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Ore characterisation for––and simulation of––primary autogenous grinding
Minerals Engineering 16 (2003) 13–19 This article is also available online at: www.elsevier.com/locate/mineng
Ore characterisation for––and simulation of––primary autogenous grinding R. Hahne a, B.I. P alsson
b,*
, P.O. Samskog
a
a
b
Research and Development, LKAB, S-981 86 Kiruna, Sweden Department of Chemical and Metallurgical Engineering, Division of Mineral Processing, Lule a University of Technology, S-971 87 Lule a, Sweden Received 26 August 2002; accepted 1 November 2002
Abstract In this work, the purpose was to study the impact of variations in feed ore properties on the performance of a primary autogenous grinding circuit by ore characterisation and simulation. Samples were selected to represent various points in the production system; ore faces with different drillability, grinding circuit feed, mill charges and waste rock. The investigation was carried out at the LKAB Kiruna mine in northern Sweden. The result clearly shows that self-breakage occurs ahead of the mill since the ore hardness, or resistance to breakage, increase with the distance from the mining face. Ore from a location, which by the mine is characterised as ‘‘hard to drill’’, has the lowest resistance to breakage, and the surrounding rock is clearly harder than the magnetite ore. Validation of a simulation model for the primary autogenous grinding circuit reveals that the differences between simulated and experimental data are small. Therefore, the model is used to simulate the influence of variations in feed ore characteristics on the circuit performance. The simulations show that the net throughput from the circuit at a coarse–hard feed will be 10% higher compared to a situation when the feed is fine–soft. Moreover, a fine and soft feed results in a coarser particle size distribution of the mill discharge, compared to a coarse and hard material. However, it is the amount of coarse material in the feed, which is the most influential factor. 2003 Elsevier Science Ltd. All rights reserved. Keywords: Iron ores; Autogenous grinding; Simulation; Ore handling; Particle size
1. Introduction In comparison to conventional tumbling mills, i.e., rod- and ball mills, the grinding media in an autogenous mill derive from the feed ore itself (Rowland and Kjos, 1978). Any change in feed ore properties will therefore affect the grinding charge and thus, the breakage mechanisms within the mill. From a size distribution point of view, the optimum feed to an autogenous mill contains enough numbers of large particles that are able to break smaller rocks (Morrell et al., 1994). Changes in feed size distribution are not the only factor that affects the grinding charge. In addition, ore hardness varies and thus, causes significant disturbances. Since the mill performance is changing with feed ore properties, the resulting ground product will also vary (Stange, 1996). In this work, the purpose was to study the impact of *
Corresponding author. Tel.: +46-920-491314; fax: +46-920-97364. E-mail address: [email protected] (B.I. P alsson).
variations in feed ore properties on the performance of a primary autogenous grinding circuit by ore characterisation and simulation. The investigation was carried out at the LKAB Kiruna mine in northern Sweden.
2. Ore characterisation and data collection Mining of the 4 km long magnetite ore body at Kiruna (Fig. 1) takes place between the 775 and 1045 m levels by sublevel caving, where the slice heights are about 27 m. After loading in vertical shafts, which discharge into railway cars at the main transportation level (1045 m), the ore is transported to gyratory crushers. The primary crushed ore is then hoisted to the surface, where coarse non-magnetic surrounding rock is separated from the magnetite by cobbing with belt separators. From the cobbing plant, the ore is conveyed to primary grinding in the fully autogenous mills at Concentrator no. 2.
0892-6875/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 2 - 6 8 7 5 ( 0 2 ) 0 0 3 1 1 - 4
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R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
Fig. 1. The Kiruna mine with sampling locations.
