论文部分内容阅读
描述了一种高炉煤气发生量软测量方法,通过冶金企业能源管控系统采集高炉运行数据对高炉运行状态进行计算,将状态分为休风、减风和正常三类,当高炉处于休风和正常状态时,建立基于高炉煤气产生机理的发生量软测量模型;当高炉处于减风状态时,通过互信息分析提取非线性特征向量,建立基于智能参数优化回归分析的高炉煤气发生量软测量模型。以某钢铁企业1号高炉为例对高炉煤气发生量软测量应用效果进行分析,结果表明该方法有效结合了高炉工艺状态和高炉炉况信息,软测量灵敏度高,结果准确,为煤气调度提供了很好的数据支撑。
Described a method of soft gas measurement of blast furnace gas through blast furnace operation data collected by the metallurgical enterprise energy management system to calculate the blast furnace operating status, the state is divided into off, down and normal three categories, when the blast is off and normal State, a soft-sensing model based on blast furnace gas generation mechanism was established. When the blast furnace was in the wind-down state, the non-linear eigenvector was extracted through mutual information analysis and a soft sensor model of blast furnace gas production based on intelligent parameter optimization regression was established. Taking the No.1 BF of an iron and steel company as an example, the application effect of the blast furnace gas volume soft sensing was analyzed. The results show that the method effectively combines the blast furnace process status and blast furnace condition information, and the soft sensing sensitivity is high and the result is accurate. Very good data support.