Simulation assisted performance improvements in ...

Simulation assisted performance improvements in iron ore processing plants S. Ersayin Program director, Coleraine Minerals Research Laboratory (CMRL), Coleraine, Minnesota

W.M. Bond Division manager, Business & Technology, ArcelorMittal, Virginia, Minnesota

J. Arola Mineral development consultant, Minnesota Department of Natural Resources, Hibbing, Minnesota

R.J. Strukel Contract process engineer, ArcelorMittal, Virginia, Minnesota

B. Kettunen Process Engineer, Noramco Engineering, Hibbing, Minnesota

Abstract In 1998, the Concentrator Modeling Center was established within the Coleraine Minerals Research Lab, Coleraine, Minnesota, to develop models for simulation of iron ore processing plants, and to assist plant operators in improving performance of their aging plants. The short-term objective was to develop necessary models for reliable simulation using conventional techniques, while the long-term objective was set as liberation-based modeling and simulation of these plants. The short-term objective was achieved in 2002 after the development of models for magnetic separators, hydroseparators and fine screens. A project funded by the U.S. Department of Energy was carried out to demonstrate the capability in improving iron ore processing at the Minorca plant. As a result of this study, a 10% improvement in throughput at the plant was obtained, and simulation became a desired tool for plant operators. A significant step in the long-term objective was reached in 2005 with the development of a liberation model. This was followed by incorporation of models capable of processing liberation data into the software. By early 2007, the Concentrator Modeling Center had the capability of performing liberation-based simulation of entire plants. Key words: Iron ore, Computer modeling, Process simulation

Introduction Iron ore mines on the Iron Range of northern Minnesota supply 80% of the iron ore pellets produced in the United States. Currently, there are six iron ore processing plants operating on Minnesota’s iron range, processing approximately 122 Mt (120 million long tons) of iron ore to produce 40.6 Mt (40 million long tons) of iron ore pellets annually. These processing plants were built in the early 1960s, and they have not gone through major modifications over the years. A typical plant has at least two stages of grinding; three stages of magnetic separations, namely, cobbers, roughers and finishers; and fine screens as one of the final stages of processing. Because silicate gangue is

hard to grind and because it has a low ore grade, it becomes essential for these plants to separate gangue particles as soon as a substantial portion gets liberated. This strategy resulted in the integrated size reduction and concentration flowsheet shown in Fig. 1. During the early 1990s, one of the plants employed a combined simulation and pilot-scale testing approach to evaluate a performance-improving concept. Plant implementation of the idea resulted in substantial performance improvements (Wennen et al., 1995). The magnitude of the improvement in circuit capacity (reported at 34%) and the decrease in unit energy consumption (reported at 25%) for this plant were remarkably large. This develop-

Paper number MMP-08-016. Original manuscript submitted March 2008. Revised manuscript accepted for publication August 2008. Discussion of this peer-reviewed and approved paper is invited and must be submitted to SME Publications Dept. prior to Aug. 31, 2009. Copyright 2009, Society for Mining, Metallurgy, and Exploration, Inc.

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Figure 1 — The ArcelorMittal Minorca plant magnetic circuit flowsheet showing ore processing steps in a typical plant.

ment sparked an interest among other plant operators to use a simulation-based approach. 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 to develop models for reliable simulation of iron ore processing plants in the area and to provide service to plant operators. 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. Usim Pac mineral processing software was selected due to the availability of a larger number of models and model incorporation capability for adding those to be developed in the near future. 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 iron ores 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. For the same reason, plant operators were interested in complete plant simulations rather than performance improvement in one or two pieces of processing equipment. Due to liberation modeling being a prerequisite to simulation of these plants, development of unit operation models for iron ore processing plants was stalled. Reliable models for magnetic separators, hydroseparators and fine screens were not available. An initial effort at integrating the modeling of size reduction/mineral liberation was carried out by Wiegel for the Erie Mining Company process in 1976 (Wiegel, 1976). In the early 1990s, Schneider developed a mineral liberation model for magnetic iron ores based on liberation characterization by scanning electron microscopy measurements (Schneider, 1995). Schneider’s model was validated using plant data obtained from the Fairlane Plant of Eveleth Taconite. Although the approach was very promising, liberation measurements involved costly scanning electron microscopy, which could not differentiate between magnetite and hematite. The plants on the Iron Range consider magnetite as the only valuable mineral to recover, while hematite is February 2009 • Vol. 26 No. 1

