【作者】 渐令；

【导师】 刘祥官；

【作者基本信息】 浙江大学 ， 运筹学与控制论， 2006， 硕士

【摘要】 高炉炼铁是钢铁工业的上游主体工序,作为国民经济支柱产业的重要组成部分,它对钢铁工业的发展与节能降耗都起重要的作用。高炉冶炼过程是一个高度复杂的过程,其运行机制往往具有非线性、时滞、高维、大噪声、分布参数等特性。高炉炼铁过程的炉温预测与控制的数学模型研究,既是炼铁自动化中的难题,也是实际炼铁生产中工长和厂长都十分关注的重要课题。炉温的准确预报,将有助于工长提高操作水平,从而达到提高利用系数和降低焦比的目的。 支持向量机是基于统计学习理论发展而来的一种机器学习算法,它能较好地解决非线性、高维数、小样本、局部极小点等实际问题。目前,已被广泛应用于模式识别、函数逼近、数据挖掘等领域。论文选取莱钢1号高炉(750m~3)在线采集的1000炉数据,通过相关系数的计算分析了高炉冶炼过程状态变量(料速、透气性、铁量差)和控制变量(喷煤、风量、风温)与高炉铁水含硅量[Si](高炉铁水含硅量反映了高炉化学热,可以用来表示炉温)的相关性,在此基础上,建立了基于支持向量机的铁水硅含量数值预报模型和铁水硅含量多类别分类模型。 论文主要包括以下四个方面的内容:高炉炼铁、高炉专家系统和炉温预测的概述;高炉冶炼过程状态参数和控制参数的分析;统计学习理论和支持向量机方法;基于支持向量机的铁水硅含量数值预报模型和铁水硅含量多类别分类模型。 论文选取了50组测试数据分别应用时间序列AR模型和支持向量机模型进行预测,并对预测结果进行了比较。结果表明:与AR模型相比,基于支持向量机的预测模型明显提高了铁水硅含量预报命中率。论文改进了M-ary分类方法,在使用C-均值算法对莱钢1号高炉(750m~3)在线采集的1000炉高炉铁水含硅量[Si]时间序列做聚类分析的基础上,实现了对[Si]的多类别分类,并对各分类机的性能做出评价。更多还原

【Abstract】 Blast Furnace ironmaking, which is the main working procedure of the steel industry, is the pillar of the national economy, and is very important for the steel industry and economizing energy sources. B.F ironmaking process is highly complicated, whose operating mechanism is characteristic of nonlinearity, time lag, high dimension, big noise and distribution parameter etc. The models to predict and control the silicon content in molten iron is the kernel of automation in B.F ironmaking. Exact prediction of the silicon content can help foreman to enhance operation level and retrench coke rate.SVM(Support vector machines) is a novel arithmetic of machine learning which is based on SLT(StatisticaI Learning Theory).It can solve the problem characterized by nonlinear, high dimension, small sample and local minimizing perfectly. Presently it has been widely used in pattern recognition, function approximation and data mine etc. In this paper, a new approach to predict the silicon content in molten iron (the silicon content in molten iron reflects the chemistry temperature of B.F, might be used to express the temperature of B.F) and classifier of silicon content in molten iron based on SVM are proposed. On the basis of analyzing the data from the Intelligent Control Expert System on 750m~3 BF Laiwu Iron & Steel Group Co. and ironmaking knowledge, SVM model is established to predict the silicon content in molten iron.There are four parts presented in this paper. In the first part, the B.F ironmaking process, B.F Expert System and the methods to predict the silicon content in molten iron are introduced. In the second part, the control parameters and state parameters of B.F ironmaking process are analyzed. In the third part, the Statistical Learning Theory and Support Vector Machine are introduced. In the fourth part, the model to predict the silicon content in molten iron and classifier of silicon content in molten iron based on SVM are proposed.Time series data of [Si] in hot metal actually observed in No.1 blast furnace at Laiwu Iron and Steel Group Co. are taken as sample space with the capacity of 1000 tap numbers and sampling time interval of 2h. The data of [Si] is divided into 5 classes according to C-mean clustering algorithm. In this paper, multi-class classification of [Si] by improved M-ary classification is proposed, and abilities of different classifiers are compared. The prediction method based on SVM get a higher hit ratio, which come up to more than 85%, than those of AR model, which only can reach 75%.更多还原