首页|高炉炼铁过程智能感知、诊断与控制方法的研究现状与展望

高炉炼铁过程智能感知、诊断与控制方法的研究现状与展望

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随着"双碳"政策的推进,对钢铁行业中主要能源消耗环节——高炉炼铁过程提出了更高的要求.实现高炉炼铁过程的关键指标智能感知、炉况诊断以及操作参数的合理优化控制,对推动高炉炼铁过程的安全、绿色低碳发展具有重要意义.首先,以高炉关键状态指标智能感知与预测作为切入点,从煤气利用率、铁水硅含量、透气性指数3个关键指标的感知与预测方法进行综述.其次,从专家系统以及数据驱动2个层面对高炉炉况监测与诊断的研究现状进行分析.然后,从专家系统与专家经验提取、多目标优化以及数据驱动预测控制3个角度综述了高炉操作参数优化及控制的研究进展.最后,通过分析各类模型、算法的优缺点,提出了当前高炉智能感知、炉况诊断以及操作优化当前面临的挑战与发展方向.
Research status and prospects of intelligent sensing,diagnosis and control method of blast furnace ironmaking processes
With the advancement of carbon peak and carbon neutrality policy,higher demands have been placed on the blast furnace ironmaking process,which constitutes a primary energy consumption segment within the iron and steel industry.Achieving intelligent sensing of key indicators,diagnosing furnace conditions,and optimizing control of operational parameters in the blast furnace ironmaking process is of paramount significance for promoting its safe,green,and low-carbon development.First-ly,taking intelligent sensing and prediction of key state indicators in blast furnaces as a starting point,providing a comprehensive review of sensing and prediction methods for three critical indica-tors:gas utilization rate,molten iron silicon content,and permeability index.Secondly,an analysis of the current research status of blast furnace condition monitoring and diagnosis is conducted from two perspectives:expert system and data-driven approaches.Subsequently,advancements in optimization and control of blast furnace operation parameters are reviewed from three angles:expert system and expert experience extraction,multi-objective optimization,and data-driven predictive control.Finally,by analyzing the strengths and weaknesses of various models and algorithms,the current challenges and development directions for intelligent sensing,furnace condition diagnosis,and operation optimi-zation of blast furnaces are proposed.

iron and steel metallurgical processintelligent sensing technologyfurnace condition di-agnosisdecision optimizationintelligent control

安剑奇、郭云鹏、张新民、杜胜、黄元峰、吴敏

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中国地质大学(武汉)自动化学院,湖北武汉 430074

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

地球探测智能化技术教育部工程研究中心,湖北武汉 430074

浙江大学控制科学与工程学院,浙江杭州 310058

工业控制技术国家重点实验室,浙江杭州 310027

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钢铁冶金过程 智能感知技术 炉况诊断 决策优化 智能控制

国家自然科学基金面上项目国家自然科学基金面上项目湖北省自然科学基金青年基金高等学校学科创新引智计划(111计划)

62373336619732872022CFB582B17040

2024

10.3969/j.issn.1000-7059.2024.02.001

冶金自动化
冶金自动化研究设计院

冶金自动化

影响因子：0.685
ISSN：1000-7059
年,卷(期)：2024.48(2)
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