A Method Of Model Establishment Of Dynamic Data Sequence And Application In Killing Oxygen By Adding Aluminum System

In the process of steel-making, killing oxygen by adding aluminum process is a controlled object whose parameters are distributed and non-linear, and its observed data are discrete. The key point of this process is model of killing oxygen by adding aluminum process. Precision of model is closely correlated with the stabilization of production, the reduction of cost and quality of steel. 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, and killing oxygen by adding aluminum process is just one part of the whole system. Consequently, in order not to interfere with next later continuing casting and other process, and to ensure the production rhythm, when the molten steel arrives at the aluminum feeding station, it cannot meet the production requirement only to sample, assay and determine the ingredient content. So the management has taken some technical reconstruction and formed a linear regression model of the killing oxygen by adding aluminum process with history data. However, it is just an empirical formula, which cannot take the real time, disturbance and randomness at the spot into account. So the fitting accuracy and forecast of this model are not perfect and it is difficult to design the automatic control system about killing oxygen by adding aluminum process and achieve accurate control.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, grey 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 ofobserved data and prediction accuracy of the model are enhanced through rolling optimization and modifying model parameters. A new approach of modeling of observed data sequences is explored.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 integrated the advantages of different observed data modeling methods, which provided a referable approach to solve the problem that it is difficult to build the killing oxygen by adding aluminum model in steel making.