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Using X-ray DiffractionIN THE HUNT FOR COST AND CO

2REDUCTIONS IN THE METAL

In recent years, the issues of global

warming and greenhouse effects

relating to the increased carbon di-

oxide (CO2) levels in the atmosphere

have forced the world to look for

solutions to reduce emissions of

CO2. This race to reduce CO

2is

propelled both by new governmental

regulations and consumer behaviour,where both of these aspects have

the possibility to give companies at

the forefront a competitive edge.

Governmental regulations impose

extra costs and penalties for every

unit of CO2released into the at-

mosphere. Furthermore, customers

are becoming increasingly aware of

the CO2footprints caused by pro-

ducts. Therefore, companies in thefrontline of adopting new efficient

technologies for CO2reduction will

both be able to get a fast return on

investment (ROI) and gain a clear

market advantage.

By

Uwe Knig, PANalytical B.V.,

Almelo, Netherlands,

Julius Hllstedt, Jakob NorelandPANalytical B.V.,Vllingby, Sweden,

Kimmo Vallo, Outokumpu Stainless Oy,Tornio, Finland,

Luciano Gobbo, PANalytical,

So Paulo, Brasil,

Thomas Fllmann, Katherine Macchiarola,

PANalytical, Inc., Westborough, MA, USA,

Jouko Nieminen, PANanalytical B.V.,

Esboo, Finland

In this article we will introduce and descri-be how X-ray diffraction (XRD) has evol-ved from a research tool to a fully maturetechnique for routine analysis in industrial

applications. We will explain how XRD canbe used as an efficient tool for identifyingthe means to reduce CO

2emissions through

knowledge of phase composition of startermaterials and the subsequent selection ofthose materials that process more efficient-

the conditions and energy consumptionduring the smelting process.

X-ray diffraction on iron oremining and processing

The iron and steelmaking industry is re-sponsible for a substantial level of anthro-pogenic CO

2 emissions. Contributions to

global warming amount to about 650 milli-on tonnes of CO

2per year (Orth 2007). The

most energy-intensive stage, the reductionstep in the blast furnace, is mainly drivenby the requirement of carbon as a reducingagent. In order to reduce the environmental

The race to reduce CO2is propelled both by new governmental regulations and

consumer behavior. Both aspects have the possibility to give companies in the

forefront a competitive edge. Photo of the Experimental Blast Furnace in Lule a unique research plant for tests of iron ore products and blast furnace operations.

(Photo LKAB)

ly into finished products. We will highlightthis with a number of examples used in in-dustry.

First, we will take a look at an applicationused when mining for iron ore, where dis-tinguishing between hematite and magne-tite has a big impact both on the refining ofthe ore and on the amount of coke needed

in the blast furnace.Second, we will present an example of

how XRD can be used in the productioncontrol of ferrochrome and stainless steel.The phase content of different types of slagwere analyzed and evaluated to optimize

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(XRD)AND MINING INDUSTRY

Fig1. Measurement and phase identification for a natural iron ore sample from Brazil

(H = Hematite, M = Magnetite, G = Goethite, Q = Quartz)

impact, careful control of the phases invol-ved in the process is important in order tominimize energy consumption.

Applications for X-ray diffraction in thesteel industry are widely spread and inclu-de the blending of iron ores, the control ofthe sintering behaviour, quality control ofdirect reduced iron and the analysis of the

converter slag.X-ray powder diffraction (XRD) is a ver-

satile, non- destructive analytical methodfor the identification and quantitative de-termination of crystalline phases presentin powder and solid samples. Identification

of phases is achieved by comparing measu-red data to a reference database, the mostcomprehensive of which is maintained bythe International Centre for Diffraction Data(ICDD). This decades-old technique hasbeen a standard technique for qualitativeanalysis of mineralogical phases, but quanti-tative methods were often difficult since thetechnique called for pure phase standards.

Modern quantification analysis techni-ques, such as Rietveld analysis, are attrac-

tive alternatives as they do not require anystandards or monitors. These methods offerimpressive accuracy and speed of analysis.Modern XRD equipment is also capable ofproducing data of sufficient quality for Riet-veld analysis within minutes, instead of anhour or more with traditional detectors, ma-king it more amenable to process control.A CubiX3Minerals industrial diffractometerwith Co anode and high-speed XCeleratordetector with measurement times of lessthen 10 minutes was used for the studiespresented in this paper.

