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)

NEW Ferroalloy CRMs

Our partner ARMI | MBH team is continuing their work to develop more new products to ensure that they can provide the products you need for your analytical testing.

Ferroalloys are binary or ternary alloys containing iron alloyed with one or two additional elements. While often produced as an intermediate product for iron and steel manufacturing, some ferroalloys are used as final products; for instance, ferrosilicon is used as a heavy media in gravity separation of diamonds during kimberlite mining.

Ferroalloys are typically produced in furnaces by reducing oxides using carbon while being mixed with iron. The chunky material produced in the furnace is then crushed and milled into a powder and homogenized. The powdered ferroalloy can then be used as is, or mixed into a melt to alter or control the alloy composition.

In addition to the primary alloying elements, ferroalloys may contain additional elements at minor or trace levels, depending on purity and specifications. Careful monitoring of major elements is necessary to control the final ratio of alloying elements, monitoring of other elements is necessary to maintain the desired purity threshold and to ensure the final product meets specifications. In addition to being certified for the major elements, these four ferroalloys are certified for more minor and trace element values than any comparable CRMs available.

ARMI MBH has released four new ARMI ferroalloy CRMs in powder form in approximately 100g bottles to their portfolio.

One of the most common ferroalloys is, high-carbon ferrochrome which is used almost exclusively in the production of stainless steel and high chromium steels. We recently released high-carbon ferrochrome, IARM-FCrP-20, with Cr certified at 68.8 wt%, Fe at 20.6 %, and C at 8.6%. It is also certified for Co, Cu, Mg, Mn, N, Ni, O, P, S, and Si, with informational values for 20 other elements.

Ferrosilicon is utilized for many uses including the manufacture of cast iron, other ferroalloys and silicon for corrosion-resistant and high-temperature ferrous silicon alloys. Our newly released ferrosilicon, IARM-FSiP-20, includes Si certified at 77.0 wt% and Fe at 21.8%. It is also certified for Al, C, Ca, Co, Cr, Cu, Mg, Mn, Mo, Nb, Ni, P, Ti, W, Zn, and Zr with informational values provided for 21 other elements.

To deoxidize steel, ferromanganese is often used which helps to reduce issues with tensile strength, ductility and toughness caused during the production process. Ferromanganese, IARM-FMn-20, was recently added to our portfolio as a powder with Mn certified at 79.7 wt%., Fe at 16.6%, and C at 1.13%. It also has certified values for B, Co, Cr, Cu, Mn, N, Ni, P, S, and Si. Informational values are provided for 22 more elements.

Lastly, ARMI MBH also added ferroboron powder, IARM-FBP-20, to our portfolio with B certified at 18.4 wt% and Fe at 77.0%. It also has certified values for Al, C, Ca, Cr, Cu, Mn, Mo, N, Ni, S, Si, Sn, Ti, V, W, and Zr. Informational values are provided for 18 other elements, including Fe.

 

Written by: James Haddad PhD – Posted by: ARMI MBH (www.armi.com)

THE USE OF XRD FOR ADDITIVE MANUFACTURING OF METALS

Many manufacturers are now looking at metal powder-based additive manufacturing (AM) as a realistic alternative to more traditional manufacturing processes such as casting, forging, and machining. While AM can be expensive it can deliver huge advantages in sustainability, efficiency, and flexibility and requires less raw material consumption than subtractive processes. Parts produced through AM can also be made lighter and more complex, delivering efficiency during use.

A major difference in traditional metal fabrication processes compared with AM processes is the heating-cooling regimes involved. For AM processes, such as selective laser melting (SLM) and electron-beam melting (EBM), the heating-cooling regimes are very fast and location-specific, which can lead to different microstructures than those obtained with conventional processes, even with the same alloy composition. This is important as many engineers are looking to achieve similar microstructures to those achieved with conventional routes.

Consistent metal powder does not necessarily mean consistent properties

Metals can crystalize in different phases (ie. atomic arrangements) that have very different properties. As an example, the different phases of steel are illustrated in Figure 1. The pathways to phase formation in traditional processing are well-known, but in AM a different heating-cooling regime or different atomizing gases can produce products with a different phase composition and, therefore, different mechanical properties. When this powder is melted and rapidly recrystallized during an EBM or SLM process, there is further potential for phase transformation to occur. Grain microstructure can also be affected by processing conditions. It is more difficult to control grain structure in an AM manufactured part and this often results in large grain sizes compared with other methods.

