Model Establishment Of Dynamic Data Sequence And Application In Killing Oxygen By Adding Aluminum Systen

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.