Physical separation processes are best understood in terms of the behaviour of individual ore particles.Yet,while different empirical particle-based separation modelling approaches have been developed,their predictive...Physical separation processes are best understood in terms of the behaviour of individual ore particles.Yet,while different empirical particle-based separation modelling approaches have been developed,their predictive performance has never been tested under variable process conditions.Here,we investigated the predictive performance of a state-of-the-art particle-based separation model under variable feed composition for a laboratory-scale magnetic separation of a skarn ore.Two scenarios were investigated:one in which the mass flow of the different processing streams could be measured and one in which it had to be estimated from data.In both scenarios,the predictive models were sufficiently general to predict the process outcomes of new samples of variable composition.Nevertheless,the scenario in which mass flow could be measured was4%more precise in predicting mass balances.The process behaviour of minerals present at concentrations above 0.1%by weight could be accurately predicted.Our findings indicate the potential use of this method to minimize the costs of metallurgical testwork while providing in-depth understanding of the recovery behaviour of individual ore particles.Moreover,the method may be used to establish powerful tools to forecast mineral recoveries for partly new ore types at a running mining operation.展开更多
基金the German Federal Ministry for Education and Research (BMBF) for funding the projects MoCa (grant number 033R189B) and AFK (grant number 033R128), which were essential to this studythe Saxore Bergbau GmbH for providing the samples for this studySabine Gilbricht (TU Bergakademie Freiberg) for support during SEM-MLA data acquisition
文摘Physical separation processes are best understood in terms of the behaviour of individual ore particles.Yet,while different empirical particle-based separation modelling approaches have been developed,their predictive performance has never been tested under variable process conditions.Here,we investigated the predictive performance of a state-of-the-art particle-based separation model under variable feed composition for a laboratory-scale magnetic separation of a skarn ore.Two scenarios were investigated:one in which the mass flow of the different processing streams could be measured and one in which it had to be estimated from data.In both scenarios,the predictive models were sufficiently general to predict the process outcomes of new samples of variable composition.Nevertheless,the scenario in which mass flow could be measured was4%more precise in predicting mass balances.The process behaviour of minerals present at concentrations above 0.1%by weight could be accurately predicted.Our findings indicate the potential use of this method to minimize the costs of metallurgical testwork while providing in-depth understanding of the recovery behaviour of individual ore particles.Moreover,the method may be used to establish powerful tools to forecast mineral recoveries for partly new ore types at a running mining operation.
