Flotation Concentrate Grade Prediction Model Based on RBF Neural Network Immune Evolution Algorithm

摘要

In the process of mineral flotation, the foam in different state represents different concentrate grade. According to this feature, a kind of concentrate grade prediction model (CGPM) was proposed based on the foam image characteristic (FIC). Using RBF neural network based on simulated annealing and fuzzy c-mean clustering algorithm, we established the prediction model between FIC parameter and concentrate grade, and then the model parameters were optimized by immune evolution algorithm (IEA) to improve the model accuracy. The simulation test shows that the model is higher in accuracy and stronger in practicability and robustness, and can give effective guidelines to flotation follow-up dosing control and technical and economic indexes assessment.