（1. State Key Laboratory of Advanced Metallurgy， University of Science and Technology Beijing， Beijing 100083， China 2. Iron Department， Shouqin Metal Materials Co.， Ltd.， Qinhuangdao 066000， Hebei， China）
Abstract：To solve the problem of neglevting the high temperature characteristics of iron ore powder in sinter forecast model， the assimilation reaction characteristic number and liquidity characteristic number which reflect the high temperature performance of iron power are added into the model. BP neural network is used to establish prediction model of sinter performance. The sinter indexes that affects the production of blast furnace were chose as the output. The high temperature and the physical and chemical properties of the iron ore powder were analyzed as input. Thus， the prediction model was established by BP neural network， and the algorithm of BP neural network was optimized. The structure of BP neural network for prediction model was 8-17-4. After training the neural network ， the prediction accuracy of the predicted characters was more than 85%， which meant that the neural network had good accuracy and adaptability.
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ZHAO Lu-peng，WU Keng，ZHU Li，，CHEN Xiao-min，QIN Xuan-ke. Prediction model of sinter properties based on BP neural network. Iron and Steel, 2017, 52(9): 11-15.
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2017, Vol. 52 