2.1. Locations of sampling To determine the variation in ore hardness, samplings were carried out at four different locations in the production system. • Two locations in the mine where the experience is that the ore is easy- and hard-to-drill respectively. These specimens were taken directly from the walls of the ore body. • A belt cut of the feed to the mill (ore that has been blasted, loaded, primary crushed, cobbed and transported to the mill). • Two mill charges, obtained after crash-stopping the mill, when the throughput in the circuit was high and low respectively. • The surrounding rock to get a reference value compared to the magnetite ore. Variations in feed size distribution were estimated by taking 27 belt cuts (300 kg/cut) of primary crushed and cobbed ore (mill feed) over a period of six months (Hahne et al., 2001). 2.2. Methods of ore characterisation Ore hardness for the samples was determined at the JKMRC by a standard drop weight- and abrasion test (Napier-Munn et al., 1996). In the standard test, particles between 13.2 and 63 mm are used to determine the impact breakage characteristics, and in the abrasion test, particles between 38 and 55 mm are required. The drop weight test results in a breakage index, t10 , which is related to the specific input energy as t10 ¼ A 1 eðbEcsÞ
ð1Þ
where t10 is the percent of breakage product that passes 1=10 of the initial particle size; Ecs is the specific input energy (kWh/t) as calculated from the input energy of the falling weight; A, b are the ore impact breakage parameters. Parameter A is the maximum value of t10 , i.e., the highest level of size reduction from a single impact event. The value of A b, the derivative of Eq. (1) at Ecs ¼ 0, can be used to compare the samples as there is an interaction between these parameters. A high value of A b means that the ore has a low resistant to impact breakage and vice versa. From the value of parameter ta , obtained in the JKMRC abrasion test, the resistance to abrasion for an ore is determined. A high value of ta implies that the ore has a low resistance to abrasion breakage. The 27 samples of primary crushed and cobbed ore were sized on a vibrating screen with apertures 3.3, 5, 10, 15, 30, 50 and 70 mm. 2.3. Results from ore characterisation The resulting values from the drop weight and abrasion tests are presented in Table 1. In Fig. 2, the plots of the resulting values of the parameters illustrates that there is a tendency for the ore samples to increase their resistance to both impact breakage ðA bÞ and abrasion breakage ðta Þ, as the distance from the mining face increases. It should also be noted that the ore sample, which by the mine is characterised as ‘‘hard to drill’’, shows the lowest resistance to breakage, and the surrounding rock is clearly harder than the ore. Based on the results from screening of the belt cuts, it was found that the variation in particle size distribution of the feed is quite large. In Fig. 3, the mean particle size
R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
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Table 1 Results from drop weight and abrasion tests Sample
A
b
Ab
ta
‘‘Hard to drill’’ ‘‘Easy to drill’’ Mill feed Charge-low tph Charge-high tph Surrounding rock
65.4 69.6 74.4 61.7 70.8 74.3
1.84 1.53 1.27 1.41 1.23 0.54
120.3 106.5 94.5 87.0 87.1 40.1
1.16 0.69 0.48 0.31 0.33 0.13
Fig. 2. Comparison of A b and ta between the samples.
Fig. 4. Simulated and experimental particle size distribution of the mill product.
100 90
Fine Mean Coarse
Cum. weight-% passing
80 70 60 50 40 30 20 10 0 1
10
100
Particle size (mm) Fig. 3. Mean particle size distribution and distribution extremes of the feed.
distribution of the 27 samples is shown together with the distribution extremes. It should be noted that the d80 varies between 13 and 80 mm. In turn, it may explain some of the difficulties experienced by the grinding operators. 2.4. Calibration of a primary autogenous mill model From the mill feed sample (cf. Fig. 1), a feed with similar particle size distribution as the mean feed to the mills in Concentrator no. 2 was prepared for a pilot plant
campaign. The pilot mill (£1.5 m) used in the campaign has the same diameter to length ratio as the full-scale primary mill in the concentrator (£6:5 m 5:3 m). The open areas of the discharge grate for the pilot mill and the full-scale mill are 3% and 4% of the mill cross-sectional area, respectively. Based on pilot plant data and ore parameters, determined in the drop weight- and abrasion test, the AG mill model (Leung et al., 1987) in the software JKSimMet 3.0 was used to calibrate a model of the pilot mill. Fig. 4 shows that a good fit of the model was reached, since the difference between simulated and experimental data for the mill product is small. The values of the resulting breakage rates in this calibration were then used in the full-scale simulation model of the primary autogenous mill. 2.5. Simulation of the primary autogenous grinding circuit The aim of this part of the investigation was to simulate the behaviour of the full-scale primary grinding circuit in Concentrator no. 2 (Fig. 5) when the feed ore properties vary. In the circuit, trommel screens are attached to the primary mills. Oversize material from the trommel screens is used as grinding media in secondary pebble mills and undersize material is pumped to a spiral classifier. The coarse material from the classifier is
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Fig. 5. Simplified flowsheet of the primary grinding circuit.
