Enhancing flotation beneficiation efficiency of complex ores using ionometry methods

Abstract

Flotation beneficiation plays a leading role in the processing most ores. The efficiency of this process is ensured by the correct selection of operating modes, which involves choosing the most selective reagents and determining their optimal consumption. Despite the significance of this issue, the classic approach to determining beneficiation parameters involves testing followed by the processing of the results obtained and the determination of the reagent consumption. However, such studies do not reveal the essence of the physicochemical processes occurring within the pulp, and the results of testing one sample may not correspond to the optimum when the properties of the sample change.The purpose of this work is to develop and implement a methodological approach to the study of ore flotation beneficiation using ionometry methods. The data obtained from ion-selective sensors significantly deepen our insight into the transformations occurring during the flotation process and allow for consideration of possible adverse factors that hinder effective process progression.To achieve this goal, a comparative analysis of two approaches to flotation beneficiation testing was performed using complex sulfide ores as examples. In the first stage, a flotation beneficiation study was conducted through D-optimal factor testing, which included 20 individual tests to determine the optimal consumption of modifying reagents, yielding qualitative indicators. In the second stage, flotation tests were conducted using electrochemical monitoring with pH, Ag2S, Pt, and membrane electrodes. A universal flowchart for flotation studies with ion-selective sensors has been developed, facilitating the application of this approach to various ores. The implementation of the results from this comparative analysis has led to a 7.8% increase in beneficiation efficiency while reducing reagent consumption. Additionally, the insights gained into the electrochemical processes occurring allowed for assumptions about the adverse factors affecting flotation outcomes. In conclusion, a model for the potential application of this approach at existing enterprises was proposed, including the implementation of an “intelligent assistant” for flotation operators based on the developed electrochemical models.