Figures 1 and 2 show the phase identi-

fication and the quantification of naturaliron ores. The samples consist of a mix-ture of hematite Fe

2O

3, magnetite Fe

3O

4,

goethite FeOOH and quartz SiO2. The

Rietveld method using the full range ofthe pattern was used to determine thephase content. Next to the quantities,Rietveld refinements can calculate prefer-red orientation of particles, the precise lat-tice parameters for the material, elementcontents in solid solutions, crystallite sizes

or amorphous parts of the sample. TheR

profile(Weighted) indicates the mathemati-

cal quality of the fit.

Cluster Analysis is a useful toolThe quantification of the main mineral pha-ses and the occurrence of other minerals(gangue) give important information forthe manufacturing process. The analysis ofhematite and magnetite in particular, andsubsequently the Fe

2+/Fe

3+ ratio, affects the

control of the energy consumption and the-reby the CO2emissions. Magnetite is oxidi-

zed to hematite in an exothermic chemicalreaction which produces heat and reducesthe need for energy.

Cluster Analysis is a useful tool withwhich to group different grades of ore intogroups or clusters when handling the largeamount of data coming from the rapid datacollection while using the XCelerator de-tector. This statistical method simplifies theanalysis of the data by:1. Automatically sorting all scans of one or

more experiments into classes of closely

related scans.2. Identifying the most representative scan

of each class.3. Identifying the two scans of each class

that differ the most.4. Identifying outliers not fitting into any

class (non-members).The tool can be used for sorting different

grades of ores, such as ores with differentmagnetite contents, i.e. different energyconsumption levels.

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Figure 3 shows the Principal ComponentsAnalysis (PCA) plot of 20 iron ore samp-les from different deposits. 4 clusters couldbe separated according to the differentamounts of hematite, magnetite, goethiteand quartz. One sample differs from theother values obtained in these clusters andcan be considered as an outlier.

The different grades of the ores are ex-plained in the following table 1:

The different clusters show different dist-ributions in the PCA plot. While the measu-rements for the red and the grey clusters areconcentrated in a small volume, the scansfor the blue and the green clusters show awider spread that represents a higher varia-bility in the material analyzed.

This information can help to distinguishbetween different ore deposits, different orequalities and ore types.

Cluster analysis can be performed before

further investigations such as phase identi-fication and quantification. The most repre-sentative scan of each cluster and perhapsthe scans that differ most can be used asstarting points for more detailed investiga-tions.

X-ray diffraction in ferrochrome

and stainless steel manufacturing

processingThis section describes the use of XRD forproduction control while minimizing theecological impact during the production offerrochrome and stainless steel. Approxi-

mately 90 percent of mined chromite oreworldwide is converted into different

Table 1

Green cluster High Magnetite, medium Hematite, high goethite, low quartzRed cluster Low Magnetite, high Hematite, low goethite, low quartzBlue cluster High Magnetite, low Hematite, high goethite, high quartzGrey cluster Low Magnetite, high Hematite, low goethite, high quartz

grades of ferrochrome used by the metal-lurgical industry. The stainless steel industryconsumes about 80 percent of the ferro-chrome produced.

Outokumpu is the sixth largest player inthe global stainless steel market. In the main,the company uses recycled steel directly asa raw material. This drastically reduces CO

2

emissions compared to steel productionbased on virgin materials but on the other

hand, it requires a much more precise con-trol of the process since the composition ofthe raw material varies.

Recently X-ray diffraction was used tomonitor the composition of different slagmaterials to control the operation of thesteel smelting process. The phase compo-sition of a slag reflects the material and en-ergy efficiency of a furnace, the presenceof impurities in the products and the life-time of the refractories used in the furnace.Furthermore, slag can be used for com-mercial purposes, such as road fills, buil-ding materials applications or ballast. Slags

containing periclase bind CO2from the airand thus reduce total CO

2emissions. Also

REACH (Registration, Evaluation, Authorisa-tion and Restriction of Chemicals) requiresmore precise characterization of the slag.

