Research Of Lf Endpoint Prediction Based On Neural Network

As an important part of the advanced steel making product process in the world, the ladle furnace(LF) steel making is a kind of secondary smelting process which adds deoxidant and alloy to the molten steel to deoxygenate, desulphurize and uniform the component, in order to smelt high quality and special type steel. Thereinto, endpoint prediction of ladle furnace steel making is important part of the ladle furnace steel control. It is a very importunate question how to control temperature and component of steel on endpoint accurately. It is helpful to operator choosing the most effective control policy. The accurate prediction of LF's temperature and component of steel in endpoint is very helpful to organising production rationally and improving quality of steel and reducing steel making's cost. It is valuable to operator choosing the most effective control policy too.The thesis have made a particular study to endpoint prediction of ladle furnace based on referring to the large numbers of literature. Knowing different prediction method, the paper have neural network predictive control as the basal prediction method, which considering practical process and actual research condition. The main idea of neural network predictive control is calculating the parameter of the model, and then predict the other spot data according the confirmed model and the controller, based on the analysis of the past spot data and the mastery of the previous neural network model and considering practical characteristic of steel making process.The very important control objects of the LF steel making process is that temperature and component of steel in endpoint. Using an improved BP neural network model, a mathematical model of endpoint temperature and components and correlative factors are developed to predict the endpoint of LF-steel making. The prediction results showed the algorithm has comparatively strong ability of self-studying. And this mathematic algorithm has better convergence character than conventional BP one. The precision of the results is comparatively high.