Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy

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 con...

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Bibliographic Details
Published in:AIChE journal 2012-04, Vol.58 (4), p.1194-1202
Main Authors: Liu, Xinggao, Zhao, Chengye
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Chemical engineering
Exact sciences and technology
Fuzzy logic
fuzzy neural network
Mathematical models
melt index prediction
Melts
Neural networks
On-line systems
Online
online correction strategy
Optimization
Optimization algorithms
particle swarm optimization
Predictions
Strategy
Variables
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Summary: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
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.12660