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Statistical data modeling based on partial least squares: Application to melt index predictions in high density polyethylene processes to achieve energy-saving operation

Title
Statistical data modeling based on partial least squares: Application to melt index predictions in high density polyethylene processes to achieve energy-saving operation
Author
여영구
Keywords
PLS Model Parameters Update; Model Bias Update; HDPE; Melt Index (MI); GUI; PLS ALGORITHM; REGRESSION; SELECTION; SENSORS; REACTOR
Issue Date
2013-01
Publisher
KOREAN INSTITUTE CHEMICAL ENGINEERS
Citation
KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2013, 30(1), P.11-19
Abstract
We propose two parameter update schemes which employ recursive update of partial Least Squares (PLS) model parameters as well as a model bias update to the process data. These update schemes have been applied to the successful prediction of Melt Index (MI) in grade-change operations of High Density Polyethylene (HDPE) plants. The lack of sophisticated software support hinders the recurrent use of these techniques. This paper also presents user-friendly, easy to use, graphical user interface to raise the usability and accessibility of the approach of online update of the PLS models.
URI
http://link.springer.com/article/10.1007/s11814-012-0107-zhttp://hdl.handle.net/20.500.11754/41434
ISSN
0256-1115
DOI
10.1007/s11814-012-0107-z
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > CHEMICAL ENGINEERING(화학공학과) > Articles
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