Modeling of Grinding Particle Size Soft Sensor Based on the Adaptive Natural Gradient Method in the Gauss Process

The online detection of the particle size is of great significance to realize the optimizing control of the grinding process and to improve the grade of concentrated ore and metal recovery rate. However, the problem of the present instrument is that the particle size cannot meet the real-time detection due to the long measurement period. Based on the characteristics of the typical two stage grinding circuits, this paper puts forward the grinding particle size soft sensor modeling method based on Gaussian process (GP), and the adaptive natural gradient (ANG) is applied to the super Gaussian process parameter optimization of the process. Then, the model of grinding particle size soft sensor was built based on ANG-GP. Soft sensor simulation experiment was carried out comparative study with the BP neural network and support vector machine model, respectively. Results show that this method is superior to the other methods, this method has high prediction accuracy, and it is effective to online detection of grinding particle size, which shows the effectiveness of this method.