considered as gangue. Therefore, plant operators on the Iron Range have never utilized this approach. A liberation model for magnetite-bearing iron ores was still needed for realistic simulation of taconite plants. The first step for the Concentrator Modeling Center was definition of the short- and long-term objectives. The longterm objective was established as liberation-based modeling and simulation of iron ore processing plants. This involved development of an integrated grinding-liberation model, as well as unit operation models that are capable of processing liberation-based data. Development and validation of such models were expected to take a number of years. In the meantime, there was an urgent need to carry out reliable simulation with the current modeling capabilities, which was limited to conventional multi-component and size-recovery type relationships, with no direct liberation information. The short-term objective for the Concentrator Modeling Center was to define the most significant shortcomings of the available software and develop multi-component and size-recovery-based models for the unit operations that are commonly used in magnetite-bearing iron ore processing plants. These unit operations included magnetic separators, hydroseparators and fine screens.

Conventional model development As noted above, the short-term objective was to gain the capability of simulating iron ore processing plants using conventional multi-component size-recovery models. The first step was to assess the accuracy of available Usim Pac models to simulate various modifications in the existing plants. This study discerned that Usim Pac simulations provided reasonable results for minor changes in operating conditions, but failed when major changes in operating conditions and flowsheets were simulated. It was concluded that there was a need to develop reliable models for magnetic separators, hydroseparators and fine screens. It was also found that iron ore processing could be considered as a binary mineral case, with the two minerals being magnetite and gangue. This classification assumes that magnetite is the only valuable mineral in the ore, and the remaining iron minerals, as well as the silicates, are gangue. This was in line with iron ore processing plant-operating practices, where recovery 42

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calculations are based on ferromagnetic (Satmagan) iron measurements, not total iron in feed and products. Later, a need for other models emerged due to newly developed pieces of separation equipment having potential use in iron ore processing. These were smart screens, stack sizers and a new generation of hydrocyclones, such as Cavex and gMax. Magnetic separator development was the most crucial portion of the conventional modeling study, because each plant on the Iron Range had at least three stages of magnetic separation in its flowsheet. For this purpose, a large set of plant data was analyzed. This information included size-by-size data from each magnetic separation unit, as well as each drum from a given unit. These analyses led to the development of a pseudoliberation concept. Data indicate that magnetic separation could not be considered as a deterministic process in terms of the particle size-mineral recovery relationship. Separation becomes harder as feed grade to a given magnetic separator increases. Consequently, less gangue and magnetite report to the tailing stream. The pseudo-liberation model uses plant data and cubic spline functions to create a plant operating surface as a magnetic separator model for a given plant and ore. The surface is then manipulated to predict size-recovery curves for a given feed grade and mineral component. Details of this model are given elsewhere (Ersayin, 2004). Model fitting uses five-knot spline curves for each mineral component to describe the recovery-size relationship for a given magnetic separator. Coordinates of each knot are adjusted to obtain the best fit to mass balanced plant data. Existing data from three stages of magnetic separation are then combined to generate a plant-operating surface, representing all three magnetic separators operating in the plant. Complete plant simulation of existing conditions is carried out to check the accuracy of the model. Fine-tuning might be required to improve the fit to plant data in terms of flow rates, recoveries, grades and size distributions. This completes the model calibration. Then, the model becomes ready for simulation of proposed modifications. Capability of the model to simulate magnetic separation was validated using data obtained under different operating conditions while the plant was processing the same ore type. Results obtained from such an exercise are illustrated in Fig. 2 for the cobber magnetic separator. Hydroseparators are thickener-like devices. They separate very fine gangue particles from magnetically flocculated magnetite. Because the process is very similar to classification, a partition curve-based approach was applicable to hydroseparator modeling. A set of plant data was analyzed, and eventually the pseudo-liberation modeling concept was combined with Rosin-Rammler equations defining partition curves for each component (Ersayin, 2006). This model implied that partition curve parameters were dominantly controlled by the feed grade. Plant data indicated that this was a reasonable assumption. A current study to include operating variables, such as feed percent solids and upward velocity in the model, is still being carried out. For a given feed grade, the model predicts three parameters, namely, bypass, d50c and imperfection, for each mineral component. Calibration factors are introduced for fine-tuning the model for a given plant and ore type. Model fitting involves adjusting of calibration parameters to obtain the best fit to plant data. Once calibrated, the model can be used for simulating the hydroseparator operation under different feed conditions. Results of a model validation study are illustrated in Fig. 3. The model was calibrated using one set of plant data. Then it was used to simulate the hydroseparator MINERALS & METALLURGICAL PROCESSING

