THE VALUE OF MINERALOGICAL ANALYSES FOR BASE METAL MINING AND BENEFICIATION – LITHIUM ORES

Increasing demand for effective energy storage used in personal devices, electrical cars, renewable energy storage facilities, boosted the global demand for lithium.

Lithium-ion batteries are not the only industrial use of lithium (e.g. catalysts, lubricants, heat-resistant glass and ceramics, alloys for aerospace), but undoubtedly, the most rapidly growing one.

Two main lithium production sources are hard-rock lithium deposits and lithium brines.

The later are lithium salts-enriched salines in the underground, which accumulate under the surface of dried lakebeds. A great advantage of lithium extraction from brines is lower production cost, which is a natural evaporation process, followed by further processing in a chemical plant. A natural evaporation assumes a low cost, but at the same time it is climate and weather dependent. The evaporation step can take up to a year and even longer, depending on the conditions. Only few regions in the world have economically valuably brine deposits (Chile, Argentina, Bolivia and China).

Lithium hard-rock deposits occur across the globe. The recovery process, although more costly, is not climate-dependent, which makes such deposits a more stable source of lithium. The three main countries producing lithium from hard-rock deposits are Canada, China and Australia. The latter is the leading producer with 18.7 million tons produced in 2018.

Lithium production from hard-rock deposits

Economically valuable lithium containing minerals that occur in hard-rock granite pegmatites deposits are spodumene, apatite, lepidolite, tourmaline and amblygonite of which spodumene is the most common lithium-bearing mineral.

Lithium ore is extracted either using open-pit or underground mining. The further processing can be broken down into the following key steps:

  1. crushing of the ore,
  2. concentration by froth floatation,
  3. thermal treatment in a rotary calcining kiln to convert α-spodumene into its β-modification.

Flotation and calcination efficiency are directly determined by the ore mineralogy. Therefore, frequent, fast and accurate mineralogy monitoring is essential for the optimized recovery rate and stable product quality.

In the introduction blog, we established that X-ray diffraction (XRD) is a fast, versatile and accurate mineralogy probe, which can be easily implemented in the process flow at mine operation and processing plant. In the following case study, we discuss the added value of XRD for the beneficiation of hard-rock lithium ore using samples from an operating lithium mine.

Monitoring of lithium ore feed, concentrates and tales by XRD

In the case study we used 14 samples from a lithium mine, representing the key steps in the lithium ore recovery: ten raw ore samples, one αspodumen concentrate and one tailing after the froth flotation step, one β-spodumene concentrate and one residue of the thermal treatment process.

All samples were prepared as pressed pellets and measured on an Aeris Minerals benchtop diffractometer with a scan time of 10 minutes, followed by an automatic quantification of the mineral phases.

Figure 1. Quantitative phase analysis of lithium ore sample #1 using Aeris Minerals tabletop diffractometer. Measurement time is 10 minutes

Figure 1 shows an example of a full-pattern XRD analysis of a hard-rock lithium ore.

Any XRD pattern is a set of diffraction peaks of different intensities, located at certain diffraction angles (2θ), specific to a certain mineralogical phase. Peak positions enable identification of present phases. The relative intensities of the peaks in the XRD pattern allow the determination of the relative amount of each present mineral. The method is called Rietveld refinement[1].

In the example shown in Figure 1, the main minerals present are spodumene LiAl(SiO3)2, quartz SiO2, albite NaAlSi3O8, anorthite CaAl2Si2O8, minor amounts of lepidolite K(Li,Al)3(Al,Si,Rb)4O10(F,OH)2 and traces of orthoclase KAlSi3Oand beryl Be3Al2(SiO3)6. The rest of the raw ore samples show similar mineralogy, some samples contain additionally traces of tourmaline (elbaite) Na(Li1.5Al1.5)Al6Si6O18(BO3)3(OH)4 and analcime NaAlSi2O6·H2O.

Figure 2. Mineralogical composition of 14 raw and processed lithium samples

The mineral quantification of the whole sample set is shown in Figure 2. Even though the mineralogy of the ore samples is consistent, the relative mineral quantities differ from sample to sample.

This information is extremely important for blending the optimal mixture for consistent input towards the beneficiation plant for further froth flotation.

Samples 11 and 12 in Figure 2 represent spodumene concentrate and tailings after the flotation step. 90% of the concentrate sample (sample 11) is spodumene with the minor amounts of quartz, albite, anorthite and traces of lepidolite, beryl, orthoclase and analcime. The main fraction of the gangue minerals went to the corresponding tailing sample (sample 12), which primarily consists of albite, quartz and anorthite. The mineralogical composition of concentrate and tailing after the flotation step indicates a high efficiency of the flotation process.

