基于最优工况迁移的高炉铁水硅含量预测方法  

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

作　　者：蒋朝辉[1,2] 许川 桂卫华 蒋珂[1] JIANG Zhao-Hui;XU Chuan;GUI Wei-Hua;JIANG Ke(School of Automation,Central South University,Changsha 410000;Peng Cheng Laboratory,Shenzhen 518000)

机构地区：[1]中南大学自动化学院,长沙410000 [2]鹏城实验室,深圳518000

出　　处：《自动化学报》2022年第1期194-206,共13页Acta Automatica Sinica

基　　金：国家自然科学基金(61773406,61988101);中南大学中央高校基本科研任务业务费专项资金(2020zzts572)资助~~。

摘　　要：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,针对冶炼过程的大时滞特性,定义相邻时间节点间的硅含量工况迁移代价函数,并提出多源路径寻优算法,实现冶炼过程中硅含量最优工况迁移路径及当前时刻硅含量最优预测值的求解.最后,基于工业现场数据验证了所提方法的有效性与准确性.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.

关 键 词：高炉炼铁 铁水硅含量 预测 工况迁移 密度峰值聚类 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TF53[自动化与计算机技术—计算机科学与技术]