基于粒度聚类的转炉炼钢氧气消耗量预测

Converter Steelmaking Oxygen Consumption Prediction Based on Granularity Clustering

转炉炼钢是钢铁企业的主要耗氧工序,预测转炉炼钢的氧气消耗量对氧气系统合理调度、保证生产安全具有重要意义.考虑到转炉冶炼工况多、钢种数据粒度不统一,提出一种基于粒度聚类的转炉炼钢氧气消耗量预测方法.首先,利用孤立森林异常检测法剔除历史数据库中的异常数据;接着,采用皮尔逊相关性分析和互信息相关系数选取相关影响因子,对不同钢种数据进行信息粒化,实现数据特征提取和维度统一,使用模糊C均值(Fuzzy C-means,FCM)划分工况并建立不同工况下的氧气消耗量预测子模型;最后,利用企业的实际生产数据进行实验,验证所提方法的准确性和有效性.

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.