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Figure 4: Nb,O. recovery between cleaner and rougher concentrates using the QEMSCAN data (after Grammatikopoulos et al. [24}).

Figure 4 Nb,O. recovery between cleaner and rougher concentrates using the QEMSCAN data (after Grammatikopoulos et al. [24}).

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Table 1: Bulk mineralogy of three feed samples. Mineral Chemistry and Elemental Distribution IN UC PY oh. OYTILTLY OILS ALLUUIILS IU) LVOL UE Ue MINE Wileledas the Y-REE silicates for most of the HREE. Given the fact that minerals are multi component element systems, mineralogical analysis is mandatory to determine the elemental distribution. The presence of Th and U is rather critical and will have to be dealt with in the downstream processes. For Li ores, mineral chemistry is important to determine the Li and trace elements, Cs, Rb, Ta, Nb in the spodumene, micas and other associated minerals. The Li content in spodumene might vary among deposits due to geological effects (e.g., alteration). Micas can contain Li which will affect the overall Li distribution in the sample. On the other hand, Fe in the spodumene will dictate its final saleable grade. Furthermore, it has been shown [20] that the natural spodumene crystals have lattice defects, including isomorphous substitution of Fe, Mn, and Cr. Such substitutions affect surface features, such as hydrophobicity of minerals and collector adsorption on mineral surfaces. Micro- flotation experiments, FTIR analysis, and contact angle results showed that higher Fe contents favored the oleate adsorption on spodumene surface [20]. Therefore, proper analytical methods and accurate determination of elemental substitutions in the spodumene lattice can be further evaluated in flotation response of the mineral. A variety of ore minerals commonly display a wide-range of solid solution compositions that vary among and across deposits. Both the major and trace element concentrations are important for exploration and downstream processes. Mineral chemistry is determined by EPMA (major and trace elements such as Zn, Fe, Cu, Ni, As in sulphides) LA-ICP-MS (trace elements such as Ga, Cs, In, and light elements such as Li in micas, spodumene etc.) and D-SIMS (gold concentration in sulphides). For example, REM such as monazite, xenotime, and fluorocarbonates do not have a fixed composition. Thus, Ce, La, Sm, Y, and also Th and U can vary significantly. In addition, commonly occurring phosphate and silicate minerals (e.g., apatite, zircon, fluorite, titanite) can carry significant REE in their matrices or as minute inclusions. Total REE concentrations can reach 7% in titanite and 12% in apatite and are widely variable in zircon (up 10%) in some ores [19]. Therefore, the elemental distribution can be calculated based on the mineral mass and elemental concentrations from the EPMA. The distribution of the REE is critical because it provides insights to the main LREE and HREE carriers. It enables mass balance calculations to predict potential concentrates and tailings losses. Figure 1 illustrates an example of the deportment calculated for a feed sample from a minerals are multi component element systems, mineralogical
Table 1: Bulk mineralogy of three feed samples. Mineral Chemistry and Elemental Distribution IN UC PY oh. OYTILTLY OILS ALLUUIILS IU) LVOL UE Ue MINE Wileledas the Y-REE silicates for most of the HREE. Given the fact that minerals are multi component element systems, mineralogical analysis is mandatory to determine the elemental distribution. The presence of Th and U is rather critical and will have to be dealt with in the downstream processes. For Li ores, mineral chemistry is important to determine the Li and trace elements, Cs, Rb, Ta, Nb in the spodumene, micas and other associated minerals. The Li content in spodumene might vary among deposits due to geological effects (e.g., alteration). Micas can contain Li which will affect the overall Li distribution in the sample. On the other hand, Fe in the spodumene will dictate its final saleable grade. Furthermore, it has been shown [20] that the natural spodumene crystals have lattice defects, including isomorphous substitution of Fe, Mn, and Cr. Such substitutions affect surface features, such as hydrophobicity of minerals and collector adsorption on mineral surfaces. Micro- flotation experiments, FTIR analysis, and contact angle results showed that higher Fe contents