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Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process using Deep Belief Networks

Lookup NU author(s): Dr Jie ZhangORCiD



This is the final published version of an article that has been published in its final definitive form by Springer, 2020.

For re-use rights please refer to the publisher's terms and conditions.


This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to be measured in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensor to estimate the quality variable from process data. In recent years, deep learning has achieved many successful applications on image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimation of polymer melt index.

Publication metadata

Author(s): Zhu C, Zhang J

Publication type: Article

Publication status: Published

Journal: International Journal of Automation and Computing

Year: 2020

Volume: 17

Pages: 44-54

Print publication date: 01/02/2020

Online publication date: 05/11/2019

Acceptance date: 18/09/2019

Date deposited: 07/10/2019

ISSN (print): 1476-8186

ISSN (electronic): 1751-8520

Publisher: Springer


DOI: 10.1007/s11633-019-1203-x


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