• Volume 45 Issue 11
    Nov.  2023
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    WANG Ling, DU Yuhang, ZHAO Zhanfeng, ZHANG Wenjuan, MA Baozhong, WANG Chengyan. Application of geometallurgical modeling in SICOMINES refractory copper–cobalt deposit in Congo (Kinshasa)[J]. Chinese Journal of Engineering, 2023, 45(11): 1847-1858. doi: 10.13374/j.issn2095-9389.2023.01.02.001
    Citation: WANG Ling, DU Yuhang, ZHAO Zhanfeng, ZHANG Wenjuan, MA Baozhong, WANG Chengyan. Application of geometallurgical modeling in SICOMINES refractory copper–cobalt deposit in Congo (Kinshasa)[J]. Chinese Journal of Engineering, 2023, 45(11): 1847-1858. doi: 10.13374/j.issn2095-9389.2023.01.02.001

    Application of geometallurgical modeling in SICOMINES refractory copper–cobalt deposit in Congo (Kinshasa)

    doi: 10.13374/j.issn2095-9389.2023.01.02.001
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    • Corresponding author: E-mail: wchy3207@sina.com
    • Received Date: 2023-01-02
      Available Online: 2023-04-07
    • Publish Date: 2023-11-01
    • The SICOMINES Cu–Co ore deposit is located in southwest Kolwezi, Congo (Kinshasa), and is a typical deposit in the Katanga Copper Belt in central Africa. Dozens of Cu and Co minerals exist in the deposit as a result of the superposition and transformation of three complex ore-forming stages, including the sediment-hosted, hydrothermal, and oxidation periods; some of these minerals include heterogenite, carrollite, chalcocite, malachite, Co-containing malachite, spherocobaltite, Cu/Co-containing psilomelane, and Co-containing limonite. The mineralogy and processability properties among Co minerals differ considerably. The variability in Co minerals poses substantial challenges in establishing a universal beneficiation or extraction process that can accommodate all geometallurgical variations. The current Co-recovery process integrates flotation and magnetic separation techniques. However, the lack of fundamental knowledge about the spatial distribution of Co minerals and the poor adaptability of current Co-recovery processes to adapt to variable ores contribute to considerable Co losses in mine tailings. The recovery efficiency for Co is generally low, and the operational stability of the process is unstable. To address the issues, this study devised a geometallurgical model of Co in an ore body using Datamine and Leapfrog software for the first time. Initially, historical exploration data were collected, strata and mineralized domain models were developed, and the spatial variation in Co grade was preliminarily obtained. Subsequently, a sampling design was implemented to collect samples for process mineralogical research, effectively representing the Co-grade distribution within the strata and ore bodies. Furthermore, quantitative data of the mineral content and Co-occurrence state for each sample were obtained using a process mineralogical method, and these data were incorporated into the model using interpolation methods such as single-domain assignment and the distance inverse power ratio. As a result, five spatial beneficiation zones were obtained based on the spatial distribution of Co minerals with varying processability properties. These zones were classified as suitable for flotation (TYPE1), suitable for magnetic separation (TYPE2), suitable for combined magnetic separation and flotation (TYPE3), suitable for leaching (TYPE4), and difficult to recover (TYPE5); this classification resulted in the formation of a preliminary geometallurgical model. Finally, comprehensive samples were collected from the five beneficiation zones for the beneficiation experiments. The results revealed that the integrated magnetic separation and flotation process employed in the mine achieved varying Co-recovery efficiencies across the five beneficiation zones. This process proves applicable solely to the spatial domains of TYPE1, TYPE2, and TYPE3. The results also indicated that the classification of beneficiation zones in the geometallurgical model was within reason. Reasonable ore blending, based on the occurrence state of Co and the effective Co grade in the model, contributes to stabilizing current production and enhancing Co recovery. The developed geometallurgy model can be continuously optimized by adding sampling points or mineralogy parameters such as Co mineral particle size, mineral liberation degree, and Co-associated relationship with other minerals. The developed geometallurgy model serves as a valuable guide for the realization of classified mining and separation of Co ores in the SICOMINES mining region and for appropriate management.

       

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