Flocculation behavior, mechanics, and optimization of tailings based on multi-objective: Insight into the concentration and time-dependent floc size

Abstract

Tailings thickening is the primary link and key technology of cemented paste backfill (CPB) technology, in which flocculation conditions are important factors affecting the thickening effect. Conventional flocculation effects only consider macro indicators such as concentration and initial settling rate (ISR), ignoring the floc evolution and multi-objective optimization. Therefore, it leads to substandard underflow, frequent rake accidents, and untimely thickener regulation. In this study, the multi-objective optimization of the flocculation effect based on response surface methodology, Box-Behnken design, and the desirability function (RSM-BBD-DF) was carried out. Firstly, through the single-factor experiments, the sedimentation properties and floc size evolution under various influencing factors were analyzed. It is found that the evolution of floc size is time-dependent, which conforms to the asymmetric double sigmoidal (Asym2sig) model, and there is a linear relationship between test-ending floc size and ISR. The ISR increases with floc size. Through the RSM-BBD, the regression models of concentration and mean weighted chord length (MWCL) as response values were established. Multi-factor interaction found that the tailing feeding concentration (TFC) had the greatest influence on the concentration, and the shear rate (SR) had the greatest influence on the MWCL. The interaction between TFC and the flocculant solution concentration (FSC) influences the concentration most. The interaction between TFC and SR has the highest influence on the MWCL. By introducing the single desirability function and the overall desirability function, the optimization method based on RSM-BBD-DF obtained the optimal flocculation conditions (TFC is 17 wt%, FSC is 1 wt‱, SR is 160 r·min−1, and ST is 30 s), which were very close to the optimization results of the Design Expert, indicating that RSM-BBD-DF optimization method is reliable. Thus, RSM-BBD-DF has important application value for multi-objective optimization in mining and other industries,.Thus, RSM-BBD-DF provides a new approach for multi-objective optimization in mining and other industries, especially for intelligent control technology of thickening processes, which has important theoretical and practical value.