Hybrid Modeling and Prediction of the Dynamic BOF Steelmaking Process

A new framework was presented for the accurate modeling and prediction of the reblown oxygen and the added coolant in dynamic basicoxygenfurnace(BOF) steelmaking processes. The proposed method takes advantages of the modeling approach based on mechanism and uses adaptive neuralnetworkfuzzyinference system(ANFIS) to compensate for the BOF modeling uncertainties based on mechanism. In the ANFIS compensating model, the firstorder TakagiSugeno type fuzzy rules were employed and a hybrid algorithm combining the least square method(LSM) and the gradient descent method was adopted to obtain the model structure. The practical data of an 180t converter were simulated. The simulated results are close to the practical values. The method is practicable and effective.