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Propylene Polymerization Reactor Control and Estimation Using a Particle Filter and Neural Network
Author(s) -
Dias Ana Carolina Spindola Rangel,
da Silva Wellington Betencurte,
Dutra Julio Cesar Sampaio
Publication year - 2017
Publication title -
macromolecular reaction engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 32
eISSN - 1862-8338
pISSN - 1862-832X
DOI - 10.1002/mren.201700010
Subject(s) - artificial neural network , process (computing) , computer science , model predictive control , filter (signal processing) , process control , control (management) , process engineering , control engineering , artificial intelligence , engineering , computer vision , operating system
Polymeric materials are present in various industrial sectors and in daily life, presenting advantages such as low cost and durability. Several processes for manufacturing have been developed. To achieve safety and operational goals measurement methods for proper process monitoring and effective control are needed. However, in real polymer plants, measuring devices are subject to uncertainties and are not always available. Hence, this paper proposes a virtual sensor scheme based on a particle filter and artificial neural network (ANN) that is applied to a simulated polymerization reactor. This scheme reduces uncertainties and enables the observation of latent variables. The ANN is also used for predicting the final properties of the polymer. The goal is to provide controllers with more complete and improved information. The results show that the virtual sensor scheme improves the process control, providing accurate estimates and action times that are consistent with industrial sampling intervals, which highlights its potential for practical applications.