favored the oleate adsorption on spodumene surface [20]. Therefore, proper analytical methods and accurate determination of elemental substitutions in the spodumene lattice can be further evaluated in flotation response of the mineral. A variety of ore minerals commonly display a wide-range of solid solution compositions that vary among and across deposits. Both the major and trace element concentrations are important for exploration and downstream processes. Mineral chemistry is determined by EPMA (major and trace elements such as Zn, Fe, Cu, Ni, As in sulphides) LA-ICP-MS (trace elements such as Ga, Cs, In, and light elements such as Li in micas, spodumene etc.) and D-SIMS (gold concentration in sulphides). For example, REM such as monazite, xenotime, and fluorocarbonates do not have a fixed composition. Thus, Ce, La, Sm, Y, and also Th and U can vary significantly. In addition, commonly occurring phosphate and silicate minerals (e.g., apatite, zircon, fluorite, titanite) can carry significant REE in their matrices or as minute inclusions. Total REE concentrations can reach 7% in titanite and 12% in apatite and are widely variable in zircon (up 10%) in some ores [19]. Therefore, the elemental distribution can be calculated based on the mineral mass and elemental concentrations from the EPMA. The distribution of the REE is critical because it provides insights to the main LREE and HREE carriers. It enables mass balance calculations to predict potential concentrates and tailings losses. Figure 1 illustrates an example of the deportment calculated for a feed sample from a minerals are multi component element systems, mineralogical
Figure 1: REE-Y deportment in a feed sample. Mineral chemistry can also determine potential unwanted and deleterious elements that affect the concentrate. Similarly, in base metal deposits, common deleterious and penalty elements do not always occur as discrete minerals that can be removed (e.g., arsenopyrite or bismuthinite) but are part of the crystal lattice of the minerals (e.g., Bi in chalcopyrite, Hg in Cu-sulfosalts, Cd in sphalerite etc.). Identification of minerals that carry the ore elements is impossible to determine with geochemical analyses alone. The understanding of the mineral and metal distribution is critical for beneficiation processes. Nickel in pentlandite, pyrrhotite, and Fe- Mg-silicates vary widely, and the Ni distribution will affect not only the final grades, but the economics of the deposits. For example, Ni in Fe-Mg minerals and pyrrhotite is considered refractory and non-recoverable. of sphalerite calculated for the head sample is ca. 81%, while the remainder of the sphalerite occurs as complex particles (8%), middlings with Fe sulphides (7%), and minor associations with chlorite and amphibole (2%) and other minerals (Figure 2a). The liberation of the mineral increases from 58% to 73% to 80% to 88% and 91% from the +150um to 20pm size fraction. Sphalerite is well exposed; 89% of the sphalerite mass has an exposure surface of greater than 50%, 7% between 20% and 50% exposure, and only 4% below 20% exposure (Figure 2b). Empirically, and assuming ideal flotation conditions and recovery regardless of grain size, the well exposed sphalerite should be recovered in the Zn concentrate, moderately exposed could be recovered but will result in a grade decrease, while poorly exposed particles will likely be rejected. The data clearly indicates that flotation of the sphalerite at this P80 will likely yield low grades and recoveries, or impure rougher concentrates. For example, the association of sphalerite with Fe sulphides remain strong (ca. 9-12%) above 75m and decreases to 6% in the +20um to 1% in the -20um fractions. Therefore, it is certain that some contamination of the concentrate will occur if such middling particles are recovered. Therefore, fine grinding would be required to improve liberation and further recover sphalerite. The additional grinding, either on a feed or a rougher concentrate, will depend on the desired grades and recoveries. Such relations can be further modelled for flotation purposes. However, very fine-grained sphalerite (<10pm) might be lost to the tailings if flotation conditions (e.g., retention times) are mot adjusted. Sphalerite shows negligible variations with regards to the Zn, Mn and Fe variations. Therefore, variations in the final Zn concentrates will be strongly dependent on the liberation and association of the sphalerite.
