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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
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ahmed, Faisal Kim, Laehyun Yeo, Yeong-Koo |
| Copyright Year | 2013 |
| 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 userfriendly, easy to use, graphical user interface to raise the usability and accessibility of the approach of online update of the PLS models. |
| Starting Page | 11 |
| Ending Page | 19 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s11814-012-0107-z |
| Volume Number | 30 |
| Alternate Webpage(s) | https://www.cheric.org/PDF/KJChE/KC30/KC30-1-0011.pdf |
| Alternate Webpage(s) | https://doi.org/10.1007/s11814-012-0107-z |
| Journal | Korean Journal of Chemical Engineering |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |