The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network

The AOD stove ferroalloy steel-making endpoint temperature and the ingredient are the control objectives of AOD stove ferroalloy steel-making, which has serious nonlinear relations with variables such as oxygen blown quantity and the quantity of molten steel and is unable to measure continuously online. This article develops a set of AOD stove smelt ferroalloy end-point control model based on the wavelet neural network and some actual data of a 180t AOD ferroalloy stove in Jilin Ferroalloy Factory to conduct the model verification research. By forecasting the end-point temperature and the carbon content and gathering the spot operating data and the practical application, we can see that the double hit probability of carbon and temperature reaches above 80%.