Neural Network Based Explicit MPC for Chemical Reactor Control
Slovak University of Technology in Bratislava, Radlinského 9, SK-812 37 Bratislava, Slovak Republic
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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.
Keywords: model predictive control, artificial neural networks, process control, continuous stirred tank reactor
Full paper in Portable Document Format: acs_0347.pdf
Acta Chimica Slovaca, Vol. 12, No. 2, 2019, pp. 218—223, DOI: 10.2478/acs-2019-0030