberated particles in these streams. Despite this deficiency, these plants separate most of their waste material in the roughing stage by circulating large amounts of material through the separators (Table 3). However, this results in large magnetite losses when the efficiency of separators is low, as was the case for Evtac.
Table 3. Rougher feed and performance data Feed Tails∗Plant
% Mag Fe P80 (μm) Flow Rate (%) Mag Fe loss (%) Concentrate % Mag Fe
Evtac 42.3 205 33.7 7.8 50.2 Inland 49.6 220 30.8 3.7 53.2 Minntac 33.0 250 16.6 1.2 45.6 NSPC 33.3 425 1.2 0.1 38.6 ∗ Relative to plant feed rate
From the point of view of mathematical modeling, magnetite recovery vs. particle size relationships did not exhibit similar trends; whereas the shape of relationship for waste was typical for all the plants.
Finishers
The position of finishers within flow sheets was also different form one plant to another. Minntac and National Steel had similar arrangements, having their finishers between hydroseparators and fine screens. The Evtac flow sheet had a small modification with feed to finishers being the primary fine screen undersize processing hydroseparator concentrate. At the Inland plant, finishers were the final step of processing and had fine screen undersize as their feed. The position of the finishers had a large influence on the feed size distribution and grade (Table 4). As the feed grade becomes higher, it is expected that the remaining waste minerals would be mostly locked and therefore attracted by magnetic forces. It is believed that the variations among the mineral recovery vs. particle size relationships (Figure 9 and 10) are a result of this phenomenon. The existence of locked waste particles with small proportion of magnetite at coarse sizes and their separation at this stage resulted in relatively lower magnetite recoveries for coarse particles where finisher feed grade was low. Similarly, where these types of particles were separated at earlier stages of separation in a flow sheet, the remaining locked waste particles had a larger proportion of magnetite, which made more difficult to separate. The net outcome was higher waste and magnetite recovery into the concentrate. The unusual shape of waste recovery vs. particle size relationship for the Minntac data is believed to be due to sampling/sample analysis errors, since this type of behavior was not observed on the more recent data obtained from the same separators.
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Table 4. Finisher feed and performance data Feed Tails∗Plant
% Mag Fe P80 (μm) Flow Rate (%) Mag Fe loss (%) Concentrate % Mag Fe
Evtac 63.4 48 1.6 0.3 66.1 Inland 65.8 47 0.5 0.1 66.6 Minntac 55.5 66 5.9 0.4 62.3 NSPC 53.6 89 8.5 0.3 59.9 ∗ Relative to plant feed rate
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Figure 9. Magnetite recovery vs. particle size relationship for finisher magnetic separators
0 10 20 30 40 50 60 70 80 90
100
W as
te R
ec ov
er y
Evtac Inland Minntac NSPC
Figure 10. Waste recovery vs. particle size relationship for finisher magnetic separators
17
3.2.2 Minntac Data
As noted above, a PUF project was carried out to collect detailed data from magnetic separators at the Minntac plant. The sampling survey involved magnetic separators in two parallel lines (Line 8 and 11). The feed and product of each drum were sampled separately. Following sieve analysis of the samples, size fractions were subjected to Davis tube tests. The test products were assayed for total and mag (Satmagan) iron. Each separator was individually mass balanced. Mass balanced mag iron values were converted to magnetite and the rest was assumed as waste, Then the data was used for performance evaluation. Details of this work will be presented in a separate PUF Project report. A brief summary is presented here.
As known, the Minntac plant flow sheet has 4 stages of magnetic separation. The performance of magnetic separators is presented below, separately for each stage. Feed characteristics including flow rates, % solids, feed grade, and rod mill discharge size distribution, were slightly different for the two lines, but the design parameters were identical. Differences in feed characteristics provided an opportunity to investigate their effects upon performance.
Feed characteristics and performance of each drum and separation stage are summarized in Appendix C both in the form of magnetite and waste, and magnetics (Davis tube concentrate) and non-magnetics (Davis tube tails) recoveries. Overall performance of each separation stage is presented as particle size vs. recovery relationship below. The implication of these results in terms of modeling is discussed briefly.
Cobbers
The overall mineral recoveries vs. particle size relationships for cobbers are presented in Figures 11and 12. Similar to the earlier data, magnetite recoveries had a plateau extending from very fine to very coarse particles. Within this large range, magnetite recoveries appear to be independent of particle size. Three fine size fractions from Line 8 data showed considerable deviation from this trend. Examination of data indicated that such deviations are likely due to sampling and sample analysis errors rather than being related to ore characteristics or operating conditions. Apart from these, both lines provided almost exactly the same magnetite recoveries, despite the differences in operating conditions, implying that operating conditions had a negligible effect on magnetite recoveries.
18
86
88
90
92
94
96
98
100
Line 8 Line 11
Figure 11. Magnetite recovery vs. particle size relationship for cobber magnetic separators at Lines 8 and 11
0
20
40
60
80
100
Line 8 Line 11
Figure 12. Waste recovery vs. particle size relationship for cobber magnetic separators at Lines 8 and 11
Waste recoveries had a typically shaped relationship increasing steadily as the particle size became coarser and decreasing slightly for the coarsest fraction. Waste recovery differences between the two lines toward the fine end are in line with the variations in head grade and feed % solid, which affects water recovery to the concentrate. While lower head grade would produce a higher proportion of liberated gangue at these size fractions, hence higher waste recovery, more dilute feed would provide more efficient separation of particularly fine waste. The flattening out of the curve at the very fine range suggests that this mechanism could be the dominant cause. In terms of modeling, such an affect could be defined by a by-pass factor, which would be a function of water recovery into concentrate or feed % solids.
