Intelligent Method For BOF Endpoint [P]&[Mn] Estimation

Effective controlling of phosphorus [P] content and manganese [Mn] content of steel bath is one of main tasks of basic oxygen furnace (BOF) steelmaking progress, but there isn´t any real-time means to measure steel bath [P] content or [Mn] content. In this paper, two models are introduced to estimation the bath endpoint [P] and [Mn] for BOF steelmaking progress. Metallurgical mechanism model is applied which is based on the raw material information and process information, for the case of slow analyzing speed of in-blow sublance sample. And neural network model is used which is based on the in-blow sample analyzing result and BOF dynamic process information, for the fast analyzing speed case. Finally the simulation results proved these models are effective