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

汪红兵; 徐安军; 艾立翔; 田乃媛

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钢铁研究学报（英文版） ›› 2012, Vol. 19 ›› Issue (1) : 11-16.

工艺

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

WANG Hong-bing1,2,XU An-jun3,AI Li-xiang3,TIAN Nai-yuan3

作者信息 +

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

WANG Hong-bing1,2,XU An-jun3,AI Li-xiang3,TIAN Nai-yuan3

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摘要

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.

Abstract

关键词

Basic Oxygen Furnace / Group Method of Data Handling / K-means / Neural Network / Entropy Weight Method

Key words

Basic Oxygen Furnace / Group Method of Data Handling / K-means / Neural Network / Entropy Weight Method

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汪红兵, 徐安军, 艾立翔, 等. Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network[J]. 钢铁研究学报（英文版）, 2012, 19(1): 11-16

WANG Hong-Bing, XU An-Jun, YI Li-Xiang, et al. Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network[J]. Journal of Iron and Steel Research International, 2012, 19(1): 11-16

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