Figure 2 — A fit of the calibrated pseudo-liberation magnetic separator model to cobber magnetic separator data obtained under different feed conditions.

Figure 3 — Plant data showing results of a model validation study for hydroseparator.

performance processing the same ore blend while operating under different conditions. A similar mathematical structure was used for fine-screen modeling. Development of a model for fine screens involved a large set of pilot-scale testing under predefined conditions (Pletka, 2004). Resultant data were fitted to Rosin-Rammler type partition curves separately for each mineral component, and model parameters were calculated. Then, empirical equations were developed to define the relationship between the model parameters, bypass, d50c and imperfection, and the operating conditions, including feed rate, percent solids, size distribution and screen mesh. The last step was the scale-up of the model to plant-size operation. This was simply incorporated as a multiplier for the feed rate roughly proportional to the surface areas. The model also had calibration parameters for fine-tuning of the model for a specific application. Once calibrated, the model provided a very good fit to the plant data obtained under different conditions, as shown in Fig. 4. After the essential models were developed, modeling efforts were shifted to the potential devices that could be used in iron ore processing. Recently, models for stack sizers and gMax cyclones were developed (Ersayin, 2007). Stack sizer modeling was very similar to fine screens. Derrick Corp, Buffalo, New 43

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capability in improving iron ore processing at the Minorca plant. The plant has three major processing units: crushing, concentrator and pelletizing. The pelletizing unit of this plant had 10% ample capacity. The concentrator appeared to be the bottleneck limiting pellet production. This implied that the pelletizing unit could handle additional tonnages if the concentrator could produce more concentrate. Based on this background, the objective was set to increase plant throughput by increasing concentrate production by 10% using simulation as a tool to evaluate and quantify benefits from performance improvement ideas. Such a task required a team effort involving plant personnel, from managerial to plant floor level; an outside consultant; and a simulation expert and vendors. The study involved all the essential elements of such work, from plant sampling, performance analysis and development of performance improvement ideas, to simulations, plant implementation and validation. The Minorca plant processes approximately 9.1 Mt (9 million long tons) of iron ore to produce 2.84 Mt (2.8 million long tons) of iron ore concentrate containing less than 4% silica. The plant has three parallel lines of magnetic separation circuits. Magnetic concentrate from three lines is combined and fed to a flotation circuit. Two different ore blends are fed to the plant during selected periods of a year. Magnetic and flotation circuit flowsheets are shown in Figs. 1 and 6, respectively. Plant sampling involved all the streams in both circuits and was repeated for the two blends. Flotation circuit samples also included cell-by-cell samples to generate kinetic data for simulations. Raw data from each circuit was separately mass balanced. Based on mass-balanced data and flow rates within the circuits, performance analysis concluded that ball milling was the major bottleneck within the magnetic separation circuit, limiting concentrator throughput; hydrocyclones had high bypass (Fig. 7), an indicator of low efficiency. Following discussions among the team members, a large number of performance improvement ideas emerged. Further discussions led to elimination of several less promising/high-cost options. Eventually, it was decided that benefits from the following list of modifications should be quantified through simulations to assist management in carrying out a benefit/cost analysis:

Figure 4 — A typical fit of the conventional fine-screen model to plant data obtained under different operating conditions.

Figure 5 — Simulated fit of gMax model to actual product size distributions from cyclones operating under different conditions.

