A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant |
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Authors: | Faisal Ahmed Salman Nazir Yeong Koo Yeo |
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Affiliation: | (1) Department of Chemical Engineering, Hanyang University, Seoul, 133-791, Korea |
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Abstract: | An empirical model has been developed for the successful prediction of the melt index (MI) during grade change operations
in a high density polyethylene plant. To efficiently capture the nonlinearity and grade-changing characteristics of the polymerization
process, the plant operation data is treated with the recursive partial least square (RPLS) scheme combined with model output
bias updating. In this work two different schemes have been proposed. The first scheme makes use of an arbitrary threshold
value which selects one of the two updating methods according to the process requirement so as to minimize the root mean square
error (RMSE). In the second scheme, the number of RPLS updating runs is minimized to make the soft sensor time efficient,
while reducing, maintaining or normally increasing the RMSE obtained from first scheme up to some extent. These schemes are
compared with other techniques to exhibit their superiority.
This paper is dedicated to Professor Chang Kyun Choi to celebrate his retirement from the school of chemical and biological
engineering of Seoul National University. |
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Keywords: | Online Updating Scheme Recursive Partial Least Square Model Bias Updating High Density Polyethylene (HDPE) Melt Index (MI) |
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