Research and application of kernel extreme learning machine in flotation recovery rate

The flotation recovery rate is an important index in the process of flotation. The flotation recovery rate is obtained by manual detection, which has a large time delay, so that workers can not effectively control the production to make the corresponding adjustment. Due to the complexity of the flotation process, the high variable dimension, strong correlation, large noise and incomplete detection signal, it is difficult to establish a more accurate prediction model of recovery rate. However, artificial intelligence and machine learning technology can establish based on data driven model of complex system in the case of unknown mechanism and incomplete information. Therefore, in order to improve the efficiency and effectiveness of the detection of the recovery rate, this paper proposes a prediction model based on the establishment of the flotation recovery rate based on the analysis of the factors affecting the flotation process. The simulation results show that the proposed method can effectively identify the nonlinear relationship between the input data and the recovery rate, and has higher prediction accuracy and training performance.