Most engineers and metallurgists are looking for a fine grain structure since this improves material strength. This is why post-treatment is still commonplace for many metal AM processes. Grain orientation (also known as texture) is also important because a textured grain orientation can substantially change mechanical properties such as chemical reactivity, strength, and deformation response. This may lead to improved component strength or weakness, and premature failure.

Residual stress is another important characteristic of AM parts. Residual stresses are stresses that are retained in a component after manufacture and act in addition to any externally applied stress, increasing the risk of mechanical failure. AM components are more prone to residual stress due to highly localized cooling and rapid phase transformations that give insufficient time for stresses to relax to their equilibrium crystal structure. Residual stresses can occur anywhere in a material, but those located near a crack, pore, or at the surface of a component are of greatest concern since this is where stresses become most concentrated.

Why XRD is an important analytical tool

X-ray diffraction is a non-destructive analytical technique used to identify and quantify phases in a material. Every crystalline phase produces a characteristic diffraction pattern (e.g. fingerprint) as illustrated for steel in Figure 1.

Illustrations of the crystal structures of Austenite, Ferrite and Martensite and their corresponding diffractionpatterns
Figure 1: Crystal structures of Austenite, Ferrite and Martensite and their corresponding diffraction patterns

In addition to phase analysis, X-ray diffraction can also be used to analyze microstructural features such as texture, residual stress and grain size. Texture produces systematic deviations of peak intensity from the characteristic diffraction pattern of a phase. The intensity deviation can be used to quantify the fraction of grains in a certain orientation by tilting and rotating the sample in the diffractometer

A tensile or compressive residual stress will change the atomic spacing of a phase, which will produce a shift in the diffraction peak position. This can be measured with high sensitivity by X-ray diffraction. A series of measurements determine how peak position varies with sample orientation relative to the incident X-ray beam, which can then be used to precisely determine the atomic strain. If the elastic constant of the material is known, then the stress can be calculated.

X-ray diffraction can also be used to analyze grain size. Small grain sizes produce a broadening effect in the diffraction peak width that can be used to quantify crystallite sizes <200 nm. This makes X-ray diffraction a powerful technique to quantify the size of nanocrystalline materials. Peak broadening may also be produced by defects, such as dislocations or stacking faults, that are created during processing. Analysis of multiple diffraction peaks can be used to separate and quantify both size and defect concentration. In addition, area (2D) detectors can be used to image the Debye diffraction cone, which can reveal large grain sizes. New image analysis techniques can calibrate and quantify grain sizes larger than 10 µm.

HOW ON-LINE NIR SENSOR ENHANCE ORE PROCESSING AUTOMATION

Critical components of any ore processing automation strategy are the sensors that provide compositional, mineralogical, and ore property information that are essential for process optimization decisions. On-line over-the-conveyor sensors provide real-time information that is then available for feed-forward control of a wide range of ore processing operations.

Near-infrared (NIR) reflectance spectroscopy is widely used to provide mineralogic and ore property information required for ore transport, sorting, and processing decision making. The advantage of the NIR technique is the speed of measurement and the need for minimal or no sample preparation. Many of our customers have developed quantitative calibrations for a wide range of gangue minerals and ore properties; these include swelling clays, total clays, kaolinite, calcite, talc, hornblende, moisture, acid consumption, and hardness.  Analysis of geolocated blast hole chip samples using high-throughput NIR analyzers currently provides information essential for heap leach optimization and, for accurate short-term loading and hauling planning at many operating mines.

With the availability of the QualitySpec 7000 over-the-conveyor NIR sensor, these calibrations are now able to provide the real-time information necessary to implement feed-forward control of ore processing. This presentation provides an overview of the NIR method, current usage examples in operational mines, and an overview of how the QualitySpec 7000 over-the-conveyor NIR sensor enables ore processing automation.

Near-infrared (NIR) reflectance spectroscopy is widely used to provide mineralogic and ore property information required for ore processing decision making.

 

Written by: Brian Curtiss, Posted by: Malvern Panaltyical (www.materials-talks.com)

3 Things to Consider Before Analyzing a Sample

  1. Is your instrument optimized?

In a recent blog we explored the importance of instrument optimization for ICP-OES and ICP-MS, but all instruments should be frequently checked to confirm they are operating as expected. Preventive maintenance and appropriate start up and shut down SOPs are key to the integrity of analysis.

  1. How should you calibrate your instrument?

If you are following a standard method, calibration points might be recommended, however, you must also consider the expected levels of your specific samples when designing your calibration curve. Other best practices to consider include:

  • Don’t bracket your calibration curve too tightly around your samples expected levels
  • Match the matrix of your calibration standards to that of your samples to improve accuracy
  • Include a check standard in your run schedule

It is also important to be sure you have a high-quality calibration blank! Purchasing a CRM with a certificate of analysis that includes a trace scan can give you confidence in your blank.