Nonetheless, both minerals exhibit typical curves, which can be fitted to mathematical equations. The parameters of such equations would then be defined as a function of ore
19
characteristics/operating conditions. Considering the earlier data from other plants, it may be suggested that each plant has a different relationship, depending on ore and separator characteristics.
Roughers
Recovery vs. particle size data from roughers is presented in Figures 13 and 14 for magnetite and waste respectively. Data from both lines exhibited similar relationships. Magnetite had a maximum recovery region for medium sized particles decreasing sharply toward the coarse end and then increasing for the coarsest fraction. Magnetite recoveries from Line 8 were slightly higher for every size fraction, resulting in a parallel relationship. Examination of available data showed that the most significant difference between the lines was water recoveries. Since the operating conditions including water recoveries did not have any significant effect on magnetite recoveries at cobbing stage, it is suspected that there might be slight differences in design parameters of these two lines of separators, e.g. lower magnetic field strength. However, it is possible that water recovery might have a pronounced effect on magnetite recoveries as feed becomes finer. Higher differences for the fines could be indicative of this type of effect.
95
96
97
98
99
100
Figure 13. Magnetite recovery vs. particle size relationship for rougher magnetic separators at Lines 8 and 11
20
0
20
40
60
80
100
Line 8 Line 11
Figure 14. Waste Recovery vs. particle size relationship for rougher magnetic separators at Lines 8 and 11
The shape of the waste recovery relationship was similar to that of the cobbers. In line with water recoveries, waste recoveries from Line 8 were slightly higher toward the fine sizes. This is again interpreted as combined head grade and by-pass effect.
Finishers
Although both lines were identical in terms of magnetite recovery vs. particle size relationship, waste recoveries from Line 8 were higher for most of the size fractions (Figure 15 and 16). This was likely due to the lower feed grade in this line, hence higher proportion of separable waste in the feed. As a matter of fact, Davis tube test data indicated that Line 8 finisher feed had a 5% higher proportion of recoverable waste than Line 11. This was coupled with higher water recovery, which is also a potential factor to increase the waste recovery into concentrate.
95
96
97
98
99
100
Figure 15. Magnetite recovery vs. particle size relationship for finisher magnetic separators at Lines 8 and 11
21
0
20
40
60
80
100
Line 8 Line 11
Figure 16. Waste recovery vs. particle size relationship for finisher magnetic separators at Lines 8 and 11
Cleaners
A very small proportion of the ore is separated into cleaners as waste with almost 100% magnetite recovery (Figures 17 and 18). This was probably due to higher magnetic field intensity at this stage, which resulted in the separation of a very small proportion of waste. The most significant difference in mineral recoveries was relatively higher waste recovery for the finest fraction from Line 11. This line had higher feed % solids, which probably prevented some of the waste from going into the tailings stream. Despite the fact that coarse fractions had relatively higher magnetite losses, this did not provide much benefit for the separation of waste at this size range.
99
99.2
99.4
99.6
99.8
100
Figure 17. Magnetite recovery vs. particle size relationship for cleaner magnetic separators at Lines 8 and 11
22
0
20
40
60
80
100
Line 8 Line 11
Figure 18. Waste recovery vs. particle size relationship for cleaner magnetic separators at Lines 8 and 11
General Evaluation of the Minntac Data
1. Each stage of magnetic separators has typical mineral recovery vs. particle size relationships. These relationships can be fitted into a mathematical function, which can serve a separator model. However, available data from other plants suggests that the shape of the relationship could depend on ore characteristics and design parameters of the separators.
2. Magnetite recovery is relatively insensitive to the changes in operating conditions. 3. Waste recoveries appear to be primarily a function of liberation characteristics of
feed. As the waste in feed increases, hence the proportion of liberated waste, waste recoveries from individual size fractions into concentrate decrease. This relationship may be used to define liberation effect in the absence of a proper liberation model.
4. Size by size Davis tube test data can be used to calculate how much waste can be separated from a given magnetic separator (Figure 19).
23
0
20
40
60
80
100
Recovery (DT)
R ec
ov er
y (A
ct ua
1st Drum 2nd Drum
Figure 19. Actual waste recoveries vs. Davis tube waste recoveries obtained from feed samples of each drum
5. Of the operating parameters recorded, feed dilution (% solids) and water recovery seem to have more significant effect upon the separation of fine waste (Figure 20).
0 10 20 30 40 50 60 70 80 90
100
Water Recovery (%)
W as
te R
ec ov
er y
(% )
Figure 20. The relationship between fine (-500 mesh) waste and water recoveries. The data includes all magnetic separation stages and drums.
3.3 Hydroseparator Modeling
Limited efforts were directed to the development of a model for hydroseparators. These included the analysis of available plant data and pilot scale hydroseparator test data performed primarily for a different purpose. Nevertheless, the present data from this simple device provides valuable information showing how it operates.
