COREX冷煤气成分预测的二步建模方法

Two-stage modeling method for predicting COREX cold gas content

  • 摘要: 针对熔融气化炉冷煤气成分含量,提出了基于熵权模糊C均值聚类和偏最小二乘的COREX冷煤气成分预测方法.建模过程中首先根据料单中各种原料的单耗量,利用熵权模糊C均值聚类的方法将料单聚类成若干种料单类别,然后针对不同的料单类别,利用偏最小二乘法分别建立冷煤气成分预测模型.对宝钢COREX-1#炉实际生产数据验证结果表明:该方法可以有效地建立COREX冷煤气成分预测模型,具有较好的预测精度.

     

    Abstract: A method for predicting cold gas content in a melter-gasifier was proposed based on entropy-weighted fuzzy C-means clustering and partial least squares(PLS).In the modeling process,an entropy-weighted fuzzy C-means clustering algorithm is used to get the clustering result of burden calculation reports according to the consumption of raw materials at first.Then,different prediction models are built based on a PLS algorithm for various cluster types.The real field data of cold gas content from Baosteel COREX-1# were used for verification.It is shown that the method can build the prediction model of COREX cold gas content effectively,and has an advantage in prediction accuracy.

     

/

返回文章
返回