The measurements were taken on a

Fig 2. Rietveld refinement for a mixture of 70% hematite, 15% goethite, 5% magne-

tite and 10% quartz (dots = measurement, solid line = calculation, below = difference

plot), Rprofile

(Weighted) = 6.1

Fig 3. PCA score plot and confidence spheres of the analysis of 20 natural iron oresamples showing 4 different clusters of scans and 1 outlier.

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CubiX3 Minerals industrial diff-ractometer with Co anode and ahigh-speed XCelerator detector.The software XPert HighSco-rePlus in combination with thePAN-ICSD database was usedfor phase identification, quan-tification and cluster analysis.The XPert RoboRiet software forautomated quantitative Rietveldanalysis was used for the analy-sis of a large amount of data in aproduction control environment,

running in the PC background.Figure 4 shows the PCA score

plot from the cluster analysis of220 slag samples measured overa production period of 100 days.The plot clearly shows 3 diffe-rent slag types. Whereas the me-asurements for slag type 1 and 2are closely related in one cluster,the measurements in cluster 3show a much wider variety. Thescans indicated with grey colour

composition, the quantitativeamounts of the phases are alsoimportant to control the process(see figure 5). The presence ofpericlase and Al-Mg spinel givesinformation of the behaviour ofthe refractory used. This infor-mation together with the presen-ce of phases containing Fe and/or Cr, such as Fe-Cr-Spinel, canbe used to monitor the tempe-ratures and energy used for thesmelting process.

The ratio of crystalline andamorphous parts is controlledby the cooling conditions of theslag. The amount of amorphouscontent is part of the Rietveldquantification. For this calcula-tion, an external standard wasused.

ConclusionsIn this article we have shown ex-amples of how X-ray diffractioncan be used to provide valua-ble information for mining andprocess control in the miningindustry through standardlessquantification and fast, statisticalevaluation of large sets of datathrough cluster analysis. Todaysoptics, detectors, and softwarecan provide accurate analy-ses within minutes, suitable for

process control environmentsas well as research. This infor-mation gives important input forregulating energy consumptionand CO

2emissions in industrial

environments. n

Table 2

Slag Type 1 Slag Type 2 Slag Type 3

Forsterite Merwinite BredigiteMg

2SiO

4 Ca

3Mg(SiO

4)2 Ca7Mg(SiO

4)4

Spinel Melilite FluoriteMgAl

2O

4 (Ca,Na)

2(Al,Mg,Fe)(Si,Al)

2O

7 CaF

2

Spinel Periclase Larnite(Mg,Fe)(Al,Cr,Fe)

2O

4 MgO Ca

2SiO

4

Enstatite Calcite PericlaseMgSiO

3 CaCO

3 MgO

Amorphous part Monticellite Amorphous part CaMgSiO

4

Amorphous part

Koenig, U. & Gobbo , L.(2009):

Modern X-ray diffraction techni-

ques as fast industrial analysis

method for iron ores from

exploration to process control.

Proceedings, Iron Ore Conference

2009, Perth, 121 128.

Macchiarola, K.; Koenig, U.,

Gobbo, L. & Campbell; McDo-

nald, A. M. & Cirelli, J.(2007):

Modern X-ray Diffraction Techni-

ques for Exploration and Analysis

of Ore Bodies. In: Milkereit, B.:

Exploration in the New Millenium.

Proceedings of the Fifth Decennial

International Conference on Mine-

ral Exploration. Toronto, Canada.

Orth, A., Anastasijevic, N. &

Eichberger, H. (2007): Minerals

Engineering Vol. 20, p 854.

Rietveld, H.M. (1969): A profile

refinement method for nuclear

and magnetic structures, J. Appl.

Cryst., 2, 65 - 71.

are mixtures between slagtypes 2 and 3.Subsequently phase

identification and quantifi-cation of the representativescans of each cluster wereperformed (these scans areautomatically marked bythe software).

Table 2 shows the phasecomposition of the 3 diffe-rent types of slag. All 3 ty-pes contain crystalline pha-ses along with amorphous

parts. Types 2 and 3 are do-minated by magnesium andcalcium phases, whereastype 1 contains also ironand chrome spinel.

In addition to the phase

Fig 4. PCA score plot of the analysis of 220 slag samples

showing 4 different clusters of scans.

Fig 5. Rietveld refinement for a slag type 1 (dots = measurement,

solid line = calculation, below = difference plot), Rprofile

(Weighted) = 5.2