Wang H.-B.,University of Science and Technology Beijing |
Wang H.-B.,Key Laboratory of Advanced Control of Iron and Steel Process of Ministry of Education |
Xu A.-J.,University of Science and Technology Beijing |
Ai L.-X.,University of Science and Technology Beijing |
Tian N.-Y.,University of Science and Technology Beijing
Journal of Iron and Steel Research International | Year: 2012
The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phosphorus content of molten steel in BOF (Basic Oxygen Furnace). At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calculated using EWM (Entropy Weight Method). At the predicting stage, one GMDH (Group Method of Data Handling) polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polynomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network. © 2012 Central Iron and Steel Research Institute.