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Heavy Mineral Sand Analysis for Industrial Process Control

posted Oct 22, 2012, 10:00 AM by Jonny Arkin

Introduction

Heavy mineral sands are a class of ore deposits that are an important source of titanium, zirconium, thorium and rare earth elements. This application note describes the use of XRD, together with the Rietveld method and cluster analysis, for phase identification and quantification of heavy mineral sands.

Phase Identification and Quantification

Phase Identification

Figure 4 shows the phase identification results for one of the heavy mineral concentrate samples. Ten phases were identified including ilmenite, rutile, zircon and hematite, along with smaller amounts of anatase, quartz, actinolite, epidote, diopside and almandine. Phase identification was conducted using PANalytical’s HighScorePlus software. To identify the phases present the software employs a powerful search-match algorithm and compares measured data to reference database PDF4+ of the ICDD.

Batch programs within the software are available to enable automation of phase identification for industrial environments, delivering results at the push of a button.

Figure 4. Measurement and phase identification for a heavy mineral concentrate sample (Z = zircon, I = iIlmenite, R = rutile, Q = quartz, Ac = actinolite, Al = almandine, Ana = anatase, Di = diopsite, He = hematite, E = epidote)

Phase Quantification

Rietveld Analysis

Quantitative analysis is possible by various classical methods such as straight line or polynomial calibration with standards. Reliable results can be obtained using the full pattern refinement introduced by H. Rietveld in 1969.

The Rietveld method is a modern, highly effective quantification technique. It delivers impressive precision, allows a rapid analysis speed and, importantly, does not require standards or monitors. In addition, the Rietveld method takes into account preferred orientation, lattice parameters, possible solid solutions, crystallite sizes and amorphous contents. The RProfile provides an indication of the mathematical quality of the fit.

Figure 5 shows the results of a Rietveld refinement of a heavy mineral concentrate. The zoomed area - Figure 6 – highlights the fit between measured and calculated profile.

Figure 5. Rietveld refinement for a heavy mineral concentrate sample (red dots = measurement, blue = calculation, below = difference plot), RProfile = 3.6

Figure 6. An area of the Rietveld simulation overlaid with measured data

Experimental Setup

Sample Preparation

The grain size of the heavy mineral sands is far too large for direct analysis. Samples were therefore ground in a disk mill with a tungsten carbide (WC) vessel and were pressed into steel ring sample holders. The steel rings are compatible with semi-automatic sample preparation equipment with a piston diameter of 35 mm.

Instrument Setup

The measurements were made using a PANalytical CubiX3 Minerals diffractometer, equipped with the X’Celerator detector and a cobalt tube with incident iron filter. This type of radiation is especially suited to iron containing materials, as it produces high resolution data without interference from the sample fluorescence. A measurement time of 5 minutes was chosen to obtain sufficient peak intensities. For good peak to background ratios, a beta filter (Fe) was placed into the incident beam path.

Figure 1. The CubiX3 Minerals diffractometer is equipped with computer controlled slit optics, a variable speed spinner stage and the X’Celerator detector, which produces high resolution data up to 100 times faster than with traditional instrumentation.

Figure 2. Schematic diagram of the diffractometer configuration

Figure 3. Comparison using the beta filter in the incident or diffracted beam

Reproducibility and Precision

To evaluate the reproducibility of the sample preparation method, a sample was prepared and measured 27 times. Analysis precision was also tested by repeating the measurement of one sample 37 times.

The results are illustrated in Table 1. Figure 7 shows the width of distribution (standard deviation or sigma). Two thirds of the results in the plots are within ± sigma, 95% are within ± two sigma and all results are within three sigma.

Table 1. Quantitative results using the Rietveld method for the precision and reproducibility

RutileZirconIlmeniteHematiteQuartzEpidoteActinoliteAlmandineDiopsiteAnatase
Reproduciblity of the sample preparation (27 preparations for 1 sample)
Average810.358.93.92.94.95.41.33.80.8
Stand. dev.0.20.51.90.20.20.60.90.10.30.1
Minumum7.6955.73.52.13.83.81.13.40.7
Maximum8.511.1624.34.166.71.44.50.9
Reproduciblity of the method (1 preparation measured 37 times)
Average7.910.757.242.45.26.61.340.8
Stand. dev.0.10.20.70.10.40.40.40.10.20.1
Minumum7.810.356.33.71.54.55.51.23.40.7
Maximum8.110.958.64.23.65.97.21.44.50.9

Figure 7. Standard deviations for rutile and zircon for the precision of the method (levels of confidence: solid = mean, dashes = 1 sigma, dotted = 2 sigma, dashes-dotted = 3 sigma)

Cluster Analysis

A third type of analysis – clustering or cluster analysis - was conducted on the XRD data collected from the heavy mineral sands. Modern XRD equipment allows rapid collection of hundreds of scans. For exploration and process control applications it would

Two different cluster analyses were performed. In a first test the 64 scans from the reproducibility measurements were analysed. The principal component analysis PCA score plot (Figure 8a) clearly shows two different clusters and one outlier. The scans of

The different clusters show different distributions in the PCA plot. While the measurements for the precision test are concentrated in a small area, the scans for the different preparations are widely spread in the PCA plot, indicating a higher standard d

Cluster analysis can be performed prior to subsequent investigations, such as phase identification and quantification. The most representative scans, and scans that differ the most, can be used as starting points for more detailed examination.

For the purposes of this study, a second test was carried out on several scans from samples taken from different steps during processing (screening). Four different groups and one outlier were calculated (Figure 8b). The clusters corresponded with a highe

Figure 8 a,b. PCA score plots for the two different experiments (reproducibility measurements, samples from the different process steps)

Conclusions

Modern XRD can provide valuable information for mining and process control in the heavy minerals industry through standardless quantification and fast, statistical evaluation of large datasets through cluster analysis. Today’s optics, detectors, and softw

The Rietveld method has many attributes that lend themselves to process control applications. It is suitable for use with homogeneous and heterogeneous samples, and works with powdered materials. It is relatively fast, cost-effective and able to distingui

The use of cluster analysis to evaluate XRD data allows fast and reliable tracking of the process. It is highly cost-effective as data evaluation is automatic and does not require dedicated personnel. It is ideal for supporting process and grade control a

The data shown here demonstrate that the CubiX3 Minerals system, together with the appropriate software modules, is ideal for the quantitative analysis of heavy mineral sands by XRD. The fast X’Celerator detector was highly valuable for delivering very sh

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PANalytical is the world’s leading supplier of analytical instrumentation and software for X-ray diffraction (XRD) and X-ray fluorescence spectrometry (XRF), with more than half a century of experience. The materials characterization equipment is used for scientific research and development, for industrial process control applications and for semiconductor metrology.

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