An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

Abstract

Accurately predicting the end temperature of molten steel is significant for controlling ladle furnace (LF) refining. This paper proposes an error correction method called EC-CBR based on case-based reasoning (CBR) to reduce errors in the prediction models caused by discrepancies between actual production data and training data. The proposed method combines the incremental learning advantage of CBR with the ability of other models to fit nonlinear relations. First, a prediction model is established, and historical heats similar to the new heat are retrieved by CBR. Then, the model error of the new heat is calculated by employing the errors of similar heats. The prediction result is calculated by subtracting the error from the predicted value. Testing and comparison are conducted on the models (support vector regression, backpropagation neural network, extreme learning machine and mechanism model) and general CBR using actual production data. Results show that the EC-CBR is effective for both data-driven and mechanism models, with an increase of approximately 5% in hit rate within the range of ±5℃ for data-driven models and an increase of 21.73% for mechanism model. Moreover, the corrected data-driven models show higher accuracy than the general CBR, further proving the effectiveness of the proposed method.