returned to the mill and the fines, i.e., the final product from the circuit, continues to magnetic separators (LIMS). The trommel screen and spiral classifier were modelled as efficiency curves (Napier-Munn et al., 1996) with parameters calibrated to fit plant data. 2 3 d 1 þ bb d50c ðexpðaÞ 1Þ 5 Eoa ¼ C 4 ð2Þ d þ expðaÞ 2 exp ab d50c where Eoa is the proportion (%) of a size d, which reports to the fines from the classifier, and the undersize from the trommel screen respectively; a, b, b are parameters which describes the shape of the efficiency curve; d50c the particle size which has equal chance to report to any of the two products from the spiral classifier and the trommel screen respectively; C is the water split to fines (%). For the spiral classifier and the trommel screen, the resulting values of d50c , a, b and C were calculated to be 0.542, 2.0, 0.2, 98.0 and 2.0, 3.5, 0.2, 98.6, respectively. Based on the results from the ore characterisation (Fig. 3 and Table 1), five cases were simulated: 1. Normal case where the feed has a mean particle size distribution. The values of the ore parameters for the mill feed sample were used, i.e., the ‘‘average’’ hardness of the ore. 2. Hard ore case where the feed has a mean particle size distribution. The values of the ore parameters for one of the charge samples (charge-low tph) were used, i.e., the ‘‘hardest’’ ore according to the test results. 3. Soft ore case where the feed has a mean particle size distribution. The values of the ore parameters for one of the samples from the mine (‘‘hard to drill’’) were used, i.e., the ‘‘softest’’ ore according to the test results. 4. Coarse- and hard ore case where the feed has a coarse particle size distribution. The values of the ore parameters for one of the charge samples (charge-low tph) were used, i.e., the ‘‘hardest’’ ore according to the test results. 5. Fine- and soft ore case where the feed has a fine particle size distribution. The values of the ore parameters for one of the samples from the mine (‘‘hard to
drill’’) were used, i.e., the ‘‘softest’’ ore according to the test results. All of the cases were simulated at the same feed rate.
3. Results Fig. 6 illustrates the simulated particle size distributions of the streams for the normal case (case 1). For comparison, experimental data obtained from ordinary surveys are plotted in the diagrams. Experimental data for the fines from the classifier is the average particle size distribution calculated from 39 screen analyses of this product. The diagram shows that the experimental distribution is in fair agreement with simulated data, except for a small deviation in the median size region. The experimental data for oversize trommel screen is the resulting average particle size distribution calculated from 30 screen analyses of this stream. It can be seen that the simulated distribution is somewhat finer than the experimental, especially in the finest size classes. The average screen analysis of the circulating load (coarse classifier) determined from 13 screen analyses shows a more narrow distribution than the simulated. However, the differences between experimental and simulated data for these three products are not too large and thus, the results were accepted. 3.1. Comparison of the simulated mass balance for the circuit at variations in feed ore properties The simulated mass balance of the circuit is changing when hardness and particle size distribution of the feed ore vary, according to the simulation cases described earlier. The resulting changes are shown in Fig. 7 as the relative change (wt.%) to the normal case (1). When a normal feed (case 1) becomes coarse and hard (case 4), the oversize material from the trommel screen decreases with more than 20%. The influence of this change on the circulating load is small while the final product from the circuit, fines classifier, increases with 6%. The largest amount of oversize material from the trommel screen, more than 15% higher than in the normal case, results when the feed to the mill is fine and
R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
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Fig. 6. Comparison between simulated particle size distributions of the streams for the normal case (continuous line) and experimental data.
Fig. 7. Influence of variations in feed ore hardness and particle size distribution on stream flow rates in the circuit (relative changes (%) to the normal case).
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soft (case 5). This type of feed ore also leads to the lowest net throughput since the fines from the classifier becomes 5% less than in the normal case. When feed ore with a particle size distribution according to the normal case gets somewhat harder (case 2), the amount of oversize material from the trommel screen increases as the fines from the classifier decreases. As this occurs, also the circulating load becomes higher relative to the normal case. The smallest influence on the streams in the circuit results when a feed, with a particle size distribution as in the normal case, becomes soft (case 3). 3.2. Comparison of the simulated particle size distributions of the circuit streams at variations in feed ore properties Maintaining of the target particle size distribution from a grinding circuit is a question of vital importance, since variations in the ground product may affect downstream process steps as flotation, dewatering, pelletising etc. Since variations in feed ore properties influence the ground product from an autogenous mill, comparisons of the resulting particle size distributions of the product streams for the different cases were carried out. The largest differences in particle size distributions of the product streams were obtained when simulation case 4 (coarse–hard feed ore) was compared to case 5 (fine– soft feed ore). The result is logical since these two cases represent the most diverse types of feed ore in the production system. As can be seen in Fig. 8, a fine and soft feed results in a coarser distribution of the mill discharge, than a feed that is coarse and hard. In this situation, the final product will contain more of the finest fractions (<45 lm) than if the feed is hard and the particle size distribution is coarse.