York, provided a large set of test data generated under different operating conditions using four different screen mesh sizes. Empirical equations defining relationships between operating conditions and model parameters were developed. For gMax cyclones, a set of plant data representing cyclone performance under different operating conditions was analyzed. It appeared that the conventional Plitt model (Plitt, 1976) could be adapted to model these cyclones. A major modification was adjustment of the hydrocyclone pressure drop. A simple multiplier for pressure provided a reasonable fit to the plant data. Once the pressure was adjusted and the model was calibrated for a given operation, it was capable of satisfactorily predicting cyclone performance within the operating range studied (Fig. 5).

• Dry cobbing. • Hydrocyclone efficiency improvements: - two stage hydrocycloning, - retrofitting the existing hydrocyclones, - high-efficiency hydrocyclones and - stack sizers replacing hydrocyclones. • Ball mill efficiency improvements: - makeup ball size, - increased ball charge, - increased critical speed and - feed percent solids. • Fine screen feed dilution. • Fine screen oversize grinding. • Preclassification of flotation feed.

Validation of conventional simulation The short-term objective was achieved in 2002. By then, improved mathematical models for magnetic separators, hydroseparators and fine screens had been developed. The next step was the demonstration of simulation capabilities to show that these models could successfully be used in simulating iron ore processing plants. Plant operators were still skeptical about the use and reliability of simulation as a tool in improving performance of their plants. The U.S. Department of Energy (DOE) provided funding for a project to demonstrate this February 2009 • Vol. 26 No. 1

As the first step in simulating the circuits, models were fitted to mass-balanced data and conditions during plant sampling were simulated. An excellent fit to the mass-balanced data was obtained in terms of all criteria, including flow rates, size distributions and chemistries. Then, simulations were performed by changing each condition above, one at a time, to allow simple comparisons. For magnetic circuit simulations, the main criterion for comparisons was the ball mill discharge rate. A 44

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Figure 6 — Simplified flowsheet of the Minorca plant flotation circuit.

lower discharge rate was considered as a relief or potential for throughput increase. The secondary bottleneck was the fine screen feed rate. Therefore, hydrocyclone overflow should be kept lower or very close to baseline rates. Mass-balanced data were used as the baseline, and the effect of each modification on circuit performance was recorded. The final step was determination of increased circuit throughput for each performance improvement option. For this purpose, the rod mill feed rate was gradually increased until one of the following conditions was satisfied: the ball mill discharge rate reached the baseline level; 10% increase in rod mill feed rate was achieved; or circuit was destabilized due to too high flow rate/coarse feed to fine screens. All the simulated modifications provided varying degrees of improved performance efficiency. The most significant improvement was achieved by simply changing the makeup ball size from 50 to 38 mm (2 to 1.5 in.). Simulations indicated that this modification by itself had the potential of increasing plant throughput by 10%. It was easy to implement in the plant and did not require any capital investment. The only disadvantage was the higher cost of makeup balls, which could reasonably be overcome by the expected benefits. Nevertheless, the study recommended that the makeup ball change should be accompanied by replacement of existing hydrocyclones with more efficient ones. It was also anticipated that the existing aged cobber magnetic separators would not be able to handle increased feed rates. Their replacement was also proposed for capacity and maintenance issues. As listed above, flotation circuit simulations were limited to preclassification of flotation feed, which involved classification of flotation feed into fine and coarse products and differential treatment of each sized stream. The coarse stream would then be sent to flotation, while the fines would be treated by the additional step of magnetic separation. Details of these simulations are presented elsewhere (Ersayin et al., 2005). Flotation simulations were also carried out to examine the sensitivity of the flotation circuit to handle the increased feed rate. These simulations concluded that there would be a minor increase in the final concentrate silica, which, based on plant experience, could easily be controlled to the desired level by increasing amine dosages. MINERALS & METALLURGICAL PROCESSING

Figure 7 — Partition curves of existing hydrocyclones, showing high bypass during plant sampling surveys.