  1. What is the best way to prepare your samples?

Top goals of sample preparation include bringing the sample to a suitable form for your instrument/ technique, ensuring sample homogeneity, and to manage potential interference’s resultant from the sample’s original form. Equally important is considering the quantity sample preparations and rate of throughput required for your lab. While there are many methods for sample analysis, some preparation techniques offer additional advantages in terms of speed and number of samples that can be prepared at once.

 

How do you prepare your instrument and samples for analysis? Share your best practices in the comments!

 

Written by: Courtney Dillon, Posted by : LGC ARMI MBH (www.armi.com)

THE IMPORTANCE OF CALIBRATING YOUR REMOTE SENSING IMAGERY

Most people using satellite or aerial imagery understand the importance of geometric calibration; you need to tie the pixels in the imagery to the corresponding coordinate locations on the earth so that you can use the imagery in mapping applications, and for analyses such as change detection.

However, there is another type of calibration that you should be doing on your optical remote sensing imagery to ensure that you are getting high quality and useful results from your images.  Accurate radiometric calibration is a critical component for successful imagery analysis.

Radiometric calibration, also known as radiometric correction, is important to successfully convert raw digital image data from satellite or aerial sensors to a common physical scale based on known reflectance measurements taken from objects on the ground’s surface.  This type of correction is important for reliable quantitative measurements of the imagery.

Each pixel in a spectral image has a signature based on the object or objects that the pixel represents as shown below:

On the left is a subset of an AVIRIS hyperspectral image that has been converted to reflectance. The spectral signature of the material associated with the pixel in the crosshairs is shown in the Spectral Profile plot on the right. ENVI software was used to display the image and the spectral plot.

You won’t see much of a difference in the image itself when correcting an image from radiance to reflectance.  The difference show up in the spectral signature associated with each pixel.

Ground Target Collection

The collection of known reflectance measurements from ground targets is performed using a field spectroradiometer like ASD’s FieldSpec 4.  To get the best results, you would try to measure the ground targets with the FieldSpec coincident with the overflight of the imaging sensor.  The collected field data is then used as input, along with the remote sensing imagery, in a software tool like ENVI to convert the radiance data to reflectance.

 

Measuring reflectance using a FieldSpec 4

What does this actually mean? 

Well, imagery typically starts out with uncalibrated, raw digital numbers (DN) for pixel values in the image.  These DN values get converted to radiance by applying a series of gains and offsets supplied by the data provider.  Imagery data that you purchase has often already been converted to radiance, or the data provider attaches some metadata with the corrections that you can apply using a remote sensing analysis package like ENVI software.

Radiance depends on the illumination (intensity and direction), orientation and position of the target feature being imaged, and the path of the light through the atmosphere.

Factors that affect radiance (Diagram from Humboldt State University [1])

The issue with radiance is that is has a lot of variability in terms of solar illumination and atmospheric effects such as water vapor in the atmosphere, so to get reliable and repeatable results, radiance typically gets converted to reflectance for image analysis.  Reflectance is the proportion of radiation striking a surface to the radiation reflected from it. With reflectance, the atmospheric variability is removed, so you get much more reliable measurements.

Spectral signatures showing radiance versus reflectance for a pure white panel, vegetation, and a dirt road. The radiance signatures show some of the main areas where atmospheric effects are a problem. [2]

Converting your imagery data to reflectance by using field reflectance measurements collected with a FieldSpec  gives you the most accurate results and greatly improves your ability to analyze your imagery, whether you are characterizing features or identifying target materials in your imagery.

If you would like more information on the FieldSpec or the benefits of calibrating your imagery, please click here

 

Written by : Ms. Susan Parks, Posted by: Malvern Panalytical (www.material-talks.com)

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

This blog elaborates on the nickel ore application in more detail.

Nickel ore processing

Primary sources of mined nickel are [1] magmatic sulphide deposits with pentlandite as a main ore mineral; and [2] 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.

the characteristic diffraction pattern with the main hpo and mpo peaks
Figure 1. Characteristic peaks of mpo (left) and hpo (right) – modifications of pyrrhotite.

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 [1].

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.

Results of XRD analysis of nickel ore concentrate
Figure 2. Results of XRD analysis of nickel ore concentrate.

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.

References:

  • [1] H.M. Rietveld, A profile refinement method for nuclear and magnetic structures, J. Appl. Cryst. (1969), 2, 65 – 71.
  • [2] H. Lohninger, Teach Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999, ISBN 3-540-14743-8.
  • [3] 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)