4. Discussion Screen analyses show that there is a large variation in the particle size distribution of the ore fed to the autogenous grinding circuits. The most important factor for this variation is difficult to point out, since the link between the mining face and the concentrator is very complex. Although, the blasted ore may have a relatively constant size distribution, the flow in draw points at the mining face is changing during loading. Thus, the size distribution of the loaded ore will vary. As the ore passes through numerous shafts and bins before it enters the mill, segregation effects are assumed to have an influence on the variation in particle size distribution of the feed ore. The results from the drop weight- and abrasion test indicate that self-breakage occurs ahead of the mill, since the resistance of the samples to both impact- and abrasion breakage are increasing, as the distance of sampling to the mining face is increased. The differences in ore properties may be significant within an ore body (Simkus and Dance, 1998) and therefore, the locations of sampling in order to determine the ore hardness were carefully selected. However, the orebody in this study seems to be fairly homogenous, since the two samples from the mine have a small difference in breakage properties even though the distance between the sample locations was 1.5 km. The simulation results show that changes in ore hardness have less of an influence than particle size. In addition, the feigned drillability of the ore seems not connected to the experimental hardness values. Instead, the particle size distribution is more important for the throughput of the autogenous mill. This was also observed by Bergstedt and F€agremo (1977), who conducted a series of pilot plant tests with varying particle size distributions of the feed. They claim that an ideal RoM-feed to an autogenous mill should have 10–15 wt.% materials coarser than 100 mm. For the investigated circuit, such an ideal feed is represented by the ‘‘coarse’’ distribution, which unfortunately rarely occurs in plant practise.
5. Conclusions
Fig. 8. Influence of feed ore properties on the particle size distributions of the product streams.
Ore hardness in the context of autogenous grinding is not similar to the hardness experienced in mining, especially not in this case, where the breakage properties are fairly constant within the ore body. Changes in the transportation system may cause significant variations in the feed to an autogenous grinding circuit, since selfbreakage occurs when the ore is transported from the mining face to the mill. Whether the method in use is simulation, pilot tests or plant surveys, a systematic characterisation of the feed is essential, when the effect of normal variations in feed ore properties on autogenous
R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
grinding is investigated. This study shows that a lack of coarse particles in the feed ore results in a significant loss of grinding action within the mill and thus, a feed system where it is possible to balance the ratio of coarse to fine particles should be considered. Control of the feedÕs particle size distribution also allows a better opportunity to maintain the target particle size of the ground product. Whether the method in use is simulation, pilot tests or plant surveys, a systematic characterisation of the feed is essential, whenever the effect of normal variations on autogenous grinding is investigated. Acknowledgements The project was co-sponsored by LKAB and The Foundation for Knowledge and Competence Development through an industrial research student grant. References Bergstedt, L., F€ agremo, O., 1977. Some basic factors influencing the use and optimisation of autogenous grinding. AIME Annual Meeting, Atlanta, Georgia, March 1977.
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Hahne, R., P alsson, B.I., Samskog, P-O., 2001. Abrasion tests on iron ore evaluated by multivariate statistics. In: Barrat, D.J., Allan, M.J., Mular, A.L. (Eds.), SAG 2001, Sept. 30–Oct. 3, 2001, Vancouver, Canada, vol. 1. Dept. of Mining and Mineral Process Engineering, Univ. of British Columbia, Vancouver, BC, Canada, pp. I-326–I-338. Leung, K., Morrison, R.D., Whiten, W.J., 1987. An energy based orespecific model for autogenous and semi-autogenous grinding. CopperÕ87, Santiago, Chile. Morrell, S., Finch, W.M., Kojovic, T., Delboni, H., 1994. Modelling and simulation of large diameter autogenous and semiautogenous mills. Proceedings of the Eighth European Symposium on Comminution, Stockholm, Sweden, 17–19 May 1994. pp. 289– 300. Napier-Munn, T.J., Morrel, S., Morrison, R.D., Kojovic, T., 1996. Mineral comminution circuits––their operation and optimisation. JKMRC, Brisbane, Qld., Australia. 413 p. Rowland, Jr., C.A., Kjos, D.M., 1978. Rod and ball mills. In: Mular, A.L., Bhappu, R.B. (Eds.), Mineral Processing Plant Design. SME, AIME, New York, NY, USA, pp. 239–278. Simkus, R., Dance, A., 1998. Tracking hardness and size: measuring and monitoring RoM ore properties at Highland Valley copper. Mine to Mill 1998, Brisbane, Australia, October 1998. pp. 113–119. Stange, W., 1996. The Modelling of Binary Ore Behaviour in FAG/ SAG Milling. In: Mular, A.L., Barrat, D.J., Knight, D.A. (Eds.), SAG 96, Oct. 6–9, 1996, Vancouver, Canada. Dept. of Mining and Mineral Process Engineering, Univ. of British Columbia, Vancouver, BC, Canada, pp. 1063–1080.
