电弧炉炼钢全过程钢水碳质量分数动态预报模型  

Dynamic Forecast Model of Carbon Content in Molten Steel in EAF Steelmaking Process

作　　者：宋水根[1] 刘花[1] 曾繁林[1] 

机构地区：[1]新余钢铁集团有限公司,江西新余338013

出　　处：《中国冶金》2013年第12期25-28,共4页China Metallurgy

摘　　要：根据电弧炉物料平衡理论与利用BP神经网络的方法,建立了理论模型结合神经网络的电弧炉炼钢全程钢水碳质量分数实时预测模型。通过模型得出冶炼过程中碳质量分数变化曲线,实现对全程钢水碳质量分数的实时监控。在接近冶炼终点时,由于脱碳反应中碳氧积的存在,因此模型对影响终点碳质量分数的因素进行分析,采用BP神经网络方法进行预测,满足了对电弧炉冶炼终点碳质量分数预报准确度的要求。In this paper, based on the electric arc furnace material balance theory and the method of bp neural net- work, the real-time forecasting model of carbon content of molten steel in electric arc furnace steelmaking is set up with theoretical model and neural network. Through the model draw curves of carbon content in the smelting process, the carbon content of molten steel in the real-time monitoring is implemented. Because of the end-point car- bon-oxygen equilibrium in decarburization reaction, the influencing factors of end point carbon content are analyzed. The forecast method based on BP neural network has reached accuracy requirements of EAF end-point carbon content prediction.

关 键 词：终点碳 BP神经网络 动态 理论模型 

分 类 号：TF741.5[冶金工程—钢铁冶金]