【作者】 刘超；

【导师】 方康玲；

【作者基本信息】 武汉科技大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 定氧加铝工艺是炼钢厂铝镇静钢冶炼过程的重要工序之一。通过往钢水中加入一定量的铝，来控制钢水中的氧的含量，同时，也保证钢水中有一定量的酸溶铝成分，这样才能保证炼出的钢的质量。因此建立一个加铝量的模型，把钢水中的氧含量和酸溶铝成分控制在所需的范围内，是十分必要的。当前，厂方经过了一些技术改造，通过历史数据建立了一个定氧加铝工艺的线性回归模型。但当每一钢包从出炉到达加铝站进行定氧加铝期间，会受到温度、定氧仪探头伸入钢水的深度和吹氩等不同因素的影响，而该模型仅仅是一个定参数公式，对现场中的扰动性和随机性等诸多因素不能作出很好的反映，故在出现大扰动时，采用该模型将很难保证其拟合精度。 本论文首先研究了国内外多种关于动态数据序列建模的方法，比较了各种方法的优缺点。根据定氧加铝工艺中钢包含氧量因工况等因素有所不同，在观察若干炉含氧观测数据序列的基础上，采用了观测数据建模策略，结合回归分析、频谱理论和时间序列建模的思想，建立了定氧加铝模型。采用信号分离方法和技术，将信号中的确定性和非确定性信号分离，分别给出相应的参数估计及数学模型；然后，充分考虑观测数据和其它变量的相关关系以及数据自身依赖关系，建立了观测数据序列的混合模型。考虑到现场的随机因素的影响，在对随机信号采用时间序列方法建立模型时，研究了一种确立模型的阶数和模型参数的算法；在工艺状况发生改变时，通过对模型的滚动优化，修正模型参数，来提高模型对实时观测数据的拟合程度和预测的准确性。 本课题的研究背景是基于“武钢二炼钢定氧加铝模型自动控制系统改造”项目，对炼钢的定氧加铝工艺中建立观测数据序列的混合预测模型是定氧加铝自动控制系统的基础。实际生产工艺参数的拟合和预测效果表明，本文提出的一种观测数据序列混合模型建立的方法，为解决目前炼钢行业中定氧加铝模型难以建立的问题，提供了一条可供参考的途径。更多还原

【Abstract】 In the process of steel-making, killing oxygen by adding aluminum process is an important working procedure.by adding the fixed quantity of the aluminum,it could control the oxygen in the molten steel. At the same time,it could guarantee the fixed quantity of the aluminium in the aching.Only by this,it can guarantee the quality of the steel.So making a model of the adding aluminum is very necessary. But now,most of the model is a linear regression model of the killing oxygen by adding aluminum process with history datal33. The molten steel have the same making environment at the spot, but it is subject to many factors such as temperature and killing oxygen instrument penetration’s depth into the molten steel during the time.But the model is just a fixed parameter formula.it can’t make right reflection to the disturbance and randomness at the spot.So when some big disturbance happen,the model can’t guarantee the fitting accuracy.This article deals with many modeling methods of dynamic data sequence and compares, the advantages and disadvantages of these methods. Due to the affection of the environment, oxygen content in the molten steel is different in the killing oxygen by adding aluminum process. On the base of many dynamic data sequences of oxygen content, advanced rational dynamical data modeling strategy is introduced, which is correlated with the analyzed method of regression, spectrum theory and time sequence to the modeling of killing oxygen by adding aluminum model and the method is adopted about the Separation of certain and uncertain signal and corresponding parameter estimation and mathematical model is put forward. And then the combined prediction model Of the Observed data is formed, which consider the correlation of the variable with the other variable and self-dependency of the variable itself fully. Considering the affection of located random factors, an algorithm is worked over to determine the model’ s Order number and parameters. When the condition changes, fitting degree of observed data and prediction accuracy of the model are enhanced through rolling optimization and modifying model parameters .This thesis is based on the project "killing oxygen by adding aluminum model automatic controlling system rebuilding of the second steelwork of Wuhan Iron and Steel company", the combined prediction model of observed data sequences in the killing oxygen by adding aluminum process is the basic of this automatic controlling system. The actual fitting and predicted results indicate that the modeling method put forward in this article of observed data sequences has provided a referable approach to solve the problem that it is difficult to build the killing oxygen by adding aluminum model in steel making.更多还原