Ore characterisation for––and simulation of––primary autogenous grinding R. Hahne a, B.I. P alsson
b,*
, P.O. Samskog
a
a
b
Research and Development, LKAB, S-981 86 Kiruna, Sweden Department of Chemical and Metallurgical Engineering, Division of Mineral Processing, Lule a University of Technology, S-971 87 Lule a, Sweden Received 26 August 2002; accepted 1 November 2002
Abstract In this work, the purpose was to study the impact of variations in feed ore properties on the performance of a primary autogenous grinding circuit by ore characterisation and simulation. Samples were selected to represent various points in the production system; ore faces with different drillability, grinding circuit feed, mill charges and waste rock. The investigation was carried out at the LKAB Kiruna mine in northern Sweden. The result clearly shows that self-breakage occurs ahead of the mill since the ore hardness, or resistance to breakage, increase with the distance from the mining face. Ore from a location, which by the mine is characterised as ‘‘hard to drill’’, has the lowest resistance to breakage, and the surrounding rock is clearly harder than the magnetite ore. Validation of a simulation model for the primary autogenous grinding circuit reveals that the differences between simulated and experimental data are small. Therefore, the model is used to simulate the influence of variations in feed ore characteristics on the circuit performance. The simulations show that the net throughput from the circuit at a coarse–hard feed will be 10% higher compared to a situation when the feed is fine–soft. Moreover, a fine and soft feed results in a coarser particle size distribution of the mill discharge, compared to a coarse and hard material. However, it is the amount of coarse material in the feed, which is the most influential factor. 2003 Elsevier Science Ltd. All rights reserved. Keywords: Iron ores; Autogenous grinding; Simulation; Ore handling; Particle size
1. Introduction In comparison to conventional tumbling mills, i.e., rod- and ball mills, the grinding media in an autogenous mill derive from the feed ore itself (Rowland and Kjos, 1978). Any change in feed ore properties will therefore affect the grinding charge and thus, the breakage mechanisms within the mill. From a size distribution point of view, the optimum feed to an autogenous mill contains enough numbers of large particles that are able to break smaller rocks (Morrell et al., 1994). Changes in feed size distribution are not the only factor that affects the grinding charge. In addition, ore hardness varies and thus, causes significant disturbances. Since the mill performance is changing with feed ore properties, the resulting ground product will also vary (Stange, 1996). In this work, the purpose was to study the impact of *
Corresponding author. Tel.: +46-920-491314; fax: +46-920-97364. E-mail address: [email protected] (B.I. P alsson).
variations in feed ore properties on the performance of a primary autogenous grinding circuit by ore characterisation and simulation. The investigation was carried out at the LKAB Kiruna mine in northern Sweden.
2. Ore characterisation and data collection Mining of the 4 km long magnetite ore body at Kiruna (Fig. 1) takes place between the 775 and 1045 m levels by sublevel caving, where the slice heights are about 27 m. After loading in vertical shafts, which discharge into railway cars at the main transportation level (1045 m), the ore is transported to gyratory crushers. The primary crushed ore is then hoisted to the surface, where coarse non-magnetic surrounding rock is separated from the magnetite by cobbing with belt separators. From the cobbing plant, the ore is conveyed to primary grinding in the fully autogenous mills at Concentrator no. 2.
0892-6875/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 2 - 6 8 7 5 ( 0 2 ) 0 0 3 1 1 - 4
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R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
Fig. 1. The Kiruna mine with sampling locations.
