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A New Soft Sensor Based on Recursive Partial Least Squares for Online Melt Index Predictions in Grade-Changing Hdpe Operations

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Chemical Product and Process Modeling
Volume 4, Issue 1 2009 Article 33

A New Soft Sensor Based on Recursive Partial Least Squares for Online Melt Index Predictions in Grade-Changing HDPE Operations
Faisal Ahmed∗ Salman Nazir† Yeong Koo Y. Yeo‡





Hanyang University, faisalahmed8@hotmail.com Hanyang University, salman nazeer@hotmail.com ‡ Hanyang University, ykyeo@hanyang.ac.kr

Copyright c 2009 The Berkeley Electronic Press. All rights reserved.

A New Soft Sensor Based on Recursive Partial Least Squares for Online Melt Index Predictions in Grade-Changing HDPE Operations
Faisal Ahmed, Salman Nazir, and Yeong Koo Y. Yeo

Abstract
Soft Sensors have been developed through phenomenological, empirical and hybrid modeling for quality variable predictions in various chemical processes. In this work a soft sensor based on an empirical model has been developed for the successful predictions of melt index (MI) in gradechanging polymerization of High Density Polyethylene (HDPE) processes. In order to capture the nonlinearity and grade-changing characteristics of the polymerization process efficiently, a recursive partial least squares (RPLS) update as well as a model bias update is applied to the process data successfully. Two schemes have been proposed: scheme-I and scheme-II. Scheme-I makes use of an arbitrary threshold value which selects one of the two update strategies according to the process requirement at a certain updating instance so as to minimize the relative root mean square error (RMSE). On the other hand, with the aim of preventing excessive RPLS update, scheme-II minimizes the number of RPLS update runs (NPR) while maintaining, increasing or sometimes reducing the RMSE obtained from scheme-I. Proposed schemes are compared with other strategies to exhibit their superiority. KEYWORDS: model update scheme, RPLS update, model bias update, HDPE, melt index (MI)

Ahmed et al.: A New RPLS Scheme for MI Predictions in HDPE Operations

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