After selection of the most feasible option, additional simulations were carried out to test the sensitivity of the modified circuit as to minor variations in ore characteristics and performance. These included coarser rod mill feed size, changes in feed grade and less-efficient separation at the new hydrocyclones. These simulations indicated there would be minor variations in plant throughput due to any of these potential variations in plant operation. Initially, partial replacement of makeup ball size was tested on one line. Performance improvement on this particular line was so obvious that all three lines were immediately converted to 38-mm (1.5 in.) makeup ball size. Other proposed modifications were gradually implemented at the plant. To validate simulation results, plant sampling surveys were repeated for both blends. One of these surveys was performed after only the makeup ball size took place. The other was performed after more efficient hydrocyclones and some of the new cobber magnetic separators were installed. Data from validation surveys were mass balanced, and flow rates were calculated. Plant sampling survey operating conditions were simulated and compared to mass balanced data. A summarized comparison of simulated and mass balanced data is presented in Table 1. Despite the major 45

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The alternative was the Gaudin Random Liberation Model (GRLM). Gaudin first proposed the concept as early as 1939 (Gaudin, 1939). The model was based on binomial distribution of cubic mineral grains in the ore matrix. Later, this concept was refined by Wiegel to develop a model to predict the proportion of free gangue and free valuable mineral in a given particle size range for a given grain size and volumetric ore grade (Wiegel, 1964). The model correlated well with sizeby-size Davis tube test data and directly measured average grain sizes for magnetite bearing iron ores (Wiegel, 1974). Wiegel also introduced directional coefficients to model the manner in which particles in one liberation and size class are distributed among the liberation-size classes. More recently, a third parameter, dilution, was added to the model to define the effect of barren rock fed to a plant due to mining practices (Wiegel, 1999). The last step was integration of these concepts into a flexible population balance-type grinding model and its incorporation into Usim Pac (Brochot et al., 2006; Wiegel, 2006). Although the liberation model correlated well with Davis tube test data and it was possible to calculate the model parameters from such data, there was still a need to validate the integrated model. Liberation data measured by Schneider (Schneider, 1995) using his controversial methodology was considered a preliminary step to validation. Despite inability of these measurements to differentiate between the different iron ore minerals, it presented a binary mineral case compatible with the model. Availability of model fit data generated by Schneider’s own model provided an opportunity to compare the results of two different models. Wiegel’s integrated liberation/grinding model is robust. It requires liberation model parameters determined through size-by-size Davis tube tests. Due to the unavailability of actual ore samples representing plant sampling conditions, a constrained model fitting was applied. This process included adjustment of breakage distribution and breakage rate parameters to obtain a best fit to size distribution data and then fine tuning of three liberation model parameters, i.e., effective grain size, volumetric grade and dilution factor, within the range that the ore from this particular mine (Utac, formerly Evtac) is expected to have. Measured and model-fitted liberation data are illustrated in Fig. 8. Fit between the two appears to be very good. Model-fitted data were also visually compared to Schneider’s simulations. It was concluded that the two models provided a very similar fit to measured data. This exercise provided a preliminary validation for Wiegel’s integrated model. The next step was to gain the capability of carrying out complete plant simulation using liberation-based modeling. This objective required liberation-based models for all the unit operations common in iron ore processing plants. For magnetic separator modeling, a liberation-based model developed by Schneider using conceptual structure proposed by Wiegel was already available (Schneider, 1995). This model was incorporated into Usim Pac for further testing of its versatility. For hydroseparators and fine screens, existing conventional models were adapted to liberation-based models. Conventional models define the relationship between particle size and mineral component recovery. A liberation-based model, on the other hand, has this relationship as a constraint, while a third dimension is introduced to define the variation of recovery/partition coefficient within liberation classes of a given particle size fraction. Following a trial-and-error-type study, it was found that an exponential function defining an almost abrupt change from one behavior type to another was the best approach for adaptations of conventional models. This

Table 1 — A comparison of major simulated and actual performance criteria representing validation sampling survey periods. Blend 2

Blend 1 Performance criteria

Simulated Actual Simulated Actual

Feed rate (ltph) Ball mill discharge rate (ltph) Hydrocyclone pressure (psi) Fine screen feed rate (gpm)

360 1,228 20.2 1,363

360 1,239 20 1,351

360 1,180 17.0 1,068

360 1,189 14.1 1,342

Magnetic concentrate results: 80% passing size (μm) 44 Magnetic iron (%) 65.0 Recovery (%) 96.0

