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• PGNAA and MR can be used simultaneously for bulk ore sorting of copper porphyries. • Integration is key for deposits with varying bornite and chalcopyrite ratio. • A greater increase in ore value is estimated when the sensors are used together. Bulk ore sorting is a preconcentration technique that may address the operational and environmental challenges of extracting low-grade ore deposits, such as copper porphyries. The technique allows for the early detection and diversion of sub-economic material by sensor-diverter systems before milling, thereby improving mill feed quality and reducing environmental impact. Grade heterogeneity in ore and sensor capability to measure grade accurately are prerequisites to applying bulk ore sorting. The technical synergy between the responses of multiple bulk ore sorting technologies can aid in taking full advantage of the grade heterogeneity existing in sorter feed while providing accurate and precise grade measurements. This study is aimed at quantitatively demonstrating the potential benefits of a conceptual strategy of integrating the responses from two sensor technologies for bulk ore sorting of copper porphyry deposits. Results show that a more significant enhancement in the Net Smelter Return (NSR) of the ore can be achieved by the simultaneous employment of the magnetic resonance (MR) and prompt gamma neutron activation analysis (PGNAA) sensors compared to the bulk ore sorting scenarios in which the sensor technologies are utilized separately. Results are encouraging and can prompt the collaboration of sensor manufacturers and mining companies to prove the actual value of the presented concept by performing on-site trials. [ABSTRACT FROM AUTHOR]

• Nine influential factors were integrated in a mathematical framework based on fractal modelling approach. • The influence of various factors on the ore sortability were investigated. • Factors of significant influence were identified. The uptake of bulk ore sorting technology is hindered by little knowledge in assessing its applicability for certain mine operation. The applicability of the bulk ore sorting technology, referred to as 'sortability', is a multi-folded concept and is influenced by many factors. Researchers have investigated the influence of some of the various factors, but few have done this in a holistic perspective and in a quantitative manner. In this research, nine influential factors of ore sortaibility were integrated in a mathematical framework based on fractal modelling approach. Box-Behnken design was applied and 130 block cave mine operations were hypothesized under different values of the factors. The influence of the factors on the ore sortability were investigated and factors of significant influence were identified. Results showed that ore property, sorting efficiency, metal price, processing cost and operation scale were the most significant factors in affecting the sortability for a mine operation. The research is intended to improve the understanding on the bulk ore sortability for mine productions under different conditions, and to provide a holistic perspective for applying the bulk ore sorting technology. [ABSTRACT FROM AUTHOR]

• The developed fractal model was validated on mine production data. • The multifractal model was proposed for bulk ore sortability assessment. • The multifractal model was more accurate in predicting the sorting results and assessing ore sortability. A previous study demonstrated the theoretical development of a model for assessing bulk ore sortability. However, the model has not been validated with many cases. In this study, the developed model was validated using the production data from 232 draw points in a block cave copper mine. For each draw point, sorting results and mine economics were predicted using fractal modelling and compared with results based on actual data analysis. The statistical error was measured for the fractal model predictions of feed grade, mass yield, pre-concentrate grade, metal recovery, and NSR with and without bulk ore sorting. An optimised approach was developed using multifractal modelling; two sets of fractal parameters were determined to characterise the metal distribution in high- and low-grade ranges. The results showed that the multifractal model was more accurate than the fractal model in predicting the sorting results and assessing ore sortability. [ABSTRACT FROM AUTHOR]

• The potential of bulk ore sorting application at a block cave mine was investigated. • The grade heterogeneity and sortability was evaluated for different mining zones. • The relationship between ore grade heterogeneity and sortability was discussed. A block cave mine operation was investigated in terms of ore grade heterogeneity and the potential of bulk ore sorting application. The grade heterogeneity within the caved material was measured for different draw points and mining zones, in terms of Distribution Heterogeneity and Fractal Dimension. The potential of bulk ore sorting was assessed by comparing the Net Smelter Return before and after a hypothetical bulk ore sorting system was implemented. Results showed that the potential of applying bulk ore sorting was influenced greatly by the heterogeneity within the mined material. A certain level of heterogeneity was necessary in considering the mine production as 'sortable'. The sortability would also vary for different mining areas depending on heterogeneity level of the production. Peripheral zones of the orebody were considered with the highest level of heterogeneity and the most significant potential of bulk ore sorting application. [ABSTRACT FROM AUTHOR]

• 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]

• A bulk ore sorting model was developed for sortability assessment. • Metal distribution within ore deposits was recognized as a fractal structure. • The optimum cut-off grade was determined based on model calculations. Bulk ore sorting (BOS) has the potential to reduce processing costs and improve mine economics. A factor that impedes its acceptance is the lack of a method to assess ore amenability to sorting. In this study, a fractal model of metal distribution was used to assess an ore's bulk sortability. A BOS model was developed where metal distribution properties were characterised, optimum cut-off grade for sorter operation was determined, and economic potential of BOS was evaluated. For the developed model, the metal distribution within the ore deposits was recognised as a fractal structure. Two parameters were introduced, namely the grade factor and fractal dimension, to quantitatively characterise ore properties in terms of grade magnitude and heterogeneity. The mass balance of the sorter operation was modelled as a function of the characterised ore properties and the optimum cut-off grade was determined based on model calculations. The economic potential of BOS was evaluated in terms of the net smelter return and compared with that when no BOS was implemented. Based on the developed model, the bulk ore sortability of different ore properties was assessed with a hypothetical underground copper mine. The developed BOS model provided a powerful tool for ore sortability assessment and is expected to encourage academic research and industrial practices of BOS. [ABSTRACT FROM AUTHOR]

• Even with the ideal conditions, bulk ore sorting may not be applicable. • Metal losses limit the value associated with bulk ore sorting. • Good correlation between copper and gold is required for effective ore sorting. Bulk ore sorting may add significant value to block and panel cave mines where processing feed grades are constrained by limited grade selectivity. The Cadia East gold-copper mine is a panel caving operation utilizing two different on-belt bulk ore sensors to evaluate the potential benefits of the technology. To date, there is limited reported work addressing the applicability of bulk ore sorting systems at a caving mine, where two commodities drive the economics. This indicative study is aimed at investigating the importance of various key factors on the bulk ore sorting potentials of the active and future panel caves of the Cadia East mine. Results show that for only one active cave of the mine, bulk ore sorting may be applied in the ideal scenario where grades are not affected negatively by mixing and the sorting takes place at maximum efficiency. As a general trend for all the caves, when the sorting cut-off grade is increased beyond a certain threshold, a sharp decline is observed in the sorting potentials due to the significant amounts of gold and copper deporting to the sorter rejects. In the case of a significant drop in metal prices, bulk ore sorting may only provide an opportunity to improve cave economics marginally. Results obtained by investigating the spatial change in sorting potential show that the portions of the caves with low metal grades are favourable for bulk ore sorting. In the scenario where bulk ore sorting is focused on low-grade portions, it is demonstrated that improvements in the economics of the future caves may additionally be achieved. However, as the low-grade portions represent a very small volume in the orebody, applying ore sorting on the plant conveyor may not be viable. [ABSTRACT FROM AUTHOR]