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 

作者机构：School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory of Advanced Control of Iron and Steel Process of Ministry of EducationBeijing 100083China School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijing 100083China 

出 版 物：《Journal of Iron and Steel Research International》 (国际钢铁研究杂志)

年 卷 期：2012年第19卷第1期

页      面：11-16页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

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) 

主　　题：basic oxygen furnace endpoint phosphorus content K-means neural network GMDH 

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.