2.1. Locations of sampling To determine the variation in ore hardness, samplings were carried out at four different locations in the production system. • Two locations in the mine where the experience is that the ore is easy- and hard-to-drill respectively. These specimens were taken directly from the walls of the ore body. • A belt cut of the feed to the mill (ore that has been blasted, loaded, primary crushed, cobbed and transported to the mill). • Two mill charges, obtained after crash-stopping the mill, when the throughput in the circuit was high and low respectively. • The surrounding rock to get a reference value compared to the magnetite ore. Variations in feed size distribution were estimated by taking 27 belt cuts (300 kg/cut) of primary crushed and cobbed ore (mill feed) over a period of six months (Hahne et al., 2001). 2.2. Methods of ore characterisation Ore hardness for the samples was determined at the JKMRC by a standard drop weight- and abrasion test (Napier-Munn et al., 1996). In the standard test, particles between 13.2 and 63 mm are used to determine the impact breakage characteristics, and in the abrasion test, particles between 38 and 55 mm are required. The drop weight test results in a breakage index, t10 , which is related to the specific input energy as t10 ¼ A 1 eðbEcsÞ
ð1Þ
where t10 is the percent of breakage product that passes 1=10 of the initial particle size; Ecs is the specific input energy (kWh/t) as calculated from the input energy of the falling weight; A, b are the ore impact breakage parameters. Parameter A is the maximum value of t10 , i.e., the highest level of size reduction from a single impact event. The value of A b, the derivative of Eq. (1) at Ecs ¼ 0, can be used to compare the samples as there is an interaction between these parameters. A high value of A b means that the ore has a low resistant to impact breakage and vice versa. From the value of parameter ta , obtained in the JKMRC abrasion test, the resistance to abrasion for an ore is determined. A high value of ta implies that the ore has a low resistance to abrasion breakage. The 27 samples of primary crushed and cobbed ore were sized on a vibrating screen with apertures 3.3, 5, 10, 15, 30, 50 and 70 mm. 2.3. Results from ore characterisation The resulting values from the drop weight and abrasion tests are presented in Table 1. In Fig. 2, the plots of the resulting values of the parameters illustrates that there is a tendency for the ore samples to increase their resistance to both impact breakage ðA bÞ and abrasion breakage ðta Þ, as the distance from the mining face increases. It should also be noted that the ore sample, which by the mine is characterised as ‘‘hard to drill’’, shows the lowest resistance to breakage, and the surrounding rock is clearly harder than the ore. Based on the results from screening of the belt cuts, it was found that the variation in particle size distribution of the feed is quite large. In Fig. 3, the mean particle size
R. Hahne et al. / Minerals Engineering 16 (2003) 13–19
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Table 1 Results from drop weight and abrasion tests Sample
A
b
Ab
ta
‘‘Hard to drill’’ ‘‘Easy to drill’’ Mill feed Charge-low tph Charge-high tph Surrounding rock
65.4 69.6 74.4 61.7 70.8 74.3
1.84 1.53 1.27 1.41 1.23 0.54
120.3 106.5 94.5 87.0 87.1 40.1
1.16 0.69 0.48 0.31 0.33 0.13
Fig. 2. Comparison of A b and ta between the samples.
Fig. 4. Simulated and experimental particle size distribution of the mill product.
100 90
Fine Mean Coarse
Cum. weight-% passing
80 70 60 50 40 30 20 10 0 1
10
100
Particle size (mm) Fig. 3. Mean particle size distribution and distribution extremes of the feed.
distribution of the 27 samples is shown together with the distribution extremes. It should be noted that the d80 varies between 13 and 80 mm. In turn, it may explain some of the difficulties experienced by the grinding operators. 2.4. Calibration of a primary autogenous mill model From the mill feed sample (cf. Fig. 1), a feed with similar particle size distribution as the mean feed to the mills in Concentrator no. 2 was prepared for a pilot plant
campaign. The pilot mill (£1.5 m) used in the campaign has the same diameter to length ratio as the full-scale primary mill in the concentrator (£6:5 m 5:3 m). The open areas of the discharge grate for the pilot mill and the full-scale mill are 3% and 4% of the mill cross-sectional area, respectively. Based on pilot plant data and ore parameters, determined in the drop weight- and abrasion test, the AG mill model (Leung et al., 1987) in the software JKSimMet 3.0 was used to calibrate a model of the pilot mill. Fig. 4 shows that a good fit of the model was reached, since the difference between simulated and experimental data for the mill product is small. The values of the resulting breakage rates in this calibration were then used in the full-scale simulation model of the primary autogenous mill. 2.5. Simulation of the primary autogenous grinding circuit The aim of this part of the investigation was to simulate the behaviour of the full-scale primary grinding circuit in Concentrator no. 2 (Fig. 5) when the feed ore properties vary. In the circuit, trommel screens are attached to the primary mills. Oversize material from the trommel screens is used as grinding media in secondary pebble mills and undersize material is pumped to a spiral classifier. The coarse material from the classifier is
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Fig. 5. Simplified flowsheet of the primary grinding circuit.
