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- In relation to this article, we declare that there is no conflict of interest.
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Received January 4, 2010
Accepted March 2, 2010
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Prediction of the melt flow index using partial least squares and support vector regression in high-density polyethylene (HDPE) process
Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea
Korean Journal of Chemical Engineering, November 2010, 27(6), 1662-1668(7), 10.1007/s11814-010-0280-x
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Abstract
In polyolefin processes the melt flow index (MFI) is the most important control variable indicating product quality. Because of the difficulty in the on-line measurement of MFI, a large number of MFI estimation and correlation methods have been proposed. In this work, mechanical predicting methods such as partial least squares (PLS) method and support vector regression (SVR) method are employed in contrast to conventional dynamic prediction schemes. Results of predictions are compared with other prediction results obtained from various dynamic prediction schemes to evaluate predicting performance. Hourly MFIs are predicted and compared with operation data for the high density polyethylene process involving frequent grade changes. We can see that PLS and SVR exhibit excellent predicting performance even for severe operating situations accompanying frequent grade changes.
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Oh SJ, Lee J, Park S, Ind. Eng. Chem. Res., 44(1), 8 (2005)
Lee EH, Kim TY, Yeo YK, Korean J. Chem. Eng., 25(4), 613 (2008)
Karthikeyan M, J. Chem. Inf. Model., 45, 581 (2005)
Zhang J, Neural Networks, 12, 927 (1999)
Afantitis A, Melagraki G, Makridima K, Alexandridis A, Sarimveis H, Iglessi-Markopoulou O, J. Molecular Structure, 716, 193 (2005)
Tantishaiyakul V, Worakul N, Wongpoowarak W, International J. Pharm., 325, 8 (2006)
Shi J, Liu X, Sun Y, Neurocomputing, 70, 280 (2006)
Shi J, Liu XG, J. Appl. Polym. Sci., 101(1), 285 (2006)
Min KG, Han CH, Chang KS, Korean J. Chem. Eng., 5, 2437 (1999)
Park CK, Korean Operations Research and Management Society, 23, 75 (2006)
Sato C, Ohtani T, Nishitani H, Comput. Chem. Eng., 24(2-7), 945 (2000)
