To handle the multiscale and dynamic characteristics of blast furnace ironmaking process

a soft sensor model based on empirical mode decomposition (EMD) and Elman neural network is proposed. First

the original silicon content dataset is decomposed into a finite collection of intrinsic mode functions (IMFs) and a residue by EMD

obtaining relatively stationary sub-series from original data set. Second

each IMF and the residue are utilized to establish the corresponding Elman neural network model. To further improve the accuracy of prediction

the result of each sub-series is multiplied by a weight and then summed up to obtain the final silicon content. Here

all the weights are optimized by particle swarm optimization (PSO). The model was applied to the prediction of silicon content of blast furnace in a steel mill

and the result proved the effectiveness of the proposed method.