Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Hosen,MA, Khosravi,A, Creighton,D and Nahavandi,S 2014, Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors, Journal of the Taiwan institute of chemical engineers, vol. 45, no. 5, pp. 2246-2257, doi: 10.1016/j.jtice.2014.05.021.

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Title Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors
Author(s) Hosen,MAORCID iD for Hosen,MA
Khosravi,AORCID iD for Khosravi,A
Creighton,DORCID iD for Creighton,D
Nahavandi,SORCID iD for Nahavandi,S
Journal name Journal of the Taiwan institute of chemical engineers
Volume number 45
Issue number 5
Start page 2246
End page 2257
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2014-09
ISSN 1876-1070
Keyword(s) Ensemble neural network
Neural network
Polymerization reactor
Prediction interval
Science & Technology
Engineering, Chemical
Summary Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.
Language eng
DOI 10.1016/j.jtice.2014.05.021
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier BV
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