1. bookVolume 12 (2019): Issue 2 (October 2019)
Journal Details
License
Format
Journal
eISSN
1339-3065
First Published
10 Dec 2012
Publication timeframe
2 times per year
Languages
English
access type Open Access

Neural network based explicit MPC for chemical reactor control

Published Online: 21 Jan 2020
Volume & Issue: Volume 12 (2019) - Issue 2 (October 2019)
Page range: 218 - 223
Journal Details
License
Format
Journal
eISSN
1339-3065
First Published
10 Dec 2012
Publication timeframe
2 times per year
Languages
English
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

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