The remaining samples in Figure 2, sample 13 and 14, represent β-spodumene and corresponding residue from the calcination of α-spodumene concentrate (sample 11). The mineralogical composition of both, concentrate (sample 13) and residue (sample14), shows a reasonable efficiency of the calcination step. Over 92% of concentrate is the desired β-spodumene phase with minor amounts of anorthite and quartz. The corresponding tailing mainly consists of analcime, however, there are over 7% remaining β-spodumene.

The mineralogical analyses showed that the conversion of α- to β-spodumene during calcination was effective, however, the separation process should be further optimized to increase the yield of β-spodumene in the final concentrate.

Additional XRD-tools for process monitoring

In the above section we analyzed the mineralogy of 14 lithium ore samples, concentrates and tailings and residues. The results identified the weak spot in the process, which can be further improved. However, accurate full mineralogical analysis is not the only process monitoring tool, which XRD can offer.

Any deviation from a normal data spread within a cluster immediately a signals possible issues in the production process.

To summarize, hard-rock lithium recovery is a complex process, heavily dependent on the ore mineralogy. A flexible, fast and accurate mineralogical probe, like X-ray diffraction, greatly improves the efficiency throughout the whole process, provides tools for quick and easy monitoring of the process stability and gives necessary insights for counteractive measures when production issues arise.

 

Written by: Dr. Olga Narygina, Posted by: Malvern Panaltyical (www.materials-talks.com)

WITHOUT A DOUBT: THE FUTURE IS ADDITIVE

The role of material characterization in the next manufacturing revolution

Ever heard of the term ‘prosumer’? If you haven’t yet, chances are you will soon – perhaps you even already are one. Through customization, design involvement, or even manufacturing entire products, prosumerism blurs the lines between production and consumption. Think of personalized sports kits or print-to-order planners – these are prosumerism in action. And now, with the rise of 3D printing, consumers can create whole objects from the comfort of their own homes.

In fact, 3D printing is part of a wider process called additive manufacturing (AM). By connecting materials additively, usually layer-on-layer, AM can deliver greater sustainability, flexibility, and process efficiency. To start with, as an additive process, it generates less waste than its subtractive counterparts. It can also produce more complex parts than traditional manufacturing methods, while its potential for small-batch production enables greater customization and shorter product cycles.

The next manufacturing revolution?

But there’s more. As well as being advantageous to manufacturers, AM could be about to spark a revolution in our entire approach to manufacturing. For instance, by enabling the production of highly complex parts in just a single piece, AM opens up a whole new world of design possibilities. As it becomes more widely adopted, designing with these possibilities in mind will become the norm.

Even more excitingly, AM could democratize the design process. As tools like 3D printers and CAD software become less expensive, design and manufacturing will become more accessible to a greater range of consumers and businesses. This shift can already be seen in the ‘maker movement’ and prosumerism. And with technological developments, the quality and scalability of these products will only increase.

One crucial component

Nevertheless, for this AM revolution to become a reality, we’ll need one important thing: strong materials characterization. Because AM processes normally have fixed parameters, inconsistent material properties result in inconsistent finished component properties. And for AM to become the new manufacturing standard, it needs to deliver goods of consistently high quality. Indeed, lack of standardization, limited material selection, and weak mechanical properties are currently some of the biggest barriers to wider adoption of AM.

Material characterization helps solve these issues. How? By using it to analyze their raw materials, manufacturers can then optimize these materials for the specific AM process used. In this way, material characterization helps prevent common issues such as cracking, distortion, weakness, and poor surface finishes. This is especially important for metal powder bed AM, where a particularly large number of factors affect final component quality.

Solutions for the future

To support manufacturers with material characterization, we offer several solutions. For instance, to enable accurate particle size and shape analysis, we provide a range of laser diffraction and automated imaging systems. These support manufacturers to ensure a spherical shape and good size distribution in their metal powder particles. In turn, this helps them achieve strong flowability, powder bed density, melt energy, and surface finishes in the final component.

We also offer X-ray fluorescence (XRF) solutions to facilitate chemical composition analysis. With this floor-standing and benchtop XRF systems, manufacturers can analyze the elemental composition of their alloys and check for impurities. Several manufacturers are already using our solutions for this, helping them to avoid issues such as cracking.

Last but not least, our X-ray diffraction (XRD) solutions enable accurate analysis of microstructure: the crystalline phases and grain structures within powder particles. Our XRD systems support manufacturers in analyzing and optimizing the microstructure of their powder. In turn, this lets them deliver strong mechanical properties in their final manufactured components – such as strength, fatigue response, and surface finish.