24
Hydroseparator operating conditions at a number of plants are presented in Table 5. Data from the Evtac and Inland plants corresponds to the conditions when plant sampling was carried out. For the calculation of upward velocities, mass balanced flow rates were used together with % solids in each stream around hydroseparators. Such data was not available for Minntac. Therefore, design values were used for calculating the upward velocity for this plant. Its actual value during the sampling period might be different.
Table 5. Hydroseparator operating conditions % Solids Plant Feed Mag Fe
(%) Feed U’flow O’flow Upward
velocity (m/h) Evtac 54.9 25 38 1.1 9.2 Inland 58.6 23 55 0.6 8.0 Minntac∗ 49.0 22 72 3.4 11.8 NSPC 48.1 n/a n/a n/a n/a ∗Design
Available plant operating data show large variation in terms of underflow % solids. This has an effect on upward velocity and eventually on the cut size of the separator. Overall performance of hydroseparators is summarized in Table 6. Performance criteria correlate well with the upward velocities. The data shows that relatively small changes in upward velocities could have large effects on the performance. The amount of tails separated by hydroseparators varied between 1.9-9.3% of feed to the plants. Some of the variations were due to differences in feed grades to hydroseparators. However, the existence of a large gap between the two plants having similar feed grade can be interpreted as an indication of inefficient use of these devices in some plants. It appears that too high upward velocities could result in high mag iron losses, whereas too low upward velocities lower the efficiency of waste separation. It should be noted that data from the Minntac plant corresponds to an unusual period. A recent set of data indicated much lower mag iron losses with only slight reduction in % tails/overflow.
Table 6. Hydroseparator performance data Plant % Tail∗ % O’Flow Concentrate
Mag Fe Mag Fe Loss
(%)∗ Evtac 2.0 3.6 56.9 0.1 Inland 1.9 1.7 59.6 0.05 Minntac 7.7 12.6 55.5 1.4 NSPC 9.2 10.5 53.6 0.6 ∗Relative to plant feed
Hydroseparator performance was also examined on the basis of a size by size mass/waste recovery relationship, since this is the type of data that is used for modeling. The data presented in Figures 21 and 22. As expected, they exhibited a shape similar to partition curves of classifiers. Available functions could be fitted to these curves to form the
25
mathematical structure for modeling. Then, a relationship between operating variables such as upward velocity, feed % solids, water recovery in underflow, etc., and model parameters would need to be established. Although almost all magnetite particles are recovered in the underflow, prediction of mag iron losses requires information on liberation characteristics, since most of the mag iron losses are likely due to poor liberation.
75
80
85
90
95
100
0
20
40
60
80
100
Evtac
Inland
Minntac
NSPC
Figure 22. Waste recovery into concentrate vs. particle size relationship for hydroseparators.
As stated above, pilot scale test work performed at the CMRL was not directed to modeling, but it provided some by-product data for this purpose and a good starting point for future work. It also pointed out the difficulties associated with simulating plant conditions in a closed circuit pilot test work. Pilot data showed that the separation
26
efficiency was also dependent on the degree of magnetization and % solids of feed, as well as other operating conditions that have direct bearing on upward velocity.
With regard to future test work, it was found that (1) circulation of feed through a magnetic coil for extended periods can cause over-magnetization before the system reaches a steady state condition, and (2) the circulated load becomes diluted due to preferential retention of solids in the hydroseparator. It is suggested that (1) over- magnetization could be avoided by placing demagnetizing and magnetic coils in series, and (2) the operation of a hydroseparator at a circulating feed of desired % solids may be achieved by re-adjusting % solids after the system reaches a steady state. The success of these remedial solutions is yet to be seen.
3.4 Fine Screen Modeling
Model development work related to development of fine screen modeling is limited to the analysis of plant data. Since detailed data defining operating conditions was not available, the data has limited use in terms of modeling. However, it reflects the performance differences existing among the plants that were sampled. Partition coefficients defining how each size fraction is split between undersize and oversize products are presented in Figure 23. This graph shows one aspect of the separation. Since fine magnetite acts like heavy medium on the surface of fine screens, magnetite and mostly magnetite bearing particles are selectively directed to underflow. Partition coefficients of each mineral type for the fine screens in the Inland plant are shown in Figure 24. It shows that mostly magnetite bearing particles have a coarser cut size, lower by-pass and higher separation sharpness. All these parameters promote the preferential separation of magnetite into the undersize, which becomes the final concentrate in some plants.
0
20
40
60
80
100
Pa rt
iti on
C oe
ffi ci
en t (
27
0
20
40
60
80
100
Waste Magnetite
Figure 24. Fine screen partition coefficients for magnetite and waste (Inland data)
Although partition curve approach, which is the model available in the Usim Pac, provides a structure for model development, linking the model parameters to operating conditions is the challenge of the future work. It is expected that pilot test work to be carried out in the near future will provide the answers.
28
4. PERFORMANCE ANALYSIS OF TACONITE PLANTS
Aforementioned data from the Iron Ore Coop project was also used to assess the prevailing performance of taconite plants. The mass balanced data used for assessment. Portions of these data are presented in the previous chapter. Here, a brief assessment of each plant’s performance will be presented. Detailed data from each plant is available at the Center and can be provided to plant engineers when needed. As noted before, the data represents the conditions existing in the plants in 1997. Since then, some plants modified their flow sheets, replaced old and inefficient pieces of equipment, and changes in ore mineralogy occurred. Nevertheless, some of the information could still be useful for the plant engineers. The actual objective is to illustrate the usefulness of the data collected for modeling and simulation purposes to evaluate performance. Results were presented to plant engineers and their implications were discussed.
