首页|Testing the robustness of particle-based separation models for the magnetic separation of a complex skarn ore

Testing the robustness of particle-based separation models for the magnetic separation of a complex skarn ore

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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 pre-dict the process outcomes of new samples of variable composition.Nevertheless,the scenario in which mass flow could be measured was~4%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.

Metallurgical testsParticle-based separation modellingMagnetic separationCassiterite recovery

Lucas Pereira、Max Frenzel、Markus Buchmann、Marius Kern、Raimon Tolosana-Delgado、K.Gerald van den Boogaart、Jens Gutzmer

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Hemholtz-Zentrum Dresden-Rossendorf,Helmholtz Institute Freiberg for Resource Technology,09599 Freiberg,Germany

German Aerospace Center,Institute of Low-Carbon Industrial Processes,03046 Cottbus,Germany

TU Bergakademie Freiberg,Institute for Stochastics,09599 Freiberg,Germany

German Federal Ministry for Educa-tion and Research(BMBF)for funding the projects MoCaAFK

033R189Bgrant number 033R128

2022

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCDSCIEI
影响因子:1.222
ISSN:2095-2686
年,卷(期):2022.32(3)
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