TY - GEN
T1 - Multiple models soft sensing technique based on online clustering arithmetic for industry distillation
AU - Gang, Teng
AU - Cuimei, Bo
AU - Bing, Lu
AU - Shu, Ma
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/3/2
Y1 - 2015/3/2
N2 - Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.
AB - Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.
KW - Correlation analysis
KW - K-means clustering
KW - Partial least squares
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=84932111388&partnerID=8YFLogxK
U2 - 10.1109/WCICA.2014.7053005
DO - 10.1109/WCICA.2014.7053005
M3 - 会议稿件
AN - SCOPUS:84932111388
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 1869
EP - 1873
BT - Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Y2 - 29 June 2014 through 4 July 2014
ER -