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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]

Abstract: Increasing energy efficiency and reducing water consumption are two of the key requirements in moving toward a more sustainable mining industry. This paper demonstrates a method of determining the lowest energy option for a mine/mill water system network. The method uses a linear programming algorithm to compare different possible combinations of supplying water to mine and mill consumers. First, a site water balance is determined and then every major water consumer and source is specified. Water sources may require pumping, cooling and/or treatment before the water meets the specifications of the water consumer. The amount of energy required for each water source to supply each consumer is calculated and then the model is solved to minimize power consumption. An example is used to illustrate the method. [Copyright &y& Elsevier]