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• Error analysis was conducted on the XRF measurements of ore materials at lab-scale. • Three main types of errors were identified associated with XRF measurements. • The applicability of XRF in bulk ore sorting applications was discussed. X-Ray Fluorescence is one of the sensing technologies with potential in bulk ore sorting application. However, its reliability is often questioned since X-Ray Fluorescence does not sufficiently penetrate and errors are associated with surface measurements representing the volumetric grade. In this paper, error analysis was conducted on the XRF measurements on bulk ore materials at lab scale. Three main sources of the errors were identified, termed as instrumental error, heterogeneity error and the sampling error. Among the errors, the heterogeneity error was identified as the most significant due to its irrelevance with measuring the concentration and the unpredictability of that value. The influence of the errors on the conventional linear regression approach was investigated in terms of the sorting results and economics. Results showed that the linear regression approach was expected suitable for cases when sufficient analysis interval time could be ensured. It this was allowed for, the applicability of XRF in the bulk ore sorting was fundamentally justified. [ABSTRACT FROM AUTHOR]

• Receiver Operating Characteristic (ROC) was applied in ore classification for bulk ore sorting. • ROC approach was found to result in more accurate classification compared to SLR. • Economics performance was enhanced by ROC approach compared to that of SLR. As high grade deposits become depleted, the mining industry is challenged by mining lower grades that require the use of non-selective bulk mining methods. Bulk ore sorting can reduce the dilution of the mined ore thereby preventing costly processing of low grade waste while improving the quality of material reporting to the processing plant. X-Ray Fluorescence (XRF) is one of the sensing technologies used for bulk ore sorting. The grade of the bulk material is predicted based on XRF measurement that is correlated to a grade using a calibrated algorithm. The effectiveness of XRF bulk sorting depends on the accuracy of the grade classification of the scanned material. False classification is attributed to poor performance of the sorting algorithm to correlate the measurements with the assay grades. The paper presents an approach to analyze XRF measurements that reduce misclassification of material. Receiver Operating Characteristic (ROC) was applied as the tool in classification of bulk materials. The performance of this approach was compared to conventional sorting algorithm, namely, simple linear regression (SLR), where XRF responses was linearly correlated to the assay grades. The ROC approach was found to result in more accurate classification and enhanced economics performance of the bulk ore sorting system. [ABSTRACT FROM AUTHOR]