<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Neural network based explicit MPC for chemical reactor control

Slovak University of Technology in Bratislava

Acta Chimica Slovaca

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"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...

Neural network based explicit MPC for chemical reactor control

Published Online: Jan 21, 2020

Page range: 218 - 223

DOI: https://doi.org/10.2478/acs-2019-0030

Keywords
model predictive control, artificial neural networks, process control, continuous stirred tank reactor

© 2019 Karol Kiš et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Neural network based explicit MPC for chemical reactor control

Published Online: Jan 21, 2020

Page range: 218 - 223

DOI: https://doi.org/10.2478/acs-2019-0030

Keywordsmodel predictive control, artificial neural networks, process control, continuous stirred tank reactor

© 2019 Karol Kiš et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
model predictive control, artificial neural networks, process control, continuous stirred tank reactor