数据驱动的多时间尺度高炉煤气利用率模型预测控制  

Data-driven multi-time scale model predictive control for blast furnace gas utilization rate

作　　者：安剑奇[1,2,3,4] 赵国宇 何勇 李炜俊 郭云鹏[2,3,4] 吴敏 AN Jian-qi;ZHAO Guo-yu;HE Yong;LI Wei-jun;GUO Yun-peng;WU Min(School of Future Technology,China University of Geosciences,Wuhan Hubei 430074,China;School of Automation,China University of Geosciences,Wuhan Hubei 430074,China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074,China;Engineering Research Center of Intelligent Technology for Geo-Exploration,Ministry of Education,Wuhan Hubei 430074,China;Loudi Valin Yunchuang Digital Intelligence Technology Co.,Ltd.,Loudi Hunan 417009,China)

机构地区：[1]中国地质大学(武汉)未来技术学院,湖北武汉430074 [2]中国地质大学(武汉)自动化学院,湖北武汉430074 [3]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 [4]地球探测智能化技术教育部工程研究中心,湖北武汉430074 [5]娄底华菱云创数智科技有限公司,湖南娄底417009

出　　处：《控制理论与应用》2025年第1期189-201,共13页Control Theory & Applications

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

摘　　要：煤气利用率(GUR)是衡量高炉能耗和稳顺运行的重要指标,受布料和送风操作在不同时间尺度下影响.现有对煤气利用率的建模、预测和控制仅在单一时间尺度上进行,忽略了多时间尺度特性,影响预测和控制的准确性.因此,提出一种数据驱动的多时间尺度高炉煤气利用率模型预测控制方法(MTSGURMPC).首先,根据经验模态分解和相关性分析得到布料和送风对煤气利用率影响的不同尺度;然后,建立布料长时间尺度和送风短时间尺度模型,提出了多时间尺度模型预测控制结构用于快速准确寻找高炉最优操作策略,该结构将煤气利用率划分为不同尺度进行模型预测控制,兼顾了高炉多时间尺度和模型预测控制动态优化特性,不断反馈优化趋近最优解;最后,基于某钢铁厂高炉工业数据进行应用实验,结果表明该方法能够实现煤气利用率准确预测和控制,并有效提高控制精度.In a blast furnace,gas utilization ratio(GUR)is an important indicator for measuring energy consumption and stable operation,which is affected by the operation of burden and blast supply at different time scales.The existing research methods on gas utilization rate are only conducted on a single time scale,ignoring the multi-time scale characteristics,which leads to the limited accuracy of gas utilization rate prediction and control.This paper presents a multi-time-scale gas utilization rate model predictive control method(MTSGURMPC)for blast furnaces based on data-driven.First,the influence of burden and blast supply on gas utilization rate in multi-time scales is analyzed by combining empirical model decomposition and correlation analysis.Then,this paper establishes a long-time-scale model for burden and a short-timescale model for blast supply,a multi-time-scale model predictive control structure is presented to search for the optimal operating strategy.The presented structure divides the gas utilization rate into different scales for model predictive control,taking into account blast furnace multi-time scales and dynamic optimization characteristics of model predictive control,which leads to continuous feedback optimization to approach the optimal solution.Finally,industrial experiments are conducted based on a blast furnace,and the results show that the method achieves accurate prediction and control.

关 键 词：高炉煤气利用率 数据驱动建模 多时间尺度系统 模型预测控制 经验模态分解 

分 类 号：TF53[冶金工程—钢铁冶金] TP273[自动化与计算机技术—检测技术与自动化装置]