MICROFLOTATION OF GOLD-BEARING ORE: WITHIN-BATCH EFFECTS AND GROUP-AWARE CLASSIFICATION OF REAGENT REGIMES
DOI:
https://doi.org/10.26577/jpcsit5320268Ключевые слова:
microflotation, gold-bearing ore, reagent regime, grouped cross-validation, machine learning classificationАннотация
Fine particles below approximately 20 µm are recovered poorly in conventional flotation, and microbubble-assisted methods are among the options proposed to address this. This paper reports a statistical assessment of multicomponent microflotation (MMF) and an auxiliary classification of reagent regimes, based on 132 laboratory flotation tests on gold-bearing feed from an operating concentrator. The tests formed 14 daily batches, each from a separate feed sample, comprising 101 microflotation and 31 baseline tests. Because feed properties varied between days, the effect was assessed within batches rather than across the pooled sample. Median Au recovery was higher under microflotation in 10 of 14 batches, with a median within-batch increase of 2.275 percentage points (cluster bootstrap 95% CI 0.025-3.650; Wilcoxon signed-rank p=0.0166). The corresponding change in enrichment efficiency did not reach significance at α=0.05 and is reported only as a tendency. Textual reagent regimes were converted into tabular features, and regimes with Au recovery of at least 84% were classified under five-fold GroupKFold validation by daily batch. A nested weighted ensemble reached ROC-AUC 0.717 (95% CI 0.632-0.792), indicating moderate discrimination rather than accurate prediction.





