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Process Systems Engineering
Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy
Article first published online: 13 MAY 2011
DOI: 10.1002/aic.12660
Copyright © 2011 American Institute of Chemical Engineers (AIChE)
Additional Information
How to Cite
Liu, X. and Zhao, C. (2012), Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy. AIChE J., 58: 1194–1202. doi: 10.1002/aic.12660
Publication History
- Issue published online: 8 MAR 2012
- Article first published online: 13 MAY 2011
- Accepted manuscript online: 19 APR 2011 12:49PM EST
- Manuscript Revised: 2 APR 2011
- Manuscript Received: 6 JUL 2010
Funded by
- National Natural Science Foundation of China. Grant Number: 50876093
- International Cooperation and Exchange Project of Science and Technology Department of Zhejiang Province. Grant Number: 2009C34008
- Zhejiang Provincial Natural Science Foundation for Distinguished Young Scientists. Grant Number: R4100133
- Abstract
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Keywords:
- fuzzy neural network;
- particle swarm optimization;
- melt index prediction;
- online correction strategy
Abstract
A black-box modeling scheme to predict melt index (MI) in the industrial propylene polymerization process is presented. MI is one of the most important quality variables determining product specification, and is influenced by a large number of process variables. Considering it is costly and time consuming to measure MI in laboratory, a much cheaper and faster statistical modeling method is presented here to predicting MI online, which involves technologies of fuzzy neural network, particle swarm optimization (PSO) algorithm, and online correction strategy (OCS). The learning efficiency and prediction precision of the proposed model are checked based on real plant history data, and the comparison between different learning algorithms is carried out in detail to reveal the advantage of the proposed best-neighbor PSO (BNPSO) algorithm with OCS. © 2011 American Institute of Chemical Engineers AIChE J, 2012