A common problem in almost all the plants was the circulation of fine (Derrick) screen oversize to ball mills. This flow generally contained very high proportions of fine and low silica material (Table 7). Circulation of such material back to the ball mill is not expected to provide much benefit. Some plants have already installed regrind mills to treat this flow separately, and have reported success. In the next chapter, the results of simulation studies investigating various options for the treatment of this type of material will be presented. Apart from that, each plant appeared to have some minor/major problems or bottlenecks. Their brief assessment is presented below. The mass balanced flow rates and data, together with some raw data, are presented in the relevant appendices.
Table 7. Typical fine screen oversize characteristics.
Particle size (mesh)
% Silica
+ 65 0.5 27.0 45.0 - 65 +100 1.7 28.0 43.0 -100 +200 20.7 31.5 35.5 -200 +270 14.2 33.5 33.5 -270 +325 3.6 63.9 6.8 -325 +400 7.6 67.3 5.0 -400 +500 12.5 68.4 3.5 -500 39.2 69.8 3.0
29
4.1 The Evtac Plant
Available data from this plant did not include tailing streams from the hydroseparator, finishers and dewatering drums. It was also questionable as to how much finisher concentrate was fed to the fine (Derrick) screens in the plant. Mass balancing was based on size distributions of these streams provided by plant engineers, and estimated size by size assays. Therefore, mass balanced data representing the section of the flow sheet between the hydroseparator and Derrick screen should be treated with some caution. The performance summary data together with the assumed flow sheet are presented in Appendix D. It also includes a comparison of raw and mass balanced data, which indicates the quality of sampling and sample analysis.
Most of the iron losses (7.8%) in this plant occurred in the rougher magnetic separators. It seemed that inefficient/old magnetic separators operating at high circulating loads were the cause of such high losses. Plant engineers realized this problem, and these separators were later replaced by new and efficient ones. As expected, this modification resulted in increased recovery.
Although mag iron losses at the hydroseparator are low, only a small proportion of tails is separated at this step. More efficient separation of fine waste at the hydroseparators could provide benefits for downstream separation stages.
In terms of fines by-pass, Derrick screens in the Evtac plant were operated very efficiently. Despite this, the screen oversize contained over 50% very fine and low silica material. This problem has also probably been solved by even more efficient operation of the Derrick screens 5 .
4.2 The Ispat Inland Plant
The performance data from the Ispat Inland plant is given in Appendix E. Of the four plants studied, Inland had the highest mag iron recovery. It is believed that this was due to efficient use of equipment, as well as ore characteristics.
Data indicate that the ball mill was forming a bottle neck with circulating loads over 400% of the fresh feed (cobber concentrate). If the circulating loads were decreased, an increase in recovery would be expected, since roughers were operated within the closed grinding circuit. Although the plant was producing a desired quality concentrate, data shows that the separation capability of the hydroseparator was not fully utilized. Improving the efficiency of hydroseparators in separating the very fine silicate bearing minerals could provide substantial benefits, particularly for the flotation circuits at the plant.
30
4.3 The Minntac Plant
Performance of the Minntac plant is summarized in Appendix F. Most of the mag iron losses at this plant occurred at cobbers (4.8%), but this was a trade off with the amount of waste separated at this stage (48%). The mag iron recovery vs. particle size relationship shows that the losses in this stage are generally independent of particle size. However, it could be possible to decrease the loss by using higher strength/efficiency separators. This may result in a decrease in the amount of waste separated at this stage, causing some downstream problems.
The primary ball mill was operating at low circulating loads. Therefore, it had an ample capacity, which could be utilized for either grinding finer at this stage, thereby reducing the load at the secondary grinding circuit, or increasing plant capacity.
4.4 The National Steel Plant
Sampling was carried out after the well-known modifications were made to the plant flow sheet 2 . Performance data is given in Appendix G. This plant has a different operating strategy than the other three. Apart from having semi-autogenous grinding instead of a conventional rod-ball mill circuit, the feed to cobber was ground to a size much finer than the others. This provided the rejection of a very large proportion of waste (52% of the feed) at the first stage of separation, but this was accompanied with the largest mag-iron loss at the cobbing stage. The mag iron recovery vs. particle size relationship differed from the others as the fine fractions had relatively low recoveries. This implies that coarser grinding could decrease the mag iron loss in expense of separating less waste at this stage. It may also be possible to improve the recovery of fine and mostly liberated fine fractions by the use of more efficient magnetic separators. This may require optimizing operating as well as the design parameters of cobbers.
The ball mill was operating at a low circulating load. This allows ample capacity to handle larger amounts of cobber concentrates, if cobber recovery was improved in expense of separating less waste, e.g. coarser grinding at the semi-autogenous mill, or use of higher strength magnetic separators.
Apart from cobbers, the other separators had very high recoveries. The hydroseparator was operating at almost optimum conditions separating large amounts of fine waste with an acceptable level of mag iron loss.
