A Hybrid Intelligent Soft-Sensor Model for Dynamic Particle Size Estimation in Grinding Circuits

The purpose of this paper is to develop an on-line soft-sensor for dynamic estimation of the particle size distribution of hydrocyclones overflow in consecutive grinding process. The hybrid model based soft-sensor is based on the following model structures: 1. a neural net-based dynamic model of state space description for hydrocyclone with a neural net-based model for classifier and a population balance model for ball mill and sump, 2. an ANFIS-based model mainly for abnormal operating conditions, 3. a fuzzy logic coordinator for the final predictive result according to output values of aforementioned models. The fact that the soft-sensor performs well in particle size estimation demonstrates that the proposed hybrid intelligent soft-sensor model is effective for dynamic estimation of particle size distribution.