Intelligent multivariable modeling of blast furnace molten iron quality based on dynamic AGA-ANN and PCA

Abstract

Blast furnace (BF) ironmaking process has complex and nonlinear dynamic characteristics. The molten iron temperature (MIT) as well as Si, P and S contents of molten iron is difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. A nonlinear multivariate intelligent modeling method was proposed for molten iron quality (MIQ) based on principal component analysis (PCA) and dynamic genetic neural network. The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network (ANN). A dynamic feedback link was introduced to produce a dynamic neural network on the basis of traditional back propagation ANN. The proposed model improved the dynamic adaptability of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system. Moreover, a new hybrid training method was presented where adaptive genetic algorithms (AGA) and ANN were integrated, which could improve network convergence speed and avoid network into local minima. The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback information for realizing close-loop control for MIQ. Industrial experiments were made through the proposed model based on data collected from a practical steel company. The accuracy could meet the requirements of actual operation.