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Sizing High-Pressure Grinding Rolls (HPGR) requires a large quantity of material, making it not attractive and costly to be considered for new mining projects regardless of their energy consumption reduction benefits. Ongoing efforts are being made at the University of British Columbia to predict the behaviour of the HPGR using a low quantity of material on a piston-and-die press apparatus. Although the energy requirements and size reduction predictive models are already developed, there is still a need to predict the HPGR throughput on a small-scale test. This paper presents a new model to predict the HPGR throughput based on the previously developed model to predict the operational gap by using less than 2 kg of sample. The throughput model was developed using machine learning techniques and calibrated using pilot-scale HPGR tests and piston press tests. The resulting model has an R2 of 0.91 with an average prediction error of ±4.2%. The developed methodology has the potential to fill the gap of the missing throughput model. Further pilot-scale HPGR testing is required to continue validating the model. [ABSTRACT FROM AUTHOR]

A method for determining the minimum practical energy for comminution was developed and is presented in this paper. An objective of the method was to determine experimentally the energy-breakage relationship for a wide size range in order to evaluate the energy performance of both crushing and grinding processes using one energy benchmarking value. Single-particle compression breakage, referred to in the field of comminution as one of the more efficient forms of mechanical comminution, was the basis for a test regimen to characterize the energy-breakage properties of ores. Existing models for impact breakage were found to be valid for single-particle compression breakage when used in a modified form. A key parameter of the adopted model, the threshold energy, was also investigated for three ore types and a range of particle sizes. The energy performance of comminution processes at a Canadian mining operation was determined by comparing the determined minimum practical energy, using the new method, with actual site specific energy requirements. In order to evaluate the energy performance of different crushing and grinding technologies, the proposed energy benchmarking method was used to compare the energy performance of alternative comminution flowsheets. [ABSTRACT FROM AUTHOR]

[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]

• A small-scale locked-cycle piston press test to provide design information closed-circuit HPGR. • The test ware inspired by the Bond ball mill grindability test. • Piston Work Index describes the samples' resistance to HPGR comminution. • The test results were within ± 20% range of HPGR tests for a fine feed. The increased energy efficiency of HPGR-based circuits as an alternative to AG/SAG mill circuits has been well established. However, assessing the HPGR technology involves pilot-scale testing requiring large samples, which is expensive and may be prohibitive, particularly for early-stage projects. As a result, the uptake of HPGR technology has been slow, emphasizing the need for small-scale tests for sizing and selection. A reliable small-scale test for preliminary HPGR circuit design is essential in garnering the mining industry's interest in the broader adoption of HPGRs. Inspired by the Bond ball mill grindability test, a small-scale locked-cycle piston press test was developed to estimate the circulating load and circuit-specific energy consumption for closed-circuit HPGR operation. The test introduces the Piston Work Index, which describes the ore samples' resistance to HPGR comminution and can be subsequently used to estimate the HPGR circuit-specific energy consumption. The test results indicated that the circuit-specific energy and circulating load obtained from the small-scale piston press test were within the ± 20% range of the locked-cycle pilot scale HPGR tests for a fine feed representing quaternary stage HPGR application. [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]