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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received May 8, 2008
Accepted July 22, 2008

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 unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant

Faisal Ahmed Salman Nazir Yeong Koo Yeo^†

Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea

Korean Journal of Chemical Engineering, January 2009, 26(1), 14-20(7), 10.1007/s11814-009-0003-3

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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_x000D_ 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_x000D_ their superiority.

Keywords

Online Updating Scheme Recursive Partial Least Square Model Bias Updating High Density Polyethylene (HDPE) Melt Index (MI)

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