Application of statistical analysis, Deng’s relevancy and BP neural network for predicting molten iron sulfur in COREX process

Abstract

Abstract The high and fluctuation property of sulfur content in hot metal (HM) is a prominent problem in COREX process. The precise prediction of sulfur content in HM of COREX can provide a theoretical basis and technical reference for stabilizing and reducing the sulfur content in HM. In this work, a three-layer back propagation (BP) neural network was developed to predict the sulfur content in HM of COREX. The main affecting factors regarded as input parameters of the model are determined by the statistical analysis, and the hysteretic heats corresponding to each input parameters are analyzed by calculating the Deng’s correlation degree. The final structure of the BP neural network model is 20-37-1. The results show that when the error of BP neural network model for predicting sulfur content in HM of COREX is ±0.1%, the hit rate is 86.7%. In order to further improve the prediction accuracy of the model, the time series process method is adopted to update the training samples in real time and optimize the model. When the prediction error of the improved model is ±0.1%, the hit rate increases to 96.7%. The improved BP model can provide technical guidance for judging the change of sulfur content in HM and subsequent operations.