Figure 1: REE-Y deportment in a feed sample. Mineral chemistry can also determine potential unwanted and deleterious elements that affect the concentrate. Similarly, in base metal deposits, common deleterious and penalty elements do not always occur as discrete minerals that can be removed (e.g., arsenopyrite or bismuthinite) but are part of the crystal lattice of the minerals (e.g., Bi in chalcopyrite, Hg in Cu-sulfosalts, Cd in sphalerite etc.). Identification of minerals that carry the ore elements is impossible to determine with geochemical analyses alone. The understanding of the mineral and metal distribution is critical for beneficiation processes. Nickel in pentlandite, pyrrhotite, and Fe- Mg-silicates vary widely, and the Ni distribution will affect not only the final grades, but the economics of the deposits. For example, Ni in Fe-Mg minerals and pyrrhotite is considered refractory and non-recoverable. of sphalerite calculated for the head sample is ca. 81%, while the remainder of the sphalerite occurs as complex particles (8%), middlings with Fe sulphides (7%), and minor associations with chlorite and amphibole (2%) and other minerals (Figure 2a). The liberation of the mineral increases from 58% to 73% to 80% to 88% and 91% from the +150um to 20pm size fraction. Sphalerite is well exposed; 89% of the sphalerite mass has an exposure surface of greater than 50%, 7% between 20% and 50% exposure, and only 4% below 20% exposure (Figure 2b). Empirically, and assuming ideal flotation conditions and recovery regardless of grain size, the well exposed sphalerite should be recovered in the Zn concentrate, moderately exposed could be recovered but will result in a grade decrease, while poorly exposed particles will likely be rejected. The data clearly indicates that flotation of the sphalerite at this P80 will likely yield low grades and recoveries, or impure rougher concentrates. For example, the association of sphalerite with Fe sulphides remain strong (ca. 9-12%) above 75m and decreases to 6% in the +20um to 1% in the -20um fractions. Therefore, it is certain that some contamination of the concentrate will occur if such middling particles are recovered. Therefore, fine grinding would be required to improve liberation and further recover sphalerite. The additional grinding, either on a feed or a rougher concentrate, will depend on the desired grades and recoveries. Such relations can be further modelled for flotation purposes. However, very fine-grained sphalerite (<10pm) might be lost to the tailings if flotation conditions (e.g., retention times) are mot adjusted. Sphalerite shows negligible variations with regards to the Zn, Mn and Fe variations. Therefore, variations in the final Zn concentrates will be strongly dependent on the liberation and association of the sphalerite.
Figure 3: Grade-recovery curves calculated from the QEMSCAN (QS) and actual metallurgical (MET) values. retention flotation time), flash flotation to recover the coarse and liberated pyrochlore particles, and re-grind the coarser middling particles to increase liberation and surface exposure for flotation. On the other hand, many samples have harmonic relations between the theoretical and metallurgical grades and recoveries. Figure 3 shows that the maximum metallurgical grades are achieved as they plateau those from the QEMSCAN, although the latter shows a range of recovery values (QS-2 and MET-2, respectively). The grain size of pyrochlore is about 10-20um coarser than that in the previous sample, and middling particles contain pyrochlore with higher surface exposure. Thus, they are better recovered from those in the previous sample. Therefore, the mineralogical information can explain the grade and recovery relations, enhance the understanding of liberation and grain size, and assist the flotation process to improve the final niobium recovery. Figure 4 illustrates a positive correlation (R?=0.84) of niobium recovery in the Ro Conc and Final Conc as a function of pyrochlore exposure (>20% exposed particles) for three different feed samples. An overall positive but empirical trend showing a correlation between Nb rougher recovery and exposure of pyrochlore in the overall process was established [24]. This might indicate that most of the poorly liberated/exposed pyrochlore never make it into the final niobium concentrate. Therefore, such relations can explain the reasons of low or high recovery and further enhance the final niobium recovery. MAINETALOglCauy lumMitead grade-recovery dNalyses provide an indication of the theoretical maximum achievable elemental (or mineral) recovery and concentrate grade and are based on individual particle liberation and grade data. The achievement