Prediction of the melt flow index using partial least squares and support vector regression in high-density polyethylene (HDPE) process |
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Authors: | Tae Chang Park Tae Young Kim Yeong Koo Yeo |
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Affiliation: | 1.Department of Chemical Engineering,Hanyang University,Seoul,Korea |
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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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