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Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy
Author(s) -
Liu Xinggao,
Zhao Chengye
Publication year - 2012
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.12660
Subject(s) - particle swarm optimization , artificial neural network , black box , algorithm , process (computing) , fuzzy logic , computer science , backpropagation , measure (data warehouse) , artificial intelligence , data mining , mathematical optimization , mathematics , operating system
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