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Predicting iron ore sinter strength through partial least square regression (PLSR) analysis of X-ray diffraction patterns

Published online by Cambridge University Press:  26 October 2017

Nathan A.S. Webster ,
Mark I. Pownceby ,
Natalie Ware  and
Rachel Pattel
Nathan A.S. Webster*
Affiliation:
CSIRO Mineral Resources, Private Bag 10, Clayton South, VIC, 3169, Australia
Mark I. Pownceby
Affiliation:
CSIRO Mineral Resources, Private Bag 10, Clayton South, VIC, 3169, Australia
Natalie Ware
Affiliation:
CSIRO Mineral Resources, PO Box 883, Kenmore, QLD, 4069, Australia
Rachel Pattel
Affiliation:
CSIRO Mineral Resources, Private Bag 10, Clayton South, VIC, 3169, Australia
*
a) Author to whom correspondence should be addressed. Electronic mail: [email protected]
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Abstract

The decrease in quality of Australian iron ore, coupled with the demand for more efficient energy use, means that closer monitoring and optimisation of process conditions for iron ore sinter production is required. Here, the suitability of using partial least-squares regression analysis of powder X-ray diffraction data, collected for iron ore sinter samples, for the prediction of iron ore sinter strength has been further assessed. In addition, a preliminary assessment of the effect of 2θ range on the quality of prediction has been made. For the purposes of process control, the level of correlation between predicted strength and actual sinter strength would inform an operator whether or not the process was operating within the acceptable limits, or whether there was a potential problem requiring further investigation or rapid intervention. Reducing the 2θ range was found to reduce the level of correlation between predicted and actual strength, to a point where the particular analysis may no longer be suitable for process control.

Keywords

iron ore sintertumble indexpowder XRDpartial least square regressionphysical property prediction
Type
Technical Articles
Information
Powder Diffraction , Volume 32 , Supplement S2 , December 2017 , pp. S66 - S69
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Copyright
Copyright © International Centre for Diffraction Data 2017 

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