of ideal recovery and concentrate grade in real metallurgical results is nearly impossible, and in the case of a system containing multiple valuable minerals, ideal grade-recovery results of the first valuable mineral may necessitate complete rejection of a second valuable mineral. Figure 3 illustrates the theoretical grade-recoveries generated from the QEMSCAN analysis compared to the actual metallurgical values from a niobium project. Pyrochlore was the main niobate in the deposit. Liberation and grain size of the pyrochlore greatly affected its recovery. Overall, the metallurgical recoveries fall between 60% and 80% of the total Nb in the samples. n cases, where the mineralogically predicted Nb,O, grade is higher, than the one achieved (Figure 3, QS-1 and MET-1, respectively). This is attributed to both mineralogical and process related parameters ie., flotation reagents). For example, pyrochlore is fine-grained <20um) and is lost as entrainment into the tailings. Coarser particles with both poor and good exposure are also lost. The theoretical grade-recovery calculations assume that every single grain, regardless of size, could be recovered. Furthermore, changes must be implemented to recover the fine particles (i.e., higher
Figure 3: Grade-recovery curves calculated from the QEMSCAN (QS) and actual metallurgical (MET) values. retention flotation time), flash flotation to recover the coarse and liberated pyrochlore particles, and re-grind the coarser middling particles to increase liberation and surface exposure for flotation. On the other hand, many samples have harmonic relations between the theoretical and metallurgical grades and recoveries. Figure 3 shows that the maximum metallurgical grades are achieved as they plateau those from the QEMSCAN, although the latter shows a range of recovery values (QS-2 and MET-2, respectively). The grain size of pyrochlore is about 10-20um coarser than that in the previous sample, and middling particles contain pyrochlore with higher surface exposure. Thus, they are better recovered from those in the previous sample. Therefore, the mineralogical information can explain the grade and recovery relations, enhance the understanding of liberation and grain size, and assist the flotation process to improve the final niobium recovery. Figure 4 illustrates a positive correlation (R?=0.84) of niobium recovery in the Ro Conc and Final Conc as a function of pyrochlore exposure (>20% exposed particles) for three different feed samples. An overall positive but empirical trend showing a correlation between Nb rougher recovery and exposure of pyrochlore in the overall process was established [24]. This might indicate that most of the poorly liberated/exposed pyrochlore never make it into the final niobium concentrate. Therefore, such relations can explain the reasons of low or high recovery and further enhance the final niobium recovery. MAINETALOglCauy lumMitead grade-recovery dNalyses provide an indication of the theoretical maximum achievable elemental (or mineral) recovery and concentrate grade and are based on individual particle liberation and grade data. The achievement of ideal recovery and concentrate grade in real metallurgical results is nearly impossible, and in the case of a system containing multiple valuable minerals, ideal grade-recovery results of the first valuable mineral may necessitate complete rejection of a second valuable mineral. Figure 3 illustrates the theoretical grade-recoveries generated from the QEMSCAN analysis compared to the actual metallurgical values from a niobium project. Pyrochlore was the main niobate in the deposit. Liberation and grain size of the pyrochlore greatly affected its recovery. Overall, the metallurgical recoveries fall between 60% and 80% of the total Nb in the samples. n cases, where the mineralogically predicted Nb,O, grade is higher, than the one achieved (Figure 3, QS-1 and MET-1, respectively). This is attributed to both mineralogical and process related parameters ie., flotation reagents). For example, pyrochlore is fine-grained <20um) and is lost as entrainment into the tailings. Coarser particles with both poor and good exposure are also lost. The theoretical grade-recovery calculations assume that every single grain, regardless of size, could be recovered. Furthermore, changes must be implemented to recover the fine particles (i.e., higher
Figure 5: Microscopic (locked, exposed, liberated) and submicroscopic gold distribution in feed samples.
Figure 5: Microscopic (locked, exposed, liberated) and submicroscopic gold distribution in feed samples.
Related topics:
Critical TheoryProcess MineralogyMineral Liberation
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