Prediction method of silicon content in blast furnace hot metal based on IPSO-HKELM

Abstract

Aiming at the problem that the silicon content of molten iron can not be detected online, a model for predicting silicon content in molten iron based on Hybrid Kernel Extreme Learning Machine optimized by Improved Particle Swarm Optimization Algorithm (IPSO-HKELM) is proposed. Firstly, the input variables are reduced by PCA, and then the prediction model of molten iron content based on HKELM is established. In this paper, PSO is used to optimize the kernel parameters of HKELM. Aiming at the problem that PSO is easy to fall into local optimum, the Inertia weight reduced with the number of iterations and the random back-based learning mutation operation are introduced, so that PSO can jump out of the local minimum point more easily and get the optimal result. Experiments show that the prediction model of silicon-based silicon content based on IPSO-HKELM has high prediction accuracy and short time, which can meet the actual production needs.