Quantitative evaluation of collector flotation performance I: The creation of a flotation index based on mineral recovery

Abstract

Collectors are crucial for successful flotation, and their performance evaluation is one of the most important steps in their development process. It is urgent to create new evaluation indices/methods because the existing indices/methods are not ideal in terms of standardization and universality. Collector flotation performance is conventionally evaluated using two parameters, i.e., collecting ability and selectivity. Thus, a two-dimensional evaluation space exists. Through the creation and normalization of a flotation index (FI), based solely upon mineral recovery, this evaluation space may be reduced to one-dimension, in the range of 0 ∼ 1 and verified mathematically. In the construction of FI, the relative recovery (Rr; collector-induced recovery) of the target mineral replaces the absolute recovery (R; used for the existing indices/methods), quantifying actual collecting ability and excluding the adverse effects of variable minerals’ natural or inherent floatability (Rn). Moreover, selectivity is quantified by the recovery gap (ΔR) between the target and gangue minerals. Compared with separation efficiency (SE; a conventional evaluation index), FI fills a gap in collector performance evaluation for single minerals by a convenient process. Additionally, FI can qualitatively/semi-quantitatively evaluate collector flotation performance for mineral mixtures and industrial ores based on the evaluation results for single minerals, reducing laboratory costs. It should be noted that if FI is directly used to calculate collector flotation performance for mineral mixtures and industrial ores, grade analysis is indispensable. The normalized and dimensionless FI is more universal, providing a standardized comparison of collector flotation performance for different flotation systems, different minerals/ores or the same mineral/ore from different mining areas. As the scoring function of flotation performance, FI is the basis for the subsequent construction of a standardized method to comprehensively and quantitatively predict collector flotation performance in combination with quantitative structure–activity relationship (QSAR), machine learning (ML) and Quantum chemistry (QC).