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Germanium (Ge) is a critical raw material for emerging high-tech and green industries, resulting in considerable recent interest in understanding its distribution and geochemical behavior in ore deposits. In this contribution, the distribution of Ge and related trace elements in the Fule Pb-Zn(-Ge) deposit, South China, is investigated to reveal the distribution of Ge in the hydrothermal ores and during sulfide weathering, using multiple microanalytical techniques, including scanning electron microscopy, electron probe microanalysis and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). In the Fule MVT deposit, sphalerite (ZnS) is the most significant Ge-carrier relative to other sulfides, though the five recognized textural types of sphalerite display progressive depletion in Ge from the first sphalerite generation to the late one. In the early stage, sphalerite with fine-grained chalcopyrite inclusions has the highest Ge concentrations, probably accounting for a significant proportion of the total Ge. We interpret that high Ge concentrations in the early sphalerite may be attributable to high Cu activity in the mineralizing fluids. During oxidative weathering, Ge was redistributed from its original host, sphalerite, to the weathering product willemite (Zn2SiO4) rather than smithsonite (ZnCO3), with high levels of Ge (up to 448 μg/g) present in the willemite. The formation of abundant willemite largely prevents the dispersion of Ge during weathering. In principle, willemite-hosted Ge should be fully recoverable, and the Zn-silicate ores may, therefore, be a potential target to meet future demand. This study provides new information on how Ge behaves from sulfide- to weathering-stage in MVT systems, which directly impacts Ge mobility and deportment changes and the development of metallurgical strategies for Ge recovery.

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 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.

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