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5. TACONITE PLANT SIMULATION
Using available plant data as a basis, detailed preliminary simulations were carried out for only two plants, i.e. Ispat Inland and Minntac. The reports of these simulations are given in Appendices H and I. These reports were presented to plant engineers working in the respective plants. The Evtac plant simulation was not performed due to ambiguity of plant data, since three tailing streams were not sampled. Although an agreement was reached with plant engineers to repeat the plant sampling to update plant data, this has not yet been accomplished yet. Only a limited number of simulations were performed for the National Steel plant and raw data from the plant was slightly modified to obtain mag iron recovery and % silica as close as possible to published data comparing plant performance before and after the well known flow sheet modifications at this plant. The objective was to examine the ability of Usim Pac to simulate plant performance for its original flow sheet using data from the modified one.
The modified data from the National Steel plant was first mass balanced and then, model parameters for each device were calculated using the model fitting functions of the simulator. The fit was satisfactory, but the problems listed in Chapter 2 were observed. Following the model fitting, the plant flow sheet prior to the modification was simulated. These flow sheets are presented in Figures 25 and 26.
O/F
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Figure 25. The current flow sheet of the National Steel Plant.
32
13
14
15
16
17
18
Figure 26. The original flow sheet of the National Steel plant.
Simulated performances of the two flow sheets are given in Table 8. The similarity between simulated and published data is remarkable. Following this success, two more flow sheet modifications were simulated. These were (1) the circulation of fine screen oversize to the rougher magnetic separator, as it was the case for some lines at the plant (Figure 27), and (2) the classification of screen oversize by a cyclone and circulation of cyclone underflow to the ball mill, and overflow to the primary screens (Figure 28).
Table 8. Simulated performances of the current and original flow sheets of the National Steel plant.
Stream Current Original Cobber Conc. Flow Rate (t/h) 100 80 Ball Mill Disch. (t/h) 151 164 Hydrosep. Feed d80 (μm) 75 60 % Silica 4.97 5.20 % Mag Fe 63.54 62.94 Total Fe 66.99 66.74 Concentrate (t/h) 60.82 48.97
33
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1819
20
21
22
2324
Figure 27. Simulated flow sheet 1: The secondary screen oversize is fed to the rougher magnetic separator.
Cobber concentrate
Rougher tails
1819
20
21
22
2324
25
26
Figure 28. Simulated flow sheet 2: The secondary screen oversize is fed to the hydrocyclone, cyclone undersize to the ball mill and oversize to the primary screens.
34
Simulation of the first modification showed that such modification would provide lower silica in the final concentrate with slight reduction in mag iron recovery (Table 9). The main objective of the second modification was to separate the fine high grade material from the stream directed to the ball mill, thereby preventing over-grinding and making more efficient use of ball milling. As expected, ore flow through the ball mill decreased. Then the feed rate to the circuit was gradually increased to obtain the same ball mill feed rate. For simulation purposes, cobber feed was assumed to have the same size distribution when the flow rate was increased. Results showed that such modification could create up to a 12% increase in capacity without causing any upset in the performance (Table 10).
Table 9. A comparison of the simulated performances of the current flow sheet and flow sheet 1.
Stream Current Flow Sheet 1 Cobber Conc. Flow Rate (t/h) 100 100 Ball Mill Disch. (t/h) 151 145 Hydrosep. Feed d80 (μm) 75 74 % Silica 4.97 4.68 % Mag Fe 63.54 64.57 Total Fe 66.99 67.73 Concentrate (t/h) 60.82 59.80
Table 10. A comparison of the simulated performances of the current flow sheet and flow sheet 2.
Stream Current Flow Sheet 2 Cobber Conc. Flow rate (t/h) 100 112 Ball Mill (t/h) 151 150 Hydrosep. Feed d80 (μm) 75 76 % Silica 4.97 5.04 % Mag Fe 63.54 63.39 Total Fe 66.99 66.89 Concentrate (t/h) 60.82 68.29
The main focus of Ispat Inland plant simulations was to examine the effect of operating condition/flow sheet modifications to decrease the load around the ball mill circuit, which appeared to be a bottleneck. Other simulations were also carried out to seek ways of
35
improving plant performance. Some modifications provided very promising results (Appendix H). It was recommended that these preliminary simulations should be repeated using updated plant data. For this purpose, the plant was thoroughly sampled while processing one of the two blends treated in the plant. Sample analysis is continuing. It is planned that the sampling will be repeated when the other blend is processed.
Simulation work on the Minntac plant involved a number of operating condition and flow sheet modifications to improve plant efficiency. Simulations showed that the modifications could only provide limited improvements in terms of grade and recovery, but results were promising for increasing the capacity. It was also found that the simulator successfully mimicked the response of the automatic control system used in controlling silica levels in magnetic concentrate going into the flotation circuit by manipulating the feed flow rate to the rod mill. Results also indicated that better plant control might be achieved by controlling the grind size from the primary ball mill circuit (Appendix I).
36
6. CONCLUSION
It has been three years since the Iron Ore Coop decided to establish a concentrator modeling center within the CMRL. Over this period, the Center has reached a mature state. Software was acquired; the training and testing period has been completed. Weaknesses and strengths of the software have been well identified. Development of liberation and magnetic separator models were set as short term objectives. The liberation model has been completed and will be incorporated into the software very soon. Progress has been made with regard to the development of magnetic separator modeling. The collected plant data showed what type of mathematical structure could be used to define the performance of these devices. It also provided valuable information indicating which parameters have significant effect on their performances. However, more plant data is needed for the development of a model capable of simulating their effects through mathematical relationships.
