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Received October 4, 2011
Accepted July 5, 2012
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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
Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea 1Department of Chemical Engineering, Seoul National University of Science and Technology, Seoul 135-743, Korea
Korean Journal of Chemical Engineering, January 2013, 30(1), 11-19(9), 10.1007/s11814-012-0107-z
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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.
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Li CF, Ye H, Wang GZ, Zhang J, Chem. Eng. Technol., 28(2), 141 (2005)
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Helland K, Berntsen HE, Borgen OS, Martens H, Chemom. Intell. Lab. Syst., 14, 129 (1992)
Dayal BS, MacGregor JF, J. Process Control, 7(3), 169 (1997)
Qin SJ, Comput. Chem. Eng., 22(4-5), 503 (1998)
Mu SJ, Zeng YZ, Liu RL, Wu P, Su HY, Chu J, J. Process Control, 16(6), 557 (2006)
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Kim TY, Yeo YK, Korean J. Chem. Eng., 27(6), 1669 (2010)
Lee EH, Kim TY, Yeo YK, Korean Chem. Eng. Res., 46(6), 1043 (2008)
Sharmin R, Sundararaj U, Shah S, Griend LV, Sun YJ, Chem. Eng. Sci., 61(19), 6372 (2006)
Stone M, Math. Operationsforch. Statist., Ser. Statist., 9(1) (1978)
Breiman L, Annals of Statistics., 24, 2350 (1996)
Breiman L, Spector P, International Statistical Review., 60, 291 (1992)
