Predictive Geometallurgy


Predictive Geometallurgy

Gutzmer, J.; Birtel, S.; Büttner, P.; Bachmann, K.; Kern, M.; Frenzel, M.

For centuries the German proverb “Vor der Hacke ist es duster” has aptly described the lack of knowledge about ore volumes, grades and beneficiation characteristics during the incremental progress of mining operations. Although much progress has been made constraining ore volumes and grades by following rigorous exploration drilling programs and applying appropriate geostatistical and spatial modelling tools, there still remains considerable technical risk when exploration turns into exploitation. This is illustrated by the observation that ca. 70% of mines perform below the prediction of their feasibility study (Wood, 2018). This underperformance is usually attributed to deficiencies in the collection of tangible geoscientific data needed to design the mine and the minerals processing plant (Wood, 2018).
Geometallurgy is an interdisciplinary approach that aims to connect the data available from geosciences with the information required to predict the performance of technologies used for ore extraction and mineral beneficiation. Tangible resource characteristics – beyond grade and tonnage - are quantified to create a model that links the geology of an ore deposit with the performance achieved during mining, mineral processing and extractive metallurgy. Successful geometallurgical programs may thus be used to mitigate the risk of production planning and plant design. However, the tools of geometallurgy have thus far mostly been used by the mining industry to improve metal recoveries and to monitor process efficiency of mineral processing plants only.
Present research goes beyond these current applications of geometallurgy. Predictive geometallurgical models for complex ore bodies and even anthropogenic raw materials are being developed by interdisciplinary teams including expertise in exploration, resource characterization, minerals processing, geostatistics and spatial modelling. Case studies will be presented in this contribution that illustrate the approach taken. These examples include (1) the recovery of Sn from a historic flotation tailings storage facility; (2) the recovery of PGE as a by-product of chromite exploitation; and (3) the intelligent use of quantitative mineral abundance and mineral association data to predict the prospects of success of sensor-based sorting.
Results obtained in the three case studies illustrate the prospects of increasing resource and energy efficiency in the mining industry. Innovative approaches are of general applicability and can be easily extended to other metals and ore deposit types. The results clearly illustrate the value of conducting comprehensive geometallurgical assessments already during the latter stages of exploration; the initial process of constructing a predictive geometallurgical model will, of course, benefit greatly from regular follow-up during the phase of active exploitation.

Keywords: geometallurgy; geosciences; minerals processing; metallurgy

  • Invited lecture (Conferences)
    4th GOOD Meeting, 23.-25.01.2019, Bremen, Germany
  • Contribution to proceedings
    4th GOOD Meeting, 23.-25.01.2019, Bremen, Germany
    4th GOOD Meeting Abstract Volume, Bremen

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Publ.-Id: 28808