Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-09T03:24:32.287Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the accuracy of mineral phase analysis by X-ray diffraction using Monte Carlo modelling

Published online by Cambridge University Press:  17 November 2014

Joel N. O'Dwyer ,
James R. Tickner  and
Greg J. Roach
Joel N. O'Dwyer*
Affiliation:
CSIRO Mineral Resources Flagship, Locked Bag 2005, Kirrawee, NSW 2232, Australia
James R. Tickner
Affiliation:
CSIRO Mineral Resources Flagship, Locked Bag 2005, Kirrawee, NSW 2232, Australia
Greg J. Roach
Affiliation:
CSIRO Mineral Resources Flagship, Locked Bag 2005, Kirrawee, NSW 2232, Australia
*
a)Author to whom correspondence should be addressed. Electronic mail: joel.o'dwyer@csiro.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Rapid, on-line measurement of feedstock mineralogy is a highly attractive technology for the mineral processing industry. A Monte Carlo particle transport-based modelling technique has been developed to help design and predict the measurement performance of on-line energy-dispersive X-ray diffraction (EDXRD) analysers. The accuracy of the technique was evaluated by performing quantitative phase analysis on a suite of fifteen synthetic potash ore samples. The diffraction profile of each sample was measured with a laboratory EDXRD analyser and an equivalent profile was simulated in the Monte Carlo package. Linear regression analysis was used to determine the mineral abundances in each sample from both the measured and modelled profiles. Comparison of the results showed that the diffraction profiles and measurement accuracies obtained by simulation agree very well with the measured data.

Keywords

Monte Carlo modellingenergy-dispersiveX-ray diffractionminerals processingonline analysis
Type
Technical Articles
Information
Powder Diffraction , Volume 29 , Issue S1 , December 2014 , pp. S102 - S106
Check for updates
Copyright
Copyright © International Centre for Diffraction Data 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Castro, C. R. F., Barroso, R. C., de Oliveira, L. F., and Lopes, R. T. (2005). “Coherent scattering X-ray imaging at the Brazilian National Synchrotron Laboratory: preliminary breast images,” Nucl. Instrum. Methods A 548, 116–122.Google Scholar
Cook, E., Fong, R., Horrocks, J., Wilkinson, D., and Speller, R. (2007). “Energy dispersive X-ray diffraction as a means to identify illicit materials: a preliminary optimisation study,” Appl. Radiat. Isot. 65, 959–967.Google Scholar
Dozier, C. M. and Anibou, N. (2009). “An innovative EDXRD verification probe,” Powder Diffr. 24, 102–106.CrossRefGoogle Scholar
Farquharson, M. J. and Speller, R. D. (1998). “Trabecular bone mineral measurements using Energy Dispersive X-ray Diffraction (EDXRD),” Radiat. Phys. Chem. 51, 607–608.Google Scholar
Harding, G. and Schreiber, B. (1999). “Coherent X-ray scatter imaging and its applications in biomedical science and industry,” Radiat. Phys. Chem. 56, 229–245.Google Scholar
Harding, G., Kosanetzky, J., and Neitzel, U. (1987). “X-ray diffraction computed tomography,” Med. Phys. 14, 515–525.Google Scholar
Kawrakow, I. and Rogers, D. W. O. (2000). “The EGSnrc Code System: Monte Carlo Simulations of Electron and Photon Transport,” NRCC Report PIRS-701.Google Scholar
Luggar, R. D., Farquharson, M. J., Horrocks, J. A., and Lacey, R. J. (1998). “Multivariate analysis of statistically poor EDXRD spectra for the detection of concealed explosives,” X-ray Spectrom. 27, 87–94.Google Scholar
Malden, C. H. and Speller, R. D. (2000). “A CdZnTe array for the detection of explosives in baggage by energy-dispersive X-ray diffraction signatures at multiple scatter angles,” Nucl. Instrum. Methods A 449, 408–415.Google Scholar
Newton, M., Hukins, D. W. L., and Harding, G. (1992). “Bone composition measured by X-ray scattering,” Phys. Med. Biol. 37, 1339–1347.Google Scholar
O'Dwyer, J. N. and Tickner, J. R. (2007). “Modelling diffractive X-ray scattering using the EGS Monte Carlo code,” Nucl. Instrum. Methods A 580, 127–129.Google Scholar
Redus, R., Huber, A., Pantazis, J., Pantazis, T., Takahashi, T., and Woolf, S. (2004). “Multielement CdTe stack detector for gamma-ray spectroscopy,” IEEE Trans. Nucl. Sci. 51, 2386–2394.Google Scholar
Rowles, M. R., Styles, M. J., Madsen, I. C., Scarlett, N. V. Y., McGregor, K., Riley, D. P., Snook, G. A., Urban, A. J., Connolley, T., and Reinhard, C. (2012). “Quantification of passivation layer growth for molten salt electrochemistry by in situ energy-dispersive diffraction,” J. Appl. Crystallogr. 45, 28–37.Google Scholar
Royle, G. J. and Speller, R. D. (1991). “Low angle X-ray scattering for bone analysis,” Phys. Med. Biol. 36, 383–389.Google Scholar
Scarlett, N. V. Y., Madsen, I. C., Evans, J. S. O., Coelho, A. A., McGregor, K., Rowles, M., Lanyon, M. R., and Urban, A. J. (2009). “Energy-dispersive diffraction studies of inert anodes,” J. Appl. Crystallogr. 42, 502–512.Google Scholar
Tickner, J. (2005). “XPERT – A Package for Geometry Definition, Verification and Visualisation,” Proceedings of Monte Carlo 2005, Chattanooga, April 17–21 2005.Google Scholar
Tucker, D. M., Barnes, G. T., and Chakraborty, D. P. (1991). “Semiempirical model for generating tungsten target X-ray spectra,” J. Med. Phys. 18, 211–218.Google Scholar
Weidinger, A. and Hermans, P. H. (1961). “On the determination of the crystalline fraction of isotactic polypropylene from X-ray diffraction,” Die Makromol. Chem., 50, 98–115.Google Scholar
Wyckoff, R. W. G. (1963). Crystal Structures (John Wiley and Sons, New York).Google Scholar