DocumentCode
518623
Title
Notice of Retraction
Robust kernel PLS based soft sensing for component concentrations in sodium aluminate solution
Author
Wei Wang ; Lijie Zhao ; Tianyou Chai
Author_Institution
Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China
Volume
2
fYear
2010
fDate
27-29 March 2010
Firstpage
254
Lastpage
258
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The component concentrations in sodium aluminate solution are very important in the process of alumina production, they represents the product quality. At present they can not be measured online, so the optimal operation is hardly to be achieved. To deal with this problem and based on the character of process industry data, we propose a RKPLS (Robust Kernel Partial Least Squares) soft sensing method by combining robust algorithm and kernel transformation to predict the component concentrations in sodium aluminate solution. Industry experiments are conducted in the alumina production process and the results show the effectiveness of this method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The component concentrations in sodium aluminate solution are very important in the process of alumina production, they represents the product quality. At present they can not be measured online, so the optimal operation is hardly to be achieved. To deal with this problem and based on the character of process industry data, we propose a RKPLS (Robust Kernel Partial Least Squares) soft sensing method by combining robust algorithm and kernel transformation to predict the component concentrations in sodium aluminate solution. Industry experiments are conducted in the alumina production process and the results show the effectiveness of this method.
Keywords
alumina; aluminium manufacture; least squares approximations; production engineering computing; Al2O3; alumina production process; aluminum production; component concentrations; process industry; robust Kernel partial least squares; robust kernel PLS; sodium aluminate solution; soft sensing; Conductivity; Kernel; Laboratories; Least squares methods; Metals industry; Monitoring; Production; Robustness; Temperature measurement; Temperature sensors; kernel PLS; robust PLS; sodium aluminate solution; soft-sensing;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486676
Filename
5486676
Link To Document