Toggle Main Menu Toggle Search

Open Access padlockePrints

Hierarchical neural network based product quality prediction of industrial ethylene pyrolysis process

Lookup NU author(s): Qiusha Zhou, Dr Zhihua Xiong, Dr Jie ZhangORCiD


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


A two-layer hierarchical neural network is proposed to predict the product qualities of an industrial KTI GK-V ethylene pyrolysis process. The first layer of the model is used to classify these changes into different operating conditions. In the second layer, the process under each operating condition is modeled using bootstrap aggregated neural networks (BANN) with sequential training algorithm. The overall output is obtained by combining all the trained networks. Results of application to the actual process show that the proposed soft-sensing model possesses good generalization capability. © Springer-Verlag Berlin Heidelberg 2006.

Publication metadata

Author(s): Zhou Q, Xiong Z, Zhang J, Xu Y

Editor(s): Wang, J; Yi, Z; Zurada, JM; Lu, B-L; Hujun, Y

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks,

Year of Conference: 2006

Pages: 1132-1137

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer Berlin / Heidelberg


DOI: 10.1007/11760191_165

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540344827