通过研究高炉-转炉界面铁水运输过程温度的主要影响因素，确定了影响高炉-转炉界面铁水运输过程温度的参数，建立了基于Levenberg-Marquardt(LM)算法BP神经网络的高炉-转炉界面铁水温度及铁水过程温降的预报模型。用沙钢100包铁水数据进行模型训练，50包铁水数据进行现场预报，结果表明：在高炉-转炉界面“一包到底”模式下，当绝对误差│X│≤20℃时，铁水温度命中率为94%，铁水温降命中率为78%；当绝对误差│X│≤40℃时，铁水温度命中率为100%，铁水温降命中率为92%，该预报模型能够满足现场实际生产需求，对炼钢生产有很好的指导意义。
Through studying the main influencing factor of hot metal transportation process temperature for BF-BOF interface, the main parameters affecting temperature of hot metal transportation process for BF-BOF interface was determined, and a prediction model of hot metal temperature for BF-BOF interface was established based on Levenberg-Marquardt (LM) algorithm of BP neural network. The data of 100 ladles were used to training the model and the other 50 ladles were selected as the predictive samples. It is shown that: under the model of “one hot metal ladle going through process” for BF-BOF interface, when the absolute error│X│≤20℃, the temperature of hot metal is shooting 94%, the hit rate of temperature drop of hot metal is 78%; when the absolute error│X│≤40℃, the temperature of hot metal is shooting 100%, the hit rate of temperature drop of hot metal is 92%, this prediction model can meet the actual production needs and can provide a very good guide to steel-making production.
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