Abstract
Reagent addition is an important operation in the froth flotation process. In most plants, it is manually regulated according to the operator’s experience, by observing the surface features of the froth. Due to the drawbacks of manual operation, large fluctuations in the process are common, resulting in unexpected process indexes. Thus, we investigated the relationship between reagent addition, feed conditions (including ore properties, slurry density, and slurry flow rate), and froth image features based on the mechanism of froth flotation and production technology of gold-antimony flotation. Then, we proposed a two-step optimal-setting control strategy for reagent addition, which included a basic dosage pre-setting model and a feedback reagent addition compensation model. According to operating conditions and ore properties, the pre-setting model was developed using a belief rule base (BRB) method based on an evidential reasoning approach (RIMER), which could effectively address the uncertainties of operator experience and historical data. The model parameters of the BRB were then optimized using a state transition algorithm (STA). In terms of the offsets of the froth image features, the feedback compensation model using rule-based reasoning (RBR) was built. Simulation results using a STA-optimized BRB, GA-optimized BRB, least squares support vector machine (LSSVM), and artificial neural network (ANN) were compared. Finally, industrial test results confirmed that the reagent addition system based on the proposed method could satisfy the requirements for automatic reagent addition in an industrial production environment. This is of great significance for improving the production efficiency of flotation plants.