基于冶炼强度分类的高炉煤气利用率时间序列预测模型  

Time-series prediction model of gas utilization rate in a blast furnace considering smelting intensity classification

作　　者：郭云鹏 安剑奇 赵国宇[2,3,4] GUO Yunpeng;AN Jianqi;ZHAO Guoyu(School of Automation,China University of Geosciences,Wuhan 430074,China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan 430074,China;Engineering Research Center of Intelligent Technology for Geo-Exploration,Ministry of Education,Wuhan 430074,China;School of Future Technology,China University of Geosciences,Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)自动化学院,湖北武汉430074 [2]复杂系统先进控制与智能自动化湖北省重点实验室,湖北武汉430074 [3]地球探测智能化技术教育部工程研究中心,湖北武汉430074 [4]中国地质大学(武汉)未来技术学院,湖北武汉430074

出　　处：《冶金自动化》2024年第2期60-73,共14页Metallurgical Industry Automation

基　　金：国家自然科学基金面上项目(62373336,61973287);高等学校学科创新引智计划项目(B17040)。

摘　　要：冶炼强度(smelting intensity, SI)影响高炉内部的物理化学反应,煤气利用率(gas utilization rate, GUR)与送风参数之间的关系随着SI的变化而变化。忽略SI,即忽略了GUR与送风参数之间的动态变化关系,对利用送风参数的GUR预测产生不利影响。本文提出了一种考虑SI分类的GUR预测模型。首先,从铁水熔炼机理的角度评价SI对高炉状态参数的影响。其次,提出一种基于状态参数的加权核模糊c均值聚类方法(weighted kernel fuzzy C-means method, WKFCM)对SI进行分类。再次,利用监督主成分分析(supervised principal component analysis, SPCA)对输入数据进行降维并基于支持向量回归(support vector regression, SVR)对GUR的发展趋势进行预测。最后,利用该模型对不同SI下的真实GUR数据进行了预测。对实际生产数据的分析表明,考虑SI分类的预测方法更适用于高炉复杂的生产环境中GUR时间序列的预测。The smelting intensity(SI)affects the physical and chemical reactions within the blast furnace,causing the relationship between the gas utilization rate(GUR)and the blast supply parameters undergoes variations with changes in SI.Disregarding the SI means neglecting the dynamic correlation between GUR and blast supply parameters,resulting in adverse effects on predicting GUR using blast supply parameters.This paper introduces a GUR prediction model that takes into account the classification of SI.Firstly,the impact of SI on the state parameters of blast furnaces is evaluated from the perspective of molten iron smelting mechanisms.Then,a weighted kernel fuzzy c-means clustering method(WKFCM)based on state parameters is proposed to classify the SI.Subsequently,supervised principal component analysis(SPCA)is employed to reduce the dimensionality of the input data and a support vector regression(SVR)method is used to predict the development trend of GUR.Finally,the model is applied to predict real GUR data under different SI.Analysis of actual production data indicates that the prediction method considering SI classification is more suitable for forecasting GUR time series in the complex production environment of blast furnaces.

关 键 词：冶炼强度 加权核模糊c 均值聚类 监督主成分分析 煤气利用率预测 

分 类 号：TF54[冶金工程—钢铁冶金]