In short, with these solutions, manufacturers can ensure that their AM-manufactured parts perform as effectively as possible. In this way, we’re helping drive the wider adoption of AM across numerous industries, applications, and regions. A new world of prosumerism, efficient design, and high-performance AM products is just around the corner!

Written by: John Duffy, Posted by: Malvern Panalytical (www.materials-talks.com)

EFFICIENT IRON SINTERING PROCESS CONTROL

In this blog we will discuss the added value of mineralogy monitoring at the next step of the ore-to-metal process, iron ore sintering.

Sintering is one of the iron ore post-processing steps to prepare iron ore fines for a blast furnace

Feed for a sinter plant consists of iron fines, coke, and flux (eg limestone). The feed is placed on a sintering bed, where thermal agglomeration (1300-1480 °C) takes place to produce clustered lumps, aka iron sinter (5-20 mm in size).

At a common sinter plant, the following sinter quality parameters need to be controlled:

  • Basicity, CaO/SiO2 – FeO (Fe2+)
  • Sinter strength index, SSI
  • Tumbler index, TI
  • Reducibility index, RI
  • Reduction degradation index, RDI
  • Low-temperature degradation, LTD

All the above parameters are linked to the properties of the mineral phases, comprising iron sinter. The main sinter phases can be divided into iron oxides and silicates, which are gluing the iron oxides together:

Iron oxides: Silicates of SFCAs:
Hematite: Fe3+2O3 Larnite: Ca2SiO4
Magnetite: Fe3+2+3O4 SFCA-a: Silica-ferrites of calcium aluminium, Fe2+ only
Wuestite: Fe2+O SFCA-b: Silica-ferrites of calcium aluminium, Fe3+, some Fe2+

X-ray diffraction (XRD) is a fast, versatile, and accurate mineralogy probe, which can be easily implemented in the process flow at mine operation and processing plant. Since all sinter quality parameters are linked to its mineralogy, XRD is a unique tool, providing in a minimum of time a comprehensive assessment of the sinter quality parameters. In the following case study, we discuss the added value of XRD for the process control at a sinter plant.

Accurate analysis of sinter mineralogy using XRD

For this case study, we used 49 sinter samples from a producing sinter plant. All samples were prepared as pressed pellets and were measured on Aeris Metals benchtop diffractometer with a scan time of 5 minutes, followed by an automatic quantitative phase analysis.

Figure 1 shows an example of full-pattern XRD analysis of one of the sinter samples, used in the study.

Figure 1. Typical result of XRD analyses of iron sinter using Aeris Metals. 5 minutes scans followed by the automatic phase quantification.

Any XRD pattern is a set of diffraction peaks of different intensities, located at certain diffraction angles (2q), specific to a certain mineralogical phase. Peak positions enable identification of present phases. The relative intensities of each mineral contribution to the XRD pattern allows us to quantify the relative amount of each present mineral using full-pattern Rietveld refinement [1].

In the example in Figure 1, the sinter sample primarily consists of hematite and magnetite with 23% of crystalline calcio-silicates (so-called SFCA phases) and 21% of the amorphous phase.

Comparing the XRD result of the sample in Figure 1 with the rest of the samples in the set (Figure 2), we see that mineralogy doesn’t change; however, the relative phase amounts differ from sample to sample.

Figure 2. Combined result of quantitative phase analysis of the entire iron sinter sample set.

Using the stoichiometry of the present mineral phases, the sinter FeO content, the most important sinter property since it is connected to the energy consumption in the blast furnace, can directly be extracted from the mineralogical composition (Figure 1, 2).

FeO quantification is done simultaneously with the quantitative phase analysis and is reported along with the phase composition.

A comparison of FeO values, as extracted from the XRD data, with the reference values, obtained by wet chemistry, is shown in Figure 3. We see a very good agreement between the XRD results and the given reference values. Every red data point on this graph takes 10 minutes on average, including automatic sample preparation, 5 minutes scan using Aeris Metals followed by an automatic quantitative analysis. Compared to a few hours of manual sample analyses using hazardous chemicals, the XRD is a fast and safe alternative. Would you still want to use wet chemistry for routine assessment of sinter FeO content?

Figure 3. Comparison of sinter FeO content as obtained from XRD (red circles) with reference values, obtained by wet chemistry (black diamonds).

Getting more from the same XRD data set

In the previous section we established that XRD is a fast and accurate tool for quantitative analysis of sinter mineralogy and determination of sinter FeO content. However, there are other sinter process parameters, which are dependent on the sinter mineralogy (e.g. strength, degradation index, etc.). Can we extract them from the same XRD data set?

