Abstract: The prediction model of end point temperature of molten steel refining in a 40 t ladle furnace has been developed bya modified artificial neural network calculation method. Compared with traditional Back-Propagation (BP) network calculation method, the modified artificial calculation method can increase prediction efficiency and precision. The examination in production situ showed that using modified BP artificial neural network calculation method, the heats percentage with ±5 ℃ error of prediction temperature of molten steel was 90% , while using traditional BP artificial neural network calculation method, that with ±5 ℃ error of prediction temperature only 77% .

Key words:  , LF Refining, Back-Propagation Neural Network, Temperature of Molten Steel, Prediction

摘要： 采用改进的人工神经网络算法,开发了40t钢包炉精炼时钢水终点温度预报模型。与传统BP网络算法相比较,改进算法可提高预测速度和精度。生产现场实验表明,传统BP神经网络算法,钢水温度预测误差±5℃的炉次仅为77%,用改进的BP神经网络算法,其误差±5℃的炉次为90%。

关键词: LF精炼, BP神经网络, 钢水温度, 预测