优化高炉配矿模型的开发及应用

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英文篇名：Development and Application of Optimized Blast Furnace Burdening Model 作者：毕学工 ; 吴名 ; 周进东 ; 张晓龙 英文作者：BI Xuegong;WU Ming;ZHOU Jindong;ZHANG Xiaolong;Wuhan University of Science and Technology; 关键词：高炉 ; 配矿 ; 燃料比 ; 利用系数 ; 人工神经网络 英文关键词：blast furnace;;burdening;;fuel rate;;productivity;;artificial neural network 中文刊名：TIEN 英文刊名：Ironmaking 机构：武汉科技大学; 出版日期：2017-04-12 出版单位：炼铁 年：2017 期：v.36;No.219 语种：中文; 页：TIEN201702004 页数：4 CN：02 ISSN：42-1156/TF 分类号：15-18

摘要

基于武钢8号高炉连续4年8个月的生产数据,开发了燃料比和利用系数预报的两种人工神经网络模型,并将它们整合到以生铁成本最低为目标的优化高炉配矿模型中。优化高炉配矿模型,根据烧结矿、球团矿和天然块矿的化学成分和冶金性能,并综合考虑焦炭的性能和高炉生产对鼓风制度、热制度、喷吹制度、有害元素入炉量等方面的各种要求,可计算提出生铁成本最低的配矿方案。该模型从2015年开始应用于武钢高炉生产,结果准确可靠。
        Two artificial neural network models for predicting fuel rate and productivity have been developed based on continuous production data of WISCO's No.8 BF in the past 4 years and 8 months,and they are integrated in the optimized blast furnace burdening model.Targeting at minimizing the pig iron cost,the optimized blast furnace burdening model is also developed with following factors taken into account:coke characteristics,and the requirements of BF production for blowing system,thermal system,injection system and amount of harmful elements in charged burden according to the chemical analysis and metallurgical characteristics of sinter,pellet and natural lump ore.It provides through calculation the burdening proposal for production with the lowest pig iron cost,and it has been used in WISCO'S BF since 2015.The practice shows its calculation result is accurate.

引文

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