Oxygen consumption prediction in converter steelmaking is of great significance for the rational schedul-ing of the oxygen system and ensuring production safety in steel enterprise.Considering the diverse operating condi-tions of converter smelting and the inconsistent granularity of steel grade data,this paper proposes a prediction method for oxygen consumption in converter steelmaking based on granularity clustering.Firstly,the isolation forest anomaly detection method is used to remove abnormal data from the historical database.Then,Pearson cor-relation analysis and mutual information correlation coefficient are employed to select relevant influencing factors and achieve information granulation for different steel grade data,thereby extracting data features and unifying di-mensions.Fuzzy C-means(FCM)clustering is utilized to divide the operating conditions and establish oxygen con-sumption prediction sub-models for different conditions.Finally,the accuracy and effectiveness of the proposed method are validated through experiments using actual production data from the steel enterprise.
维普
万方数据