Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network Based Explicit MPC for Chemical Reactor Control   In this paper, we show the implementation of deep neural networks applied in
process control. In our approach, we based the training of the neural network
on model predictive control. Model predictive control is popular for its
ability to be tuned by the weighting matrices and by the fact that it respects
the constraints. We present the neural network that can approximate the
behavior of the MPC in the way of mimicking the control input trajectory while
the constraints on states and control input remain unimpaired of the value of
the weighting matrices. This approach is demonstrated in a simulation case
study involving a continuous stirred tank reactor, where multi-component
chemical reaction takes place.

22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas