Abstract
LF(LADLE FURNACE) refining technology is the key process to regulate the temperature in steelmaking process. To predict the end temperature of molten steel in LF, this paper proposes a new data preprocessing technique based on feature extraction and clustering. Firstly, random forest algorithm was used to predict the temperature, the predictive hit rate of error within ± 10°C was 73.18%. The Lasso algorithm and K-means algorithm was used for feature extraction and clustering. After improvement, the prediction accuracy of the LF end temperature of error within ± 10°C was about 88.16%. The results show that this improvement has high prediction accuracy in the prediction about the end temperature of molten steel in LF refining.
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