【作者】 张慧书；

【导师】 姜周华； 战东平；

【作者基本信息】 东北大学 ， 钢铁冶金， 2006， 硕士

【摘要】 随着冶金工业的发展和钢质量的不断提高，铁水预脱硫成为钢铁生产工艺流程中的一项重要任务。为了实现铁水预处理工艺过程快节奏、高效率化的生产发展需求，前人提出了利用计算机模型进行铁水预处理终点硫含量预报的方法。但预报终点硫含量的过程是一个非常复杂的工艺过程，应用传统的工艺理论建模已难以适应其多参数、非线性和高度不确定对象的特点，因此近年来多采用人工智能的方法来进行预报。 课题以梅山钢铁公司(以下简称梅钢)和本溪钢铁公司(以下简称本钢)的铁水预处理生产工艺为研究背景，采用改进的BP算法，应用Visual Basic 6.0高级程序语言进行程序设计，建立铁水预处理终点硫含量预报模型。模型建立过程中，针对BP网络迭代次数多、收敛速度慢等问题对标准BP算法进行了分析和改进，得到了适于本模型的改进型BP算法。对模型中各个参数的选择做了较详细的选择分析，从热力学和动力学的角度出发，结合现场数据情况，深入考察了影响铁水预处理终点硫含量的各种因素，确定了模型的网络结构及输入、输出参数。 用梅钢的1154炉数据和本钢的1900炉数据作为模型的训练样本，另外，再分别随机选取100炉数据作为模型测试样本，分别对模型进行了训练和测试。然后，对产生误差的原因以及模型各个输入参数与终点硫含量的关系进行了分析和讨论。 课题得到的主要结论如下： (1)提出了采用自适应调整学习率、增加动量项和最大误差学习法的适合本课题使用的改进BP算法。其中，新提出的自适应调整学习率改进方法如下： (2)确定模型输入参数为：铁水温度、铁水重量、镁粉耗量、石灰粉耗量、初始硫含量；模型的输出参数为：终点硫含量； (3)改进的BP算法比标准BP算法预报误差≤0.003％的精度提高28％； (4)梅钢模型的网络结构为5-14-1结构，动量项为0.6。本钢模型的网络结构为5-10-1结构，动量项为0.7。输入输出数据归一化范围均为[0.2，0.8]区间； (5)梅钢模型的预报结果有19％的炉次预报值与实际值完全一致，有90％的炉次误差≤0.003％，达到96％的炉次误差≤0.005％，平均误差为0.0017％；更多还原

【Abstract】 With the development of metallurgical industry and the improvement of steel quality, pre-desulfurization of hot metal has become an important task for steel production. In order to meet the demand of hot metal pretreatment processing with the character of fast rhythm and high efficiency, the predicting of the final sulfur content with computer model are put forward by the predecessors. However, it is a complex process to predict the final sulfur content. It is unsuitable for the features of multiparameter, nonlinear and uncertainty to modeling with traditional theroy model, and then the artifical intelligence method is applied to predict recently.Based on the productive practice of Meishan Steel Co. Ltd. and Benxi Steel Co. Ltd., adopted the improved BP algorithm, and used Visual Basic 6.0 programme software, the prediction model of final sulfur content during hot metal pretreatment processing is established. During modeling process, normal BP algorithm is analysed and improved for overcome its disadvantages of overmuch iterative repetition and slow convergence. All kinds of parameters in the model are elaborated. In the view of thermodynamic, kinetics and combining the characteristic of the field datas, the factors affecting final sulfur content during hot metal pretreatment processing are detailedly investigated. At the same time, the network configuration, input and output parameters are established.Training samples of Meishan Iron and Steel Co. Ltd. are 1154 heats, and which are 1900 heats for Benxi Iron and Steel Co. Ltd. 100 heats datas are randomly selected as the test samples respectively, and which are different from the above heats. The model is seperately trained and tested using the selected samples. And then the reasons resulting in error are analysed and discussed. The following conclusions are drawn:(1) The improved BP algorithm which is adaptive to this subject is put forward by adjusting study rate, adding momentum coefficient and employing the learning method of maximal error. The new study rate is as follows:更多还原