returned to the mill and the fines, i.e., the final product from the circuit, continues to magnetic separators (LIMS). The trommel screen and spiral classifier were modelled as efficiency curves (Napier-Munn et al., 1996) with parameters calibrated to fit plant data. 2 3 d 1 þ bb d50c ðexpðaÞ 1Þ 5 Eoa ¼ C 4 ð2Þ d þ expðaÞ 2 exp ab d50c where Eoa is the proportion (%) of a size d, which reports to the fines from the classifier, and the undersize from the trommel screen respectively; a, b, b are parameters which describes the shape of the efficiency curve; d50c the particle size which has equal chance to report to any of the two products from the spiral classifier and the trommel screen respectively; C is the water split to fines (%). For the spiral classifier and the trommel screen, the resulting values of d50c , a, b and C were calculated to be 0.542, 2.0, 0.2, 98.0 and 2.0, 3.5, 0.2, 98.6, respectively. Based on the results from the ore characterisation (Fig. 3 and Table 1), five cases were simulated: 1. Normal case where the feed has a mean particle size distribution. The values of the ore parameters for the mill feed sample were used, i.e., the ‘‘average’’ hardness of the ore. 2. Hard ore case where the feed has a mean particle size distribution. The values of the ore parameters for one of the charge samples (charge-low tph) were used, i.e., the ‘‘hardest’’ ore according to the test results. 3. Soft ore case where the feed has a mean particle size distribution. The values of the ore parameters for one of the samples from the mine (‘‘hard to drill’’) were used, i.e., the ‘‘softest’’ ore according to the test results. 4. Coarse- and hard ore case where the feed has a coarse particle size distribution. The values of the ore parameters for one of the charge samples (charge-low tph) were used, i.e., the ‘‘hardest’’ ore according to the test results. 5. Fine- and soft ore case where the feed has a fine particle size distribution. The values of the ore parameters for one of the samples from the mine (‘‘hard to
drill’’) were used, i.e., the ‘‘softest’’ ore according to the test results. All of the cases were simulated at the same feed rate.
3. Results Fig. 6 illustrates the simulated particle size distributions of the streams for the normal case (case 1). For comparison, experimental data obtained from ordinary surveys are plotted in the diagrams. Experimental data for the fines from the classifier is the average particle size distribution calculated from 39 screen analyses of this product. The diagram shows that the experimental distribution is in fair agreement with simulated data, except for a small deviation in the median size region. The experimental data for oversize trommel screen is the resulting average particle size distribution calculated from 30 screen analyses of this stream. It can be seen that the simulated distribution is somewhat finer than the experimental, especially in the finest size classes. The average screen analysis of the circulating load (coarse classifier) determined from 13 screen analyses shows a more narrow distribution than the simulated. However, the differences between experimental and simulated data for these three products are not too large and thus, the results were accepted. 3.1. Comparison of the simulated mass balance for the circuit at variations in feed ore properties The simulated mass balance of the circuit is changing when hardness and particle size distribution of the feed ore vary, according to the simulation cases described earlier. The resulting changes are shown in Fig. 7 as the relative change (wt.%) to the normal case (1). When a normal feed (case 1) becomes coarse and hard (case 4), the oversize material from the trommel screen decreases with more than 20%. The influence of this change on the circulating load is small while the final product from the circuit, fines classifier, increases with 6%. The largest amount of oversize material from the trommel screen, more than 15% higher than in the normal case, results when the feed to the mill is fine and
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Fig. 6. Comparison between simulated particle size distributions of the streams for the normal case (continuous line) and experimental data.
Fig. 7. Influence of variations in feed ore hardness and particle size distribution on stream flow rates in the circuit (relative changes (%) to the normal case).
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soft (case 5). This type of feed ore also leads to the lowest net throughput since the fines from the classifier becomes 5% less than in the normal case. When feed ore with a particle size distribution according to the normal case gets somewhat harder (case 2), the amount of oversize material from the trommel screen increases as the fines from the classifier decreases. As this occurs, also the circulating load becomes higher relative to the normal case. The smallest influence on the streams in the circuit results when a feed, with a particle size distribution as in the normal case, becomes soft (case 3). 3.2. Comparison of the simulated particle size distributions of the circuit streams at variations in feed ore properties Maintaining of the target particle size distribution from a grinding circuit is a question of vital importance, since variations in the ground product may affect downstream process steps as flotation, dewatering, pelletising etc. Since variations in feed ore properties influence the ground product from an autogenous mill, comparisons of the resulting particle size distributions of the product streams for the different cases were carried out. The largest differences in particle size distributions of the product streams were obtained when simulation case 4 (coarse–hard feed ore) was compared to case 5 (fine– soft feed ore). The result is logical since these two cases represent the most diverse types of feed ore in the production system. As can be seen in Fig. 8, a fine and soft feed results in a coarser distribution of the mill discharge, than a feed that is coarse and hard. In this situation, the final product will contain more of the finest fractions (<45 lm) than if the feed is hard and the particle size distribution is coarse.