45 65.3 95.2

49 64.5 94.7

50 63.8 94.5

modifications, the fit between mass balanced and simulated data was remarkable. A similar fit to size distributions, flow rates and stream chemistries was obtained. Details of this study can be found elsewhere (Bond and Ersayin, 2006). The success of this study produced desired progress. Simulation proved to be a reliable tool for plant operators to improve plant performance. Performance improvement ideas were first tested by simulation. Plant-scale testing was considered only in cases where simulation indicated significant improvement. Steady-state simulations were also used in developing better control strategies for iron ore processing plants. The Concentrator Modeling Center continued to improve its conventional simulation capabilities by developing new models and incorporating these into Usim Pac. These included an improved hydrocyclone model for magnetic iron ore processing, a hydrocyclone model capable of simulating slurry temperature effect, and a sump model for operating hydrocyclones with constant pressure drop to mimic plant operation.

Liberation-based modeling and simulation A significant achievement in the long-term objective was accomplished in 2005, when an integrated liberation/size reduction model for magnetite-bearing iron ores was developed and incorporated into the software. This was the crucial step for the Concentrator Modeling Center to move forward to liberationbased modeling. This was followed by efforts in developing new models and modification of existing ones capable of processing liberation-based information. The liberation model describes the degree of liberation for a given size fraction, with 12 liberation classes, ranging from free gangue to free magnetite, with 10% volumetric grade intervals in between the two complete liberation states. Presence of such detailed liberation information requires modifications in models to process this type of data. The 1990s witnessed a boom in the field of liberation measurements and modeling; the most notable achievement was the quantification of liberation from linear intercepts measured on polished sections of sized samples using scanning electron microscopy (Schneider, 1995). This approach was tested on plant data collected from one of the iron ore processing plants on the Iron Range. The model provided good fit to the plant data, but it did not find much use in the industry, mainly due to its inability to differentiate between various iron ore minerals, relying on expensive sample preparation and measurement techniques and the ambiguity of converting linear measurements to three-dimensional liberation data. Existence of an inexpensive alternative might also have been a factor. February 2009 • Vol. 26 No. 1

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Figure 8 — (a) Measured and (b) model-fitted liberation distributions of the ball mill discharge stream.

was adapted to liberation modeling, and its flexibility as a test model was increased by inclusion of calibration parameters for each liberation class. Adaptation of existing conventional models to liberationbased models enabled the Concentrator Modeling Center to carry out complete plant simulation using liberation data. However, these models are considered as preliminary only, needing minor/major refining. As actual measurement data become available, these models are expected to be modified for improving their capability to mimic actual plant operations. This is the future objective for the Concentrator Modeling Center.

function is similar to the one used by Schneider for magnetic separation to describe variation of magnetite recovery with degree of liberation (1) where X(gv) is the model parameter value for liberation class of gv; subscripts 0 and 100 denote free gangue and magnetite, respectively; and a and b are model parameters defining variation of model parameters between the two extreme liberation classes.

Conclusion

Such functions were incorporated into the two models to enable them to process liberation-based data. An existing hydrocyclone model already had the capability to simulate liberation-based data, treating each liberation class as having a different particle density. As noted above, this model was modified for improved simulation of magnetic iron ore processing operation to define differential bypass. This modified version MINERALS & METALLURGICAL PROCESSING

In 1998, iron ore processing plant operators on the Iron Range of northern Minnesota jointly decided that computer simulations should be utilized for improving the performance of their ageing plants. Due to iron ore processing having distinct differences from conventional mineral processing operations, a specialized center was established to develop necessary models

