As part of a study investigating the influence of mineralogical variability in a sediment hosted copper–cobalt deposit in the Democratic Republic of Congo on flotation performance, the flotation of nine sulphide ore samples was investigated through laboratory batch kinetics tests and quantitative mineral analyses. Using a range of ore samples from the same deposit the influence of mineralogy on flotation performance was studied. Characterisation of the samples through QEMSCAN showed that bornite, chalcopyrite, chalcocite and carrollite are the main copper-bearing sulphide minerals while carrollite is the only cobalt-bearing mineral. Mineralogical characteristics were averaged per sample to allow for a quantitative correlation with flotation performance parameters. Equilibrium recoveries, rate constants and final grades of the samples were correlated to the feed mineralogy through Multiple Linear Regression (MLR). Target sulphide minerals content and particle size, magnesiochlorite content, carrollite liberation and association of the copper and cobalt minerals with magnesiochlorite and dolomite were used to predict flotation performance. Leave One Out Cross Validation (LOOCV) revealed that the final copper and cobalt grades are predicted with an R² of 0.80 and 0.93 and Root Mean Square Error of Cross Validation (RMSECV) of 4.41% and 1.34%. The recovery of cobalt and copper with time can be predicted with an R² of 0.94 for both and an overall test error of 4.70% and 5.14%. Overall, it was shown that quantitative understanding of changes in mineralogy allows for prediction of changes in flotation performance.

中文翻译：

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沉积物沉积的铜钴硫化物矿石浮选性能的矿物学预测

作为调查刚果民主共和国沉积物中铜钴钴矿床矿物学变异性对浮选性能影响的研究的一部分，通过实验室批量动力学测试和定量矿物分析研究了九种硫化矿石样品的浮选。使用来自同一矿床的一系列矿石样品，研究了矿物学对浮选性能的影响。通过QEMSCAN进行的样品表征表明，斑铜矿，黄铜矿，黄铜矿和红柱石是主要的含铜硫化物矿物，而红柱石是唯一的含钴矿物。将每个样品的矿物学特征取平均值，以便与浮选性能参数进行定量关联。平衡回收率 通过多元线性回归（MLR）将样品的速率常数和最终品级与饲料矿物学相关联。使用目标硫化物矿物含量和粒度，氯化镁含量，红柱石释放以及铜和钴矿物与氯化镁和白云石的结合来预测浮选性能。留一法交叉验证（LOOCV）显示最终的铜和钴品位用R预测^2分别为0.80和0.93，交叉验证的均方根误差（RMSECV）为4.41％和1.34％。可以预测钴和铜随时间的回收率，两者的R ²均为0.94，总测试误差为4.70％和5.14％。总的来说，表明对矿物学变化的定量了解可以预测浮选性能的变化。