The answer is “Yes”, we can by using a modern statistical method, Partial Least Square Regression, (PLSR) [2]. In short, a statistical model is built using a set of reference samples, for which the value of a process parameter (e.g. RDI, SSI, LTD) is known. Afterward, this model is used to predict the process parameter(s) directly from an XRD pattern. This eliminates the need for additional time-intense physical tests and increases the frequency of monitoring.

We applied the PLSR approach to the sinter sample set used in the case study. Part of the set, for which we had reference values of process parameters, was used to build (calibrate) the corresponding PLSR models. Afterward, the obtained PLSR models were used to predict the process parameters directly from the XRD data. Figure 4 summarizes the results for the studied sinter sample set.

Figure 4. results for the studied sinter sample set

Using PLSR approach we obtained sinter strength index SSI, reduction degradation index, RDI, and basicity. The rest of the sinter process parameters, e.g. low-temperature degradation index, tumbler index, reducibility index) can be obtained using the same approach.

Note that both, automatic full-pattern mineral quantification using the Rietveld method and PLSR analysis, can be simultaneously performed on the same XRD pattern. Thus, a single, 5-minute XRD measurement on the Aeris Metals benchtop diffractometer provides the full sinter phase composition along with all-important process parameters.

To summarize, most of the iron sinter quality parameters (aka process parameters) are determined by the properties of the mineral phases. XRD is an indispensable tool for fast, accurate, and tailored mineralogical analysis, which can be easily implemented into the process flow. XRD can be used not only for the fast-quantitative assessment of the full mineralogical composition of iron sinter and its FeO content. New statistical methods (e.g. partial least square regression) opened up new possibilities and enabled the extraction of relevant process parameters directly from the same diffraction data set, eliminating the need for additional time-consuming, costly tests.

 

Written by: Dr. Olga Narygina, Posted by: Malvern Panalytical (www.materials-talk.com)

OPTIMIZING THE PERFORMANCE OF TITANIUM DIOXIDE AND OTHER PIGMENTS FOR YOUR PAINTS, PIGMENTS, PLASTICS – Q&A

Characterization of TiO2 within the paint and pigment industry is a critical component of paint development. X-ray diffraction and DLS are important techniques as they identify the material and various features like particle size, shape, and size distribution. The physical properties of the final product depend on these features.

Our specialists from Malvern Panalytical shared tips that manufacturers can engage to fully optimize the use of their pigments for high-performance products.

Can we identify the surface treatment of TiO2 pigments with XRD?

If the surface capping entities are crystalline then they can be observed in X-ray diffraction (XRD) pattern. Moreover capping changes the size, size distribution and shape of particles which can be observed in Small Angle X-ray Scattering (SAXS) experiments which can now be done on a multipurpose Empyrean diffractometer.

Why the peaks in XRD become broader with decrease in particle size?

A diffraction peak originates from volume which is exposed to X-rays. The broadness of a diffraction peak is governed by two properties: size and strain of crystallites within this diffraction volume. When crystal grow to smaller sizes and the effect of defects in producing strained crystals is pronounced which results in producing broad peak in a diffraction pattern. But broadness is only partly due to strain. Size effects originates since distribution of coherently scattering domain (which produces diffraction intensity) increases with smaller sizes. For amorphous material it is almost truly randomly oriented, hence the peak is broadest.

TiO2 is UV light active ……How could we use in paints and it may damage the paints?

The UV active TiO2 produces radicals which break down the adjoining chemicals especially polymers. This leads to weathering of paint.

For agglomeration tendencies of pigments in actual paint products, how can we use DLS and XRD to assess this?

One can study agglomeration by taking successive SAXS scans which will show change in shape, size and size distribution of particles within the sample.

How can we distinguish nanoparticles and amorphous using SAXS

SAXS is sensitive to “particle” size, shape and size distribution only, it is not sensitive to crystalline nature of material. XRD on other hand is sensitive to crystalline nature, hence one must perform XRD scan of sample to distinguish between crystalline nanoparticles and amorphous particles.

How about the hematite based pigment. what is the advantage and disadvantage comparing with TiO2?

Hematite pigment is a non-white colored pigment that can be identified and quantified using XRD. With Cu K-alpha radiation, it produces fluorescence. This makes an higher background within an XRD scan and thus make it difficult for phase quantification. Usage of multi-core optics in combination with 1Der detector solves the problem of fluorescence and hence makes it possible to identify and quantify hematite in extremely low quantities.

Written by: Tamal Mukherjee, Postes by: Malvern Panalytical (www.materials-talk.com)