Sangoi, Enrico;
Quaglio, Marco;
Bezzo, Fabrizio;
Galvanin, Federico;
(2022)
Optimal Design of Experiments Based on Artificial Neural Network Classifiers for Fast Kinetic Model Recognition.
In:
Proceedings of the 14th International Symposium on Process Systems Engineering (PSE 2021+).
(pp. pp. 817-822).
Elsevier B.V.
![[thumbnail of PSE2021_Sangoi_et_al.pdf]](https://discovery.ucl.ac.uk/style/images/fileicons/text.png) |
Text
PSE2021_Sangoi_et_al.pdf - Accepted Version Access restricted to UCL open access staff Download (313kB) |
Abstract
Developing mathematical models for the description of reaction kinetics is fundamental for process design, control and optimisation. The problem of model discrimination among a set of candidate models is not trivial, and recently a new and complementary approach based on artificial neural networks (ANNs) for kinetic model recognition was proposed. This paper extends the ANNs-based model identification approach by defining an optimal design of experiment procedure, whose performance is assessed through a simulated case study. The proposed design of experiments method allows to reduce the number of experiments to be conducted while increasing the ability of the artificial neural network in recognising the proper kinetic model structure.
| Type: | Proceedings paper |
|---|---|
| Title: | Optimal Design of Experiments Based on Artificial Neural Network Classifiers for Fast Kinetic Model Recognition |
| Event: | 14th International Symposium on Process Systems Engineering – PSE 2021+ |
| Location: | Kyoto |
| Dates: | 19 Jun 2022 - 23 Jun 2022 |
| DOI: | 10.1016/B978-0-323-85159-6.50136-6 |
| Publisher version: | https://doi.org/10.1016/B978-0-323-85159-6.50136-6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Optimal design of experiments, kinetic model identification, ANN classifiers. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10173315 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