4. Discussion Screen analyses show that there is a large variation in the particle size distribution of the ore fed to the autogenous grinding circuits. The most important factor for this variation is difficult to point out, since the link between the mining face and the concentrator is very complex. Although, the blasted ore may have a relatively constant size distribution, the flow in draw points at the mining face is changing during loading. Thus, the size distribution of the loaded ore will vary. As the ore passes through numerous shafts and bins before it enters the mill, segregation effects are assumed to have an influence on the variation in particle size distribution of the feed ore. The results from the drop weight- and abrasion test indicate that self-breakage occurs ahead of the mill, since the resistance of the samples to both impact- and abrasion breakage are increasing, as the distance of sampling to the mining face is increased. The differences in ore properties may be significant within an ore body (Simkus and Dance, 1998) and therefore, the locations of sampling in order to determine the ore hardness were carefully selected. However, the orebody in this study seems to be fairly homogenous, since the two samples from the mine have a small difference in breakage properties even though the distance between the sample locations was 1.5 km. The simulation results show that changes in ore hardness have less of an influence than particle size. In addition, the feigned drillability of the ore seems not connected to the experimental hardness values. Instead, the particle size distribution is more important for the throughput of the autogenous mill. This was also observed by Bergstedt and F€agremo (1977), who conducted a series of pilot plant tests with varying particle size distributions of the feed. They claim that an ideal RoM-feed to an autogenous mill should have 10–15 wt.% materials coarser than 100 mm. For the investigated circuit, such an ideal feed is represented by the ‘‘coarse’’ distribution, which unfortunately rarely occurs in plant practise.
5. Conclusions
Fig. 8. Influence of feed ore properties on the particle size distributions of the product streams.
Ore hardness in the context of autogenous grinding is not similar to the hardness experienced in mining, especially not in this case, where the breakage properties are fairly constant within the ore body. Changes in the transportation system may cause significant variations in the feed to an autogenous grinding circuit, since selfbreakage occurs when the ore is transported from the mining face to the mill. Whether the method in use is simulation, pilot tests or plant surveys, a systematic characterisation of the feed is essential, when the effect of normal variations in feed ore properties on autogenous
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grinding is investigated. This study shows that a lack of coarse particles in the feed ore results in a significant loss of grinding action within the mill and thus, a feed system where it is possible to balance the ratio of coarse to fine particles should be considered. Control of the feedÕs particle size distribution also allows a better opportunity to maintain the target particle size of the ground product. Whether the method in use is simulation, pilot tests or plant surveys, a systematic characterisation of the feed is essential, whenever the effect of normal variations on autogenous grinding is investigated. Acknowledgements The project was co-sponsored by LKAB and The Foundation for Knowledge and Competence Development through an industrial research student grant. References Bergstedt, L., F€ agremo, O., 1977. Some basic factors influencing the use and optimisation of autogenous grinding. AIME Annual Meeting, Atlanta, Georgia, March 1977.
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Hahne, R., P alsson, B.I., Samskog, P-O., 2001. Abrasion tests on iron ore evaluated by multivariate statistics. In: Barrat, D.J., Allan, M.J., Mular, A.L. (Eds.), SAG 2001, Sept. 30–Oct. 3, 2001, Vancouver, Canada, vol. 1. Dept. of Mining and Mineral Process Engineering, Univ. of British Columbia, Vancouver, BC, Canada, pp. I-326–I-338. Leung, K., Morrison, R.D., Whiten, W.J., 1987. An energy based orespecific model for autogenous and semi-autogenous grinding. CopperÕ87, Santiago, Chile. Morrell, S., Finch, W.M., Kojovic, T., Delboni, H., 1994. Modelling and simulation of large diameter autogenous and semiautogenous mills. Proceedings of the Eighth European Symposium on Comminution, Stockholm, Sweden, 17–19 May 1994. pp. 289– 300. Napier-Munn, T.J., Morrel, S., Morrison, R.D., Kojovic, T., 1996. Mineral comminution circuits––their operation and optimisation. JKMRC, Brisbane, Qld., Australia. 413 p. Rowland, Jr., C.A., Kjos, D.M., 1978. Rod and ball mills. In: Mular, A.L., Bhappu, R.B. (Eds.), Mineral Processing Plant Design. SME, AIME, New York, NY, USA, pp. 239–278. Simkus, R., Dance, A., 1998. Tracking hardness and size: measuring and monitoring RoM ore properties at Highland Valley copper. Mine to Mill 1998, Brisbane, Australia, October 1998. pp. 113–119. Stange, W., 1996. The Modelling of Binary Ore Behaviour in FAG/ SAG Milling. In: Mular, A.L., Barrat, D.J., Knight, D.A. (Eds.), SAG 96, Oct. 6–9, 1996, Vancouver, Canada. Dept. of Mining and Mineral Process Engineering, Univ. of British Columbia, Vancouver, BC, Canada, pp. 1063–1080.