针对LF精炼操作对人工经验过度依赖的问题,马鞍山钢铁有限公司长材事业部基于冶金机理,在120 t LF上开发了温度模型、合金模型、吹氩模型和造渣模型,建立以钢水温度、成分、炉渣三者相互统一的控制模型,利用大数据技术和自学习功能对控制模型进行优化,实现了各模型协同集成和LF智能控制,取得了良好的应用效果,LF自动控制比例达到80％,终点目标温度±5 ℃命中率达95％以上,终点成分窄范围命中率(w(Si)±0.02％、w(Mn)±0.02％、w(S)±0.001％、w(Als)±0.005％)达97％以上,降低LF精炼电耗约4 kWh/t、减少精炼处理时间约5 min,提高了生产效率和钢水质量,对炼钢工序降本增效起到了重要作用.
Development and application of intelligent control system for 120 t LF
Aiming at the problem of the over dependence of LF refining operation on manual experience,based on metallurgical mechanism,temperature model,alloy model,argon blowing model,and slag making model were developed on the 120 t LF of the Long Product Division of Maanshan Iron and Steel Co.,Ltd.And a control model was established based on the mutual unity of molten steel temperature,composition and slag.The big data technology and self-learning function were used to optimize the control model,the collaborative integration of various models and the whole process intelligent control of LF was realized,and good application results were achieved.The proportion of LF automatic control reached 80％,the hit rate of endpoint target temperature±5 ℃was more than 95％,the hit rate of narrow range of endpoint component(w(Si)±0.02％、w(Mn)±0.02％、w(S)±0.001％、w(Als)±0.005％)was above 97％,the power consumption of LF refining was reduced by about 4 kWh/t,the refining treatment time was reduced by about 5 min,and the production efficiency and molten steel quality were improved.It played an important role in reducing cost and increasing efficiency during steelmaking process.
LF refiningintelligent controlmodelintegrationbig data
万方数据
维普