In the long term, improved mathematical models of hydroseparators and fine screens will be required. Some progress has already been made in this direction. As more data becomes available through pilot and plant test work, these models will mature into a reliable and accurate state.
Despite the shortcomings of the software, it has been shown that it can be reliably used for simulating taconite plants. It can also be used to evaluate plant performance, as well as studying the effects of various options to improve the efficiency. Addition of new and improved models will make the software more versatile by simulating the effects of a large number of operating conditions, and allowing it to be used as a tool to study how to control a circuit or device. That will also increase the reliability of simulations.
37
APPENDIX A
A COMPARISON OF MASS BALANCING SOFTWARE: USIM PAC, JKSIMMET AND MATBAL
38
A Comparison of Mass Balancing Software: Usim Pac, JKSimmet and Matbal
A study was carried out to investigate the mass balancing capabilities of three different programs, namely Usim Pac, JKSimmet and Wiegel`s Matbal. The first two are commercially available and popular software. Although a version of Wiegel’s Matbal is also commercially available, non-commercial version was used in this study.
The data obtained from the complex grinding circuit of a flotation (Cu and Zn) plant was used as the basis for comparison. The circuit consisted of two ball mills and two sets hydrocyclones operating in series (Figure 1). The primary cyclone unit included two hydrocyclones operating in parallel, each of which was sampled separately. In total there were 15 streams. The samples went through screen analysis and cyclosizing to determine their size distribution in the range of 20 mm to 9 micron. Each size distribution was defined by 26 size fractions.
To Flotation Circuit
1 2
71
72
Figure 1. The flow sheet of grinding circuit used in the study
The raw data had been mass balanced using mass balancing algorithm of JKSimmet earlier by Dr. Ergun in Hacettepe University, Ankara, Turkey, who provided the mass balanced and raw data. His noted that there could be relatively large errors in the Primary ball mill discharge (Stream 2) data, since it was difficult to sample this stream due to high a flow rate and coarse nature of its size distribution. This point was taken into account
39
when the data was mass balanced the other two programs by allocating relatively lower accuracy to this particular stream data.
The results are compared using two criteria, calculated flow rates and adjusted size distributions. The calculated flow rates of the main streams are presented in Table 1. In general, calculated flow rates are very close to each other. There were relatively larger deviations at only two streams (6 and 8). This was probably due to the differences in defining accuracies in each program. It was found that manipulating the accuracies of data points could reduce the deviations.
Table 1. A Comparison of Flow Rates Calculated by Different Mass Balancing Programs
Flow Rate (t/h) Stream Number Usim Pac JKSimmet Matbal
2 120 120 120 3 226 235 235 4 226 235 235 5 346 355 355 6 106 127 98 7 240 228 257 8 120 108 137 9 120 120 120
Despite of the similarity between the flow rates calculated by different mass balance algorithm, there was significant difference in terms of fit to the raw data provided by each algorithm. The difference was particularly significant for the pan (screen undersize) fraction. This is shown in Table 2. While both Usim Pac and JKSimmet provided good fit to the raw data, Matbal provided unacceptably large deviations due to the fact that Matbal does not include the pan fraction in its flow rate and data adjustment calculations. It calculates the adjusted value by difference. This resulted in accumulation of large errors in this fraction for the streams with relatively low sampling accuracy. The possibility of resolving this deficiency by the use of cumulative size distribution values was tested by carrying out mass balance calculations, it was found that, although this improves the fit of adjusted size distributions, it results in larger deviations in flow rates.
40
Pan Fraction (%) Stream Number Measured Usim Pac JKSimmet Matbal
2 11.16 14.02 11.73 27.90 3 5.01 5.46 5.57 7.78 4 13.61 11.83 12.16 6.19 5 13.14 12.58 12.25 13.55 6 6.46 5.78 5.88 4.93 7 15.74 15.6 15.80 16.84 8 5.04 5.18 5.03 9.84 9 25.04 26.04 25.05 24.82
When the fitness provided by the Usim Pac and JKSimmet are compared on the same basis, it appears that JKSimmet gives slightly better fit to the raw data. The mean deviations between measured and calculated size fractions of Stream 2 were 0.64% and 1.78% respectively for the JKSimmet and Usim Pac. This stream had the lowest accuracy and deviations were relatively large (Figure 2). Although the deviations were lower for the stream with highest accuracy (Stream 9), JKSimmet provided slightly better fit with a mean deviation of 0.15%, as compared to 0.23% of the Usim Pac. However, it may be possible to improve the fit by adjusting the measurement accuracy of each data point. In the calculations, it is assumed that all size fractions of given stream had the same level of relative accuracy.
41
42
A BRIEF SUMMARY AND EVALUATION OF SCHNEIDER`S LIBERATION MODEL
1. Introduction
The model is based on the linear grade measurements obtained from SEM pictures of polished sections containing broken particles of narrow size range. It appears that the primary aim was to develop a transformation kernel to calculate volumetric grade distributions from these measurements. The validity of the kernel was, then, tested by applying it to an emulated closed circuit grinding and iron ore concentration plant.
