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Enhanced Predictive Modeling Using Multi Block Methods

Lookup NU author(s): Jeong Hong, Dr Jie ZhangORCiD, Emeritus Professor Julian Morris


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Multi block PLS (MBPLS) models have been reported to handle the complexity of data which is difficult to anlayse with the conventional PLS models. However, the conventional MBPLS models do not offer improved predictive modeling in terms of prediction performance. A data partitioning method for enhanced predictive process modeling is proposed in this paper and is called Time-MBPLS. It enables data to be separated into blocks by different measuring time. Model parameters can be used to express contributions for all variables in a certain time period and it is possible to determine data blocking structures according to variable impacts on the quality variables. The proposed method is applied to the inferential estimation of a quality variable in a bio-production process. The results demonstrate that the proposed method can improve model prediction performance on the unseen testing data. © 2009 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Hong JJ, Zhang J, Morris J

Editor(s): Jezowski, J., Thullie, J.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 19th European Symposium on Computer Aided Process Engineering

Year of Conference: 2009

Pages: 327-332

ISSN: 1570-7946

Publisher: Elsevier BV


DOI: 10.1016/S1570-7946(09)70055-0

Library holdings: Search Newcastle University Library for this item

Series Title: Computer Aided Chemical Engineering

Series Editor(s): Jacek Jeżowski and Jan Thullie

ISBN: 9780444534330