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[Display omitted] • 27 laboratory HIG5 grinding tests are performed for modelling the influence of operational variables. • Stress analysis is applied for understanding laboratory variables' correlation with their production level. • Correlation factors are determined based on empirical fitting with production data. • Laboratory models are calibrated for predicting and improving large-scale mill performance. As a fine and ultrafine grinding technology, the High Intensity Grinding (HIG) Mill is relatively new and there is opportunity to improve its performance by adjusting operational conditions. A laboratory HIG5 mill is commonly used for HIG mill sizing and scale up, however the clear correlation between the operational variables of laboratory mill to their production level is somewhat lacking. This paper presents a stress-based approach for predicting and improving large-scale HIG mill performance. A total of 27 experimental tests were performed with a HIG5 mill over a range of operational variable set points, including media density, media filling level, feed solid content, mill speed and flow rate. The influence of the variables on product size, mill operating power and specific grinding energy was characterized via multivariable linear regression modelling. Based on theoretical stress analysis and empirical calibration with available survey data, the model derived from laboratory testing was calibrated to predict the operation of the commercial HIG1600 mill. An approach was proposed for determining the effect of operational variables using a HIG5 that can be directly applied to reduce energy consumption at a given mill feed rate and product size target for the production scale mill. Results demonstrated that the stress-based approach integrated with laboratory testing can be used for predicting and improving large-scale HIG mill performance. [ABSTRACT FROM AUTHOR]

XRF bulk sorting is put forward as a credible intender for pre-concentration methods in mining. This paper presents a preliminary study employing a bench-scale approach to calibrate XRF surface sensing to a target concentration of interest related to its volume so as to assess the feasibility of a case barren copper ore “slots” to bulk sorting. Firstly, various top sizes, along with different operators and specimens’ weight/volumes, were examined to quantify the minimum readings needed (MRN) for XRF spot scanning at this scale. Multiple linear regression with a correlation coefficient of 0.921 was validated to be a reliable algorithm for calibrating XRF surface response to Cu volumetric concentration, owing to its consideration of elemental interacts. With the aid of graphical representations of calibrated data on recovery/grade curves, cut-off grade of 0.23% was chosen to enable 30% mass below the threshold to be discarded as waste and the remainder to go for further processing. [ABSTRACT FROM AUTHOR]