摘要
步进式加热炉出钢节奏辩识技术是加热工艺优化的基础,对于选择最优加热曲线、炉内各个区域最优温度设定、加热炉可比能耗分析都具有重要意义。以递推法和自学习法系统地论述了加热炉出钢节奏的计算过程。递推法从轧线轧制节奏、加热炉步进机械以及加热能力三个角度出发,分析其对各个加热炉出钢节奏的影响,同时增加了辊道传输时间、装出料操作时间对步进机械动作影响等因素,使理论模型更加符合生产实际。在复杂生产条件下,递推法会受到各种因素的制约,影响其预测的准确性,而出钢节奏自学习法以统计学习为基础,通过样本采集、分析与专家数据库相结合的方法,可实时准确地预测炉群中各个加热炉的出钢节奏,以适应复杂工况的要求。
Discharging pacing forecast is a basic technology to optimize the heating process of Walking beam furnace, also be of great significance to optimal heating curve selection, optimal temperature setting of different regions in the furnace and comparable energy consumption analysis. Systematically discussed the calculation process of furnace＇s discharging pacing by using recursive method and self - learning method. The recursive method analyzed the influence on discharging pacing made by the fol- lowed factors： pacing of mill line, actions of walking beam machine and heating capacity of furnace, and also discussed the factors of roller transmission time and the impact of charging and discharging time on walking machine~ actions to make the theoretical model more in line with the actual situation. Discharging pacing self - learning is a method based on statistical learning, which can accurately forecast each furnace discharging interval by combining sample collection and analysis with expert database.
出处
《冶金能源》
2016年第4期55-59,共5页
Energy For Metallurgical Industry
关键词
加热炉
出钢节奏
轧制节奏
自学习
heating furnace discharging pacing mill pacing self-learning
