Data-driven modelling of the flocculation process on mineral processing tailings treatment

The clarification of tailings slurry using polymer flocculants has been widely used in the mining industry to promote the cleaner production of mineral resources. In this paper, a data-driven prediction model was proposed using gradient boosting machine (GBM) for the non-linear relationship modelling and firefly algorithm (FA) for GBM hyper-parameters tuning. Two studies were performed, among which the main study omitted the influence of chemical characteristics of mineral processing tailings (MPT) while the supplementary study considered. For the main study, 27 types of MPT and 4 types of anionic flocculants were used to prepare the dataset. The flocculation performance was represented by the initial settling rate (ISR) and its influencing variables were selected to be the particle size distribution (PSD) of MPT, the solids content of tailings slurry, the flocculants type, and the flocculants dosage. For the supplementary study, the chemical characteristics of 7 types of MPT were also considered as influencing variables and its influence on the predictive performance of GBM was investigated. The main study shows that the optimum GBM model achieved a correlation coefficient of 0.841 between the predicted and experimental ISR values on the testing set, denoting it was robust in predicting the ISR of the flocculation. Compared with the solids content, the flocculants dosage and the flocculants type, the PSD of MPT was found to be the most significant influencing variable for the flocculation with an importance score of 0.420 out of 1. The supplementary study shows that the predictive performance of GBM could be improved considering chemical compositions of MPT, which were also important influencing variables for the flocculation process.