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Prediction model of end-point phosphorus content for BOF based on monotone-constrained BP neural network

Journal of Iron and Steel Research International 2022年第5期29卷 751-760页

作者： Kai-xiao Zhou Wen-hui Lin Jian-kun Sun Jiang-shan Zhang De-zheng Zhang Xiao-ming Feng Qing Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Xinyu Iron and Steel Co. Ltd.Xinyu338001JiangxiChina

Dephosphorization is essential content in the steelmaking process,and the process after the converter has no dephosphorization ***,phosphorus must be removed to the required level in the converter *** order to better control the end-point phosphorus content of basic oxygen furnace(BOF),a prediction model of end-point phosphorus content for BOF based on monotone-constrained backpropagation(BP)neural network was *** the theoretical analysis of the dephosphorization process,ten factors that affect the end-point phosphorus content were determined as the input variables of the *** correlations between influencing factors and end-point phosphorus content were determined as the constraint condition of the model.200 sets of data were used to verify the accuracy of the model,and the hit ratios in the range of±0.005%and±0.003%are 94%and 74%,*** fit coefficient of determination of the predicted value and the actual value is 0.8456,and the root-mean-square error is 0.0030;the predictive accuracy is better than that of ordinary BP neural network,and this model has good *** can provide useful reference for real production and also provide a new approach for metallurgical predictive modeling.

关键词： Converter end-point phosphorus content Monotonic constraint BP neural network Prediction model Steelmaking

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