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Inferential Estimation of Polymer Melt Index Using Deep Belief Networks

Lookup NU author(s): Dr Changhao Zhu, Dr Jie ZhangORCiD

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Abstract

This paper presents using deep belief networks for the inferential estimation of polypropylene melt index in an industrial polymerization process. The polymer melt index is difficult to be measured online in practice. The relationship between easy-to-measure process variables and difficult-to measure polymer melt index is found by using a deep belief network model. The development of a deep belief network model contains an unsupervised training process and a supervised training process. Deep belief networks use a novel semi-supervised learning method. The process operational data without corresponding quality measurements can be used in the unsupervised training process. The profuse information behind input data are captured by deep belief networks. It is shown that the deep belief network model gives very accurate estimation of melt index.


Publication metadata

Author(s): Zhu C, Zhang J

Editor(s): Xiandong Ma

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 24th International Conference on Automation and Computing (ICAC 2018)

Year of Conference: 2018

Pages: 147-152

Online publication date: 01/07/2019

Acceptance date: 06/01/2018

Publisher: IEEE

URL: https://doi.org/10.23919/IConAC.2018.8749111

DOI: 10.23919/IConAC.2018.8749111

Library holdings: Search Newcastle University Library for this item

ISBN: 9781862203419


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