2. Development of a Transformation Kernel
For the development of the kernel, density fractionated particles of dolomite - sphalerite ore of narrow size (-1000 +710μm) were prepared using MAGSTREAM device. Seven density fractions were produced within a density range of 2.9 - 4.0 g/cc. The samples taken from density fractions were mounted in epoxide resin. Then, polished sections representing each density fraction were prepared. SEM images obtained from polished sections were used to determine the linear grade distributions. The volumetric grade of each density fraction was determined from linear intercept measurements of each mineral. The agreement between the densities measured in the laboratory and estimated from volumetric grades was very good indicating that the volumetric grades estimated from SEM images were reliable. The linear intercept measurements, volumetric grades and volumetric distribution of density fractionated data provides the basis for the development of the kernel. The steps involved in developing the kernel are summarized in Figure 1.
First, the linear intercept measurements of each density fraction were classified into 12 linear grade classes ranging from 0 to 100 and frequency of each was determined. Zero corresponds to apparent liberated linear dolomite measurements, while 100 to apparent liberated linear sphalerite measurements. The remaining ten classes represents unliberated linear grades with 10% intervals.
Secondly, the liberated linear measurements (0 and 100 linear grade classes) were discarded and distribution of unliberated linear measurements was determined for each density fraction. The data was then converted to cumulative distribution.
Apparent linear liberated fractions were plotted against the average volumetric grade. Examining the shape of the curves, it was concluded that the variation of apparent linear liberation of dolomite and sphalerite could be defined by the equations below.
)1)(1()1()1( 11 )1( 2
where
=− )1()1( vA gL The fraction of all particle intercepts that reports as
liberated phase A that is generated by particles of volumetric grade vg .
=)()1( vB gL The fraction of all particle intercepts that reports as
liberated phase B that is generated by particles of volumetric grade vg .
=BBAA and 2121 ,, ξξξξ Arbitrary model parameters.
Cumulative distributions of unliberated linear measurements were plotted against linear grade classes and it was concluded that the shape of relationship could be defined by Incomplete Beta function. The parameters of the functions are its first (mean) and second (corresponding to variance) moments. General form of incomplete beta function and equations defining the relationships between its parameters and, first and second moments are given below.
))(),(()( vvgvllu ggIggF l
)(1 v B gn and )(2 v
B gn =The first and second moments of the unliberated linear grade distribution produced by particles of volumetric grade vg .
The second moment of the distribution can be written in terms of its first moment and variance.
2 1
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volumetric grade, unliberated linear grade distribution, with respect to phase B.
For the use of incomplete beta function, the variation of its first and second moments with linear volumetric grade needs to be defined. The first moment and variance of unliberated linear distributions were calculated for each density fraction and plotted against volumetric grade. Evaluating the shape of curves, it was concluded that their relationship could be defined by the equations below.
1211 )1()( θθθ +−−= vv B ggn
vvvvv ggggg 431 2 )1()1()( 2 ωωωσ ω +−+−=
where 432121 ,,,,, ωωωωθθ = Arbitrary model parameters
∫ ∫
−
−
∗
∗
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=∗ )( DgF l j lu Cumulative unliberated linear grade distribution, weighted
by length, that is generated by particles in a finite size range and in volumetric grade class j (corresponds to the experimental measurements).
=)1( AjL Apparent linear liberation of phase A generated by particles in
volumetric grade class j (corresponds to the experimental data). =)1(
BjL Apparent linear liberation of phase B generated by particles in volumetric grade class j (corresponds to the experimental data).
=∗ )( Dgf vv Volume weighted volumetric grade distribution density of particles in a finite size range.
Rosenbrock hill climb was used as the optimization method and Romberg integration with values of function )( ∗Dgf vv obtained from the slopes of the corresponding cumulative distribution. It appears that a curve was drawn between the experimental points using flexicurve and then slopes were determined from the tangents drawn to this curve.
The transformation kernel was discretisized for its easier use in calculations. A symmetrical transformation kernel was also developed for minerals having symmetrical texture. Symmetrical texture means that the apparent linear liberation distribution of both minerals are the same and the unliberated particles transformation kernel must be symmetric with respect to the two phases. This reduces the number of parameters defining the transformation kernel to 6 ( 32121 ,,,,, ωωωϑξξ ). A discretisized symmetrical transformation kernel was also developed by assuming that dolomite sphalerite ore exhibits symmetrical texture.
3. Inversion of the Transformation Equation for Stereological Correction
Although discrete form of the transformation equation appears to be suitable for direct inversion, this was not used due to ill nature of the problem. A constrained optimization technique was applied to calculate the best values of each volumetric liberation grade class in cumulative form. This method required the calculation of 11 values. The frequency of remaining class was calculated from the difference.
Rosenbrock`s hill climb method was used for optimization. The criterion for optimization is given below. Tikhonov regularization was used for determining the initial values.
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where NSR= Normalized sum of residuals.
=ilF A measured cumulative fraction in the discrete cumulative linear grade distribution, by length.
=ilF A calculated cumulative fraction in the discrete cumulative linear grade distribution, by length.
4. Development of Quadrivariate Breakage Function
Quadrivariate breakage function defines how the liberation distribution of a progeny size would be when a size fraction of known mean grade and liberation distribution is broken.
