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.
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.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2012年第10期1184-1189,共6页
Journal of University of Science and Technology Beijing
基金
国家自然科学基金资助项目(50934007
51004013)
中央高校基本科研业务费专项(FRF-MP-09-009B
FRF-AS-09-008B)
关键词
熔炼
铁矿石还原
煤气
预测
模糊聚类
偏最小二乘法
smelting
iron ore reduction
gases
prediction
fuzzy clustering
partial least squares
