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Hierarchical Neural Network Based Product Quality Prediction of Industrial Ethylene Pyrolysis Process

  • Qiang Zhou
  • Zhihua Xiong
  • Jie Zhang
  • Yongmao Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

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.

Keywords

Individual Network Good Generalization Capability Hierarchical Neural Network Process Operating Condition Feedstock Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qiang Zhou
    • 1
  • Zhihua Xiong
    • 1
  • Jie Zhang
    • 2
  • Yongmao Xu
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijngP.R. China
  2. 2.School of Chemical Engineering and Advanced MaterialsUniversity of NewcastleNewcastle upon TyneUK

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