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烧结过程智能控制及烧结矿冶金性能预测研究现状

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随着人工智能、物联网等技术的发展,利用大数据、自动化控制等手段来实现烧结过程智能控制及性能预测已经成为智慧炼铁的发展趋势.旨在综述烧结过程智能控制和性能预测研究现状,包括智能点火、料层状态监控、终点控制以及烧结矿性能检测,并通过生产成本、质量、效率、控制和预测的准确率等多角度综合分析不同技术的优缺点.在智能点火方面,详细分析点火工艺关键参数控制的重要性,指出机理分析法、数据驱动、PIDNN控制算法和修正的EID技术等在智能点火方面的应用现状.在料层状态监控方面,从料层温度和漏风监测2个方面展开叙述,在料层温度控制方面,重点分析温度模拟系统、Matcom、VC++以及多线程技术应用的准确率,同时介绍氧气平衡分析法、流体力学、红外热成像技术对烧结机漏风监测的影响.终点控制层面,系统讨论了灰色理论、反向传播神经网络、AdaBoost.RS算法、减法聚类和粒子群优化方法等技术特点和优化建议.烧结矿性能检测领域,涵盖了烧结矿成分在线检测和烧结矿性能预测与控制研究,通过PGNAA、LIBS以及基于DNN和LSTM的在线监测等技术智能分析烧结矿成分,同时介绍机器学习算法和神经网络等技术在烧结矿性能预测与控制方面的应用现状.综合介绍了烧结过程在信息化、智能化以及双碳背景下的发展情况及应用效果,通过系统分析烧结过程控制发展现状及特点,对未来烧结工艺在新形势下的发展脉络进行预测总结,为钢铁企业在烧结智能化研究领域提供理论和应用依据.
Research status of intelligent control of sintering process and prediction of metallurgical properties of sinter
With the development of artificial intelligence,Internet of Things and other technologies,the use of big data,automatic control and other means to achieve intelligent control and performance prediction of the sintering pro-cess has become the development trend of smart ironmaking.The purpose is to review the research status of intelli-gent control and performance prediction of sintering process,including intelligent ignition,layer condition monitor-ing,endpoint control and sinter performance detection,and comprehensively analyze the advantages and disadvan-tages of different technologies from multiple perspectives such as production cost,quality,efficiency,control and prediction accuracy.In terms of intelligent ignition,the importance of the control of key parameters of the ignition process is analyzed in detail,and the application status of mechanism analysis,data-driven,PIDNN control algo-rithm and modified EID technology in intelligent ignition is pointed out.In terms of layer temperature control,the accuracy of temperature simulation system,Matcom,VC++ and multi-threaded technology application is ana-lyzed,and the influence of oxygen balance analysis,fluid mechanics and infrared thermal imaging technology on the air leakage monitoring of sintering machine is introduced.At the endpoint control level,the technical characteristics and optimization suggestions such as grey theory,backpropagation neural network,AdaBoost.RS algorithm,sub-tractive clustering and particle swarm optimization method are systematically discussed.In the field of sinter perfor-mance detection,it covers the research of online detection of sinter components and sinter performance prediction and control,intelligent analysis of sinter composition through PGNAA,LIBS and online monitoring based on DNN and LSTM,and introduces the application status of machine Xi algorithms and neural networks in sinter perfor-mance prediction and control.It comprehensively introduces the development and application effect of the sintering process under the background of informatization,intelligence and carbon peaking and carbon neutrality,systemati-cally analyzes the development status and characteristics of sintering process control,predicts and summarizes the development context of the future sintering process under the new situation,and provides a theoretical and applica-tion basis for iron and steel enterprises in the field of intelligent sintering research.

sinteringintelligent controlintelligent ignitionend point controlmaterial layer monitoringalgorithm

丁成义、常仁德、郭胜兰、薛生、龙红明、余正伟

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安徽工业大学冶金工程学院,安徽 马鞍山 243032

安徽工业大学冶金工程与资源综合利用安徽省重点实验室,安徽 马鞍山 243032

宝钢湛江钢铁有限公司能源环保部,广东 湛江 524000

烧结 智能控制 智能点火 终点控制 料层监控 算法

国家自然科学基金青年基金资助项目安徽省自然科学基金青年基金资助项目冶金减排与资源综合利用教育部重点实验室开放基金资助项目

522043312208085QE145JKF20-03

2024

钢铁
中国金属学会钢铁研究总院

钢铁

CSTPCD北大核心
影响因子:1.204
ISSN:0449-749X
年,卷(期):2024.59(4)
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