This blog elaborates on the nickel ore application in more detail.
Nickel ore processing
Primary sources of mined nickel are  magmatic sulphide deposits with pentlandite as a main ore mineral; and  laterites deposits, where primary minerals are nickeliferous limonite and garnierite. Historically most nickel production was derived from sulphide deposits due to the lower cost of processing, compared to laterite ores. However, today laterite nickel ore became an important second source of nickel production, next to the sulphide deposits.
Mined sulfide ore, after crushing and grinding, is concentrated using flotation and magnetic separation. Subsequently, concentrates are smelted to produce nickel matte, which is further refined to produce pure nickel metal. Mineralogy plays an important role during flotation to separate nickel sulfides from gangue minerals. Apart from usual “trouble-makers”, like talc and other soft minerals, the different crystallographic modifications of pyrrhotite (commonly occurring together with pentlandite) affect downstream processing. Pyrrhotite is an iron-deficient sulfide, which occurs in either hexagonal (hpo) or monoclinic (mpo) form. Mpo and hpo pyrrhotites differ in their magnetic properties and hence behave differently during magnetic separation. Furthermore, hpo is known to be more reactive, which must be taken into account during the flotation.
Processing of laterite nickel ore is more complex. Crushed and ground ore is leached under high pressure using sulphuric acid. After the separation the nickel liquor goes straight to a refinery for pure metal production. Alternatively intermediate nickel hydroxide (or sulphide) is produced that is further processed in a nickel refinery. Similar to the processing of nickel sulphide ore, the mineralogy of nickel laterites defines the efficiency of the leaching step. Soft minerals, (e.g talc, clays) need to be monitored carefully since such minerals can decrease the efficiency of the leaching process. Soft minerals will react with sulphuric acid, thus excluding a portion of that expensive reagent from the process. Furthermore, large concentration of soft minerals may cause blockages and reduce the pumping efficiency.
Accurate and frequent mineralogical monitoring of nickel ore by either x-ray diffraction (XRD) or near-infrared spectroscopy (NIR) helps to increase the efficiency of the concentration and refining and enables corrective measures that increase the lifetime of processing equipment and avoid unexpected maintenance.
The added value of XRD for efficient nickel ore processing
In the previous section we discuss the challenge of handling two modifications of pyrrhotite, hpo and mpo, during sulphide nickel ore concentration. As both modifications have different crystal structure (hexagonal vs. monoclinic) they can be easily identified and quantified using XRD.
Figure 1 zooms on the characteristic diffraction peaks of hpo and mpo pyrrhotite modifications.
Any XRD pattern is a set of diffraction peaks of different intensities, located at certain diffraction angles (2Theta), specific to a certain mineralogical phase. Peak positions enable identification of present phases.
The hexagonal modification (Figure 1, right) has a simpler diffraction pattern and gives a single peak just above 51 °2Theta (using Co radiation). The diffraction pattern of monoclinic pyrrhotite (Figure 1, left) is more complex with two overlapping peaks forming a doublet. In the case of hpo/ mpo mixtures peaks from both modifications overlap each other additionally.
XRD can distinguish between the two modifications of pyrrhotite. Using the relative intensities of the various mineral contributions to the diffraction pattern. Subsequently the different amounts can be quantified using the full-pattern Rietveld refinement method .
The full diffraction pattern along with the full mineralogical quantification for nickel ore concentrate is shown in Figure 2. The sample consists of 50% of mpo with only 3% of hpo and 4% of pentlandite. The hpo/ mpo ratio helps to define a strategy for the following separation steps. Analysis of gangue minerals is as important as characterization of nickel-bearing phases. For example, the sample, analyzed in Figure 2, contains significant amount of chlorite and biotite, known for their detrimental effects during flotation, which should be considered to improve the recovery rate. Quartz and other hard materials should also be monitored to increase the lifetime of crushing and milling equipment.
Additional XRD tools for process monitoring
In the above section, we analyzed the mineralogy of nickel ore concentrate required for the optimization of downstream processing. In addition to the classical quantitative phase analysis, XRD offers several other tools to simplify day-to-day process monitoring. In our following blogs on iron ore and heavy mineral sand processing, we will give an example of cluster analyses [2,3] being used for quick and easy monitoring of different ore grades and mineral separation efficiency. A similar approach can be used to monitor the flotation and separation efficiency at nickel ore processing plant. Mineralogy of tails and waste products can also be controlled using XRD.
Added-value of on-line analysis by near-infrared (NIR) spectroscopy
In our blog about “The value of mineralogical monitoring” near-infrared spectroscopy (NIR) was discussed as a valuable tool for mine exploration and on-line process control. A typical NIR application for nickel ores processing is the real-time monitoring of clays, chlorite and other gangue minerals in the ore on a belt.
To summarize, efficiency of nickel ore processing is directly determined by the mineralogy of the ore. The properties of the minerals, not the elemental content, define the behaviour during separation and concentration. At-line XRD and on-line NIR can be easily implemented into the process flow and ensure fast and accurate mineralogy monitoring at the most sensitive process steps.
-  H.M. Rietveld, A profile refinement method for nuclear and magnetic structures, J. Appl. Cryst. (1969), 2, 65 – 71.
-  H. Lohninger, Teach Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999, ISBN 3-540-14743-8.
-  G.N. Lance, W.T. Williams, A general theory of classification sorting strategies 1., Hierarchical systems, Comp. J. (1966), 9, 373 – 380.
Written by : Dr. Olga Narygina, Posted by : Malvern Panaltyical (www.materials-talk.com)