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作 者:郑键[1] 李炜俊 安剑奇 ZHENG Jian;LI Weijun;AN Jianqi(Digital Intelligence Department,Hunan Valin Lianyuan Iron and Steel Co.,Ltd.,Loudi 417009,China;School of Automation,China University of Geosciences,Wuhan 430074,China)
机构地区:[1]湖南华菱涟源钢铁有限公司数智中心,湖南娄底417009 [2]中国地质大学(武汉)自动化学院,湖北武汉430074
出 处:《冶金自动化》2024年第2期114-124,共11页Metallurgical Industry Automation
基 金:国家自然科学基金面上项目(62373336)。
摘 要:高炉透气性指数是反映炉料间接还原程度以及炉况状态的重要指标,受高炉各操作在不同时间尺度下影响,目前对透气性指数发展趋势的分析、建模和预测多数是基于同一时间尺度且预测步长较短,预测结果难以指导现场判断。因此,本文提出一种基于多时间尺度的高炉透气性指数多步预测模型。首先通过机理和数据分析计算高炉各操作对透气性指数多时间尺度影响的时域特性,并结合频域特性多维度论证透气性指数受各操作在不同时间尺度影响;然后根据高炉操作在不同时间尺度影响透气性指数发展的特性,建立基于支持向量机的单步预测模型;最后在单步预测模型的基础上建立基于递归策略的透气性指数多步预测模型。实验结果表明,该方法能有效预测透气性指数未来发展趋势,便于现场决策。Blast furnace permeability index is an important index to reflect the indirect reduction degree of charge and furnace condition,which is affected by blast furnace operations in multiple time scales.The existing research analysis,modeling and prediction of the development trend of the permeability index are mostly based on the same time-scale and the prediction step is short,so the prediction results are difficult to guide the on-site judgment.Therefore,this paper proposed a multi-step prediction model of blast furnace permeability index based on multi-time scale.Firstly,the time domain characteristics of the influence of blast furnace operations on the permeability index on multi-time scale are calculated through mechanism and data analysis,and the multi-time scale effects of different operations on the permeability index in different time scales are analyzed in combination with the frequency domain characteristics.Then,according to the characteristics of blast furnace operation affecting the development of permeability index in different time scales,a single-step prediction model based on support vector machine is established.Finally,a multi-step prediction model of permeability index based on recursive strategy is established on the basis of single-step prediction model.The experimental results show that this method can effectively predict the future development trend of permeability index and is convenient for on-site decision-making.
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