Neural network based explicit MPC for chemical reactor control

In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A...

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Bibliographic Details
Published in:Acta Chimica Slovaca 2019-10, Vol.12 (2), p.218-223
Main Authors: Kiš, Karol, Klaučo, Martin
Format: Article
Language:English
Subjects:
Artificial neural networks
Chemical reactions
Chemical reactors
Computer simulation
continuous stirred tank reactor
Continuously stirred tank reactors
model predictive control
Neural networks
Organic chemistry
Predictive control
process control
Trajectory control
Weighting
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Summary:In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.
ISSN:1339-3065
1337-978X
1339-3065
DOI:10.2478/acs-2019-0030