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

Authors:
Qiang Zhou;Zhihua Xiong;Jie Zhang;Yongmao Xu
Affiliations:
Department of Automation, Tsinghua University, Beijng, P.R. China;Department of Automation, Tsinghua University, Beijng, P.R. China;School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne, UK;Department of Automation, Tsinghua University, Beijng, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
Cited 1

Hierarchical mixtures of experts and the EM algorithm

Neural Computation
Nonlinear black-box modeling in system identification: a unified overview

Automatica (Journal of IFAC) - Special issue on trends in system identification
Optimal Control of Fed-Batch Processes Based on Multiple Neural Networks

Applied Intelligence

Review: A review of data mining applications for quality improvement in manufacturing industry

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.