摘要
高炉煤气流的控制是高炉操作的重要方面 ,本文开发了自动识别炉喉温度分布模式的自组织神经元网络 ,利用大量炉顶十字测温数据归纳整理出 2 5种温度分布模式 ,并自动将当前的温度分布归类为其中的某一种模式。将模型识别的温度分布模式与炉体热负荷等检测信息进行综合分析 ,能够对高炉的煤气流分布做出更准确的判断 。
The control of gas flow distribution is an imp ortant aspect of blast f urnace operation. This paper developed a self-organization neural network for au tomatically recognizing the mode of temperature distribution at the furnace thro at. Twenty five modes of temperature distribution were figured out by using a bi g number of measured data of the cross probe and the current temperature distrib ution is classified into one of these modes. This recognized mode of temperature distribution was used together with furnace heat load and so on for conducting systematical analysis and it is able to make more precise diagnosis for gas dist ribution of the blast furnace and provide valuable guidance for foremen in the regulation of charging patterns.
出处
《河南冶金》
2004年第1期10-12,20,共4页
Henan Metallurgy