Basic Assumptions: • The quadrivariate breakage function is unambiguously normalizable with respect to
parent size, i.e. breakage function can be characterized from samples in a single size range along with a set of narrow grade samples that cover the width of diagram. This means that the quadrivariate breakage function for an ore could be determined by separating a narrow size sample into narrow grade samples and separately grinding these narrow grade samples. The model parameters that are obtained by fitting the function to this data would be valid for all sizes.
• The liberation and breakage processes can be decoupled; i.e. the distribution of progeny sizes is independent of parent composition. This means that the characteristic size distribution (breakage function) of an ore does not change with its (or of the size fractions) initial grade.
4.1 Experimental Studies
Each grade class was ground to -710μm in an ultrasonic mill and then sieved through 500, 355, 250, 180 and 106 μm sieves. Polished sections representing each grade and size fraction were prepared. Volumetric grades and linear distribution of phases were determined from SEM images. The linear distributions were converted to volumetric distribution using the transformation kernel. This data forms the basis for the quadrivariate breakage function.
4.2 Mathematical Structure
The following two equations defines the mathematical structure of quadrivariate breakage function.
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kl v the discrete breakage function that is produced by
. comminution of monosize and monograde particles,
=′′ ),;( DgDgB v k vv the cumulative conditional on size quadrivariate
breakage function
=′′ ),;( DgDf vv the size distribution in the progeny originated at point )( ,Dgv ′′ .
=klij vb the volume fraction of material in the size range of ),( 1−ll DD that
report to grade range ),( 1−k v
k v gg and which originate from grade
range ),( 1−′′ j v
j v gg and size range ),( 1−′′ ii DD . They are measured
directly by image analysis and using the stereological correction procedure.
For the practical use of the above equations, the functional forms of two components are required. These are the size distribution of progeny originated at point ),( Dgv ′′ i.e.
),;( DgDf vv ′′ and the cumulative conditional on size quadrivariate breakage function i.e. ),;( DgDgB v
k vv ′′ .
The size distribution of progeny was defined by spline fitting and extrapolating the experimental volumetric distribution on linear x linear scales.
It was proposed that internal structure of quadrivariate breakage function could be defined by incomplete beta function using Andrews – Mika diagrams. However, the use of this approach requires the measurement of geometrical textural parameter,φ, and determination of the first moment of the function, i.e. the variation of parent grade with parent size. These are explained below.
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4.3 Measurement of Geometrical Texture Parameter
One of the key parameters determining the volumetric grade distribution is geometrical texture parameter )( vS . It is defined as the ratio of surface area / unit volume and calculated from average intercept length (μ).
vS = 4 / μ
Chord lengths were measured across the entire particle cross sections. Average intercept length associated with particles (μ p ) is calculated. Then,
p p vS μ
4=
The same procedure was used for calculating aμ and bμ by taking only one phase into account. S A
v and S B v were calculated using the equation above. The interphase area / unit
volume was calculated by balancing the surface areas of the phases and that of particles taking into account the volumetric abundance of each phase.
S ( )( )PvB vv
for sphalerite.
The geometrical texture parameter (φ) of the ore was calculated using the relationships below.
v AB vp gSd .. φ= and )1.(. v
BA vp gSd −= φ
The geometrical texture parameter, φ, is calculated plotting Sd p . against vg and drawing a best fit line through the points.
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4.4 The Variation of Grade with Particle Size: The First Moment of Incomplete Beta Function
The variation of grade with particle size was examined and a model consisting of three arbitrary parameters was proposed. These were minΔ =location of minimum grade,
minΓ =minimum average grade at D= minΔ and the location of the crossing point in the progeny size domain, 0Δ .
uugDgDg vvv log..),;( 2 1
αα+′=′′
The coefficients of u, 1α and 2α were defined in terms of 0Δ , minΔ and minΓ . Their best- fit values were calculated using an optimization routine. It is assumed that it is normalizable with respect to parent size.
4.5 Modeling the Conditional Quadrivariate Breakage Function
The model is based on Andrews – Mika diagram. There are two definitions forming the model basis. These are limiting boundaries and accessible region. The limiting boundaries define the theoretical upper particle size limits for a given grade of progeny particles when a binary parent ore of grade vg ′ and size D′ is broken. They are independent of the texture of the ore. Based on conservation of phase volume, the following equations are derived to define the limiting boundaries.
3/1
g DD
where =V
B V A DD , limiting liberation size associated to phase A and B
respectively.
On the other hand, accessible region defines the practical limit and is strong function of the texture of ore. The boundaries of the accessible region are associated with the limiting boundaries and the geometrical texture parameter φ.
The following functional forms were proposed for the boundary of the accessible region associated with phase A.
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AP


′ =
=
where == )0( vA gD the critical size at which liberated phase A starts appearing
=AD the boundary of accessible region and =vg Average volumetric grade of particles that have size D, generated
from breakage of particles that have volumetric grade vg ′ and size D′ .
Similarly for phase B,


′ =
=
The following equations were proposed for defining the critical sizes at which liberated phases start appearing.
V A
C A
C B
C Aφ and C
Bφ are the critical texture parameters associated with phases A and B.
For the modeling of internal structure of the accessible region by incomplete