Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network  

作　　者：WANG Hong-bing XU An-jun AI Li-xiang TIAN Nai-yuan 

机构地区：[1]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China [2]Key Laboratory of Advanced Control of Iron and Steel Process of Ministry of Education,Beijing 100083,China [3]School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing,Beijing 100083,China

出　　处：《Journal of Iron and Steel Research International》2012年第1期11-16,共6页

基　　金：Sponsored by National Key Technology Research and Development in 11th Five Years Plan of China(2006BAE03A07);Fundamental Research Funds for Central University of China(FRF-AS-09-006B)

摘　　要：The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus 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 calcu- lated 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 polnomial 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.The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus 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 calcu- lated 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 polnomial 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.

关 键 词：basic oxygen furnace endpoint phosphorus content K-MEANS neural network GMDH 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP311[自动化与计算机技术—控制科学与工程]