Control of polystyrene batch reactor using fuzzy logic controller

Hosen, Mohammad Anwar, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Douglas 2013, Control of polystyrene batch reactor using fuzzy logic controller, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 4516-4521.

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Title Control of polystyrene batch reactor using fuzzy logic controller
Author(s) Hosen, Mohammad Anwar
Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Creighton, DouglasORCID iD for Creighton, Douglas
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 4516
End page 4521
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) fuzzy logic controller
polystyrene reactor
Summary Control of polymerization reactors is a challenging issue for researchers due to the complex reaction mechanisms. A lot of reactions occur simultaneously during polymerization. This leads to a polymerization system that is highly nonlinear in nature. In this work, a nonlinear advanced controller, named fuzzy logic controller (FLC), is developed for monitoring the batch free radical polymerization of polystyrene (PS) reactor. Temperature is used as an intermediate control variable to control polymer quality, because the products quality and quantity of polymer are directly depends on temperature. Different FLCs are developed through changing the number of fuzzy membership functions (MFs) for inputs and output. The final tuned FLC results are compared with the results of another advanced controller, named neural network based model predictive controller (NN-MPC). The simulation results reveal that the FLC performance is better than NN-MPC in terms of quantitative and qualitative performance criterion.
Language eng
Field of Research 080101 Adaptive Agents and Intelligent Robotics
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
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Created: Mon, 09 Dec 2013, 10:44:31 EST

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