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and eventually carry out plant simulations. As the first step, the Concentrator Modeling Center defined its short-term and long-term objectives. The principal short-term objective was to gain conventional plant simulation capability by developing reliable models for unit operations commonly used in iron ore processing. Conventional simulation relied on a particle size-recovery type relationship for modeling separation processes, whereas the long-term objective was liberation-based simulation requiring an integrated liberation/size reduction model, as well as separation models capable of processing size-liberation data. The Concentrator Modeling Center developed conventional models and successfully demonstrated its capabilities by using simulation to improve the performance of iron ore processing plants on the Iron Range. Essential models were for magnetic separators, hydroseparators and fine screens. Conventional model development included models for separation devices that have potential use in ore processing and improvements in the existing ones. In terms of long-term objectives, an integrated liberation/ size reduction model was developed and incorporated into Usim Pac. This model was validated by using liberation data available in the literature. Conventional models of separation devices were adapted and incorporated into the software to enable them to process liberation-based data. The Concentrator Modeling Center now has the capability to carry out entire iron ore processing plant simulation using liberation-based models. The future target is to refine these models for improved accuracy, while providing a simulation-based service to iron ore processing plants using the existing capabilities of the Concentrator Modeling Center.

of the University of Minnesota for their funding of a number of iron ore simulation-related projects.

References Bond, W.M., and Ersayin, S., 2006, “Improving Taconite Plant Efficiency by Computer Simulation,” The DOE report, DE-FC36-02ID14320. Brochot, S., Touze, S., Ersayin, S., and Wiegel R., 2006, “Modeling and simulation of comminution circuits with USIM PAC,” in Advances in Comminution, S.K. Kawatra, ed., SME, Littleton, Colorado, pp. 495-512. Ersayin, S., 2004, “Low intensity magnetic separator modelling: a pseudo liberation approach,” Mineral Processing and Extractive Metallurgy, Trans. Inst. Min. Metall. C, Vol. 113, pp. C167-C174. Ersayin, S., 2006, “Simulation of taconite processing plants,” Proceedings of the 23rd International Mineral Processing Congress, Istanbul, Sept. 4-8, pp. 1836-1841. Ersayin, S., 2007, “Taconite Concentrator Modeling – the Next Phase,” Technical Report NRRI/TR-2007/20, University of Minnesota, Coleraine Minerals Research Laboratory, Coleraine, Minnesota. Ersayin, S., Bond, W.M., Arola, J., and Kettunen B., 2005, “Simulation of flotation feed pre-classification,” Proceedings of the Centenary of Flotation 2005 Symposium, Aus. IMM, Brisbane, Australia. Gaudin, A.M., 1939, Principles of Mineral Dressing, McGraw Hill, pp 70-91. Pletka, J., 2004, “Development of a Mathematical Model for Fine Screening,” Technical Report NRRI/TR-2004/15, University of Minnesota, Coleraine Minerals Research Laboratory, Coleraine, Minnesota. Plitt, L.R., 1976, “A mathematical model of hydrocyclone classifier,” CIM bulletin, Vol. 69 No. 776, pp. 114-123. Schneider, C.L., 1995, “Measurement and Calculation of Liberation in Continuous Milling Circuits,” PhD thesis, Metallurgical Engineering Department, University of Utah. Wennen, J.E., Nordstrom, W.J., and Murr D.L., 1995, “National Steel Pellet Company’s secondary grinding circuit modifications,” in Comminution Practices, S.K. Kawatra, ed., SME, Littleton, CO. Wiegel, R.L., 1964, “A Mathematical Model for Mineral Liberation by Size Reduction,” M.S. thesis for Chemical Engineering Dept., Carnegie Institute of Technology. Wiegel, R.L., 1974, “Liberation in Magnetite Iron Formations,” Progress Report No. 30, Mineral Resources Research Center, University of Minnesota, pp. 24-83. Wiegel, R.L., 1976, “Simulation of Magnetic Taconite Concentration Processes,” PhD dissertation, University of Queensland, Brisbane, Australia. Wiegel, R.L., 1999, “Fitting Liberation Model Parameters to Davis Tube Test Data,” Coleraine Minerals Research Lab, Technical Report CMRL/TR-99-13. Wiegel, R.L., 2006, “The rationale behind the development of one model describing the size reduction/liberation of ores,” in Advances in Comminution, S.K. Kawatra, ed., SME, Littleton, Colorado, pp. 225-241.

Acknowledgments The authors thank the U.S. Department of Energy, the Iron Ore Cooperative Research program of the Minnesota Department of Natural Resources and the Permanent University Trust Fund

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