摘要
LF精炼炉是一种多功能的炉外精炼设备,在我国钢铁行业得到广泛的应用,其中LF精炼温度控制对产品的质量和连铸环节的顺行十分重要。介绍了采用基于机理模型和BP神经网络算法的LF精炼温度预报模型在企业的使用实例,其温度预报在±5℃范围内占比为91.95%,其温度预报的准确性为生产冶炼过程具有一定的指导意义;模型投入使用后平均吨钢节电约9.7 kW·h,降低了生产成本;本文为LF温度预报模型的发展提供一定的参考价值。
Refining furnace(LF)is a kind of multifunctional refining equipment,which is widely used in China's iron and steel industry,and LF refining temperature control is very important to the quality of products and the continuous casting process.This paper introduces the application of the mechanism model and the BP neural network algorithm in LF refining temperature prediction in an enterprise.The proportion of the temperature prediction model is 91.95%within the range of±5℃,and its prediction accuracy is a major guiding significance for the production and smelting process.After the model used,the electricity saving per ton of steel is about 9.7 kw·h,and the production cost is reduced.This paper also provides a certain reference value for the development of LF temperature prediction model.
作者
赵舸
王雪明
何赛
ZHAO Ge;WANG Xueming;HE Sai(Metallurgical Technology Institute,Central Iron and Steel Research Institute,Beijing 100081,China;Electric Furnace Workshop No.3,Jiangsu Shagang Group Co.,Ld.,Suzhou 215625,China)
出处
《山东冶金》
CAS
2023年第6期34-36,共3页
Shandong Metallurgy
关键词
LF精炼炉
温度预报模型
机理模型
BP神经网络算法
refining furnace(LF)
temperature prediction model
mechanism model
BP neural network algorithm
作者简介
赵舸,男,1979年生,硕士研究生,现为钢铁研究总院有限公司高级工程师,主要从事钢铁企业电炉炼钢、工艺模型开发等方面工作。
