基于最优工况迁移的高炉铁水硅含量预测方法

高炉铁水硅含量是铁水品质与炉况的重要表征,冶炼过程关键参数频繁波动及大时滞特性给高炉铁水硅含量预测带来了巨大挑战.提出一种基于最优工况迁移的高炉铁水硅含量预测方法.首先,针对过程变量频繁波动问题,提出基于邦费罗尼指数的自适应密度峰值聚类算法,实现对高炉冶炼过程变量的工况划分,并建立不同工况硅含量预测子模型.其次,针对冶...

Prediction Method of Hot Metal Silicon Content in Blast Furnace Based on Optimal Smelting Condition Migration

The hot metal silicon content in blast furnace can characterize the hot metal quality and the condition of blast furnace. It poses a great challenge to the online prediction of silicon content because of the frequent fluctuation of smelting parameters and the existence of large time delay during the ironmaking process. This paper proposes an algorithm for predicting the hot metal silicon content in blast furnace based on optimal smelting condition migration. Firstly, arming at the frequent fluctuation of smelting process variables, an adaptive density peak clustering algorithm based on the Bonferroni index to dynamically cluster the process variables of blast furnace ironmaking process is proposed, which can obtain clusters of different smelting conditions, and establish sub-models for different smelting conditions. Secondly, to mitigate the large time delay of blast furnace ironmaking process, this paper defines the silicon content migration cost function between adjacent time nodes, and proposes a multi-source path optimization algorithm to solve the optimal migration path of silicon content during the smelting process and the optimal prediction value of silicon content at the current time. Finally, the effectiveness and accuracy of the proposed method are verified based on the industrial field data.