Quaglio, M;
Roberts, L;
Bin Jaapar, MS;
Fraga, ES;
Dua, V;
Galvanin, F;
(2020)
An artificial neural network approach to recognise kinetic models from experimental data.
Computers & Chemical Engineering
, 135
, Article 106759. 10.1016/j.compchemeng.2020.106759.
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Abstract
The quantitative description of the dynamic behaviour of reacting systems requires the identification of an appropriate set of kinetic model equations. The selection of the correct model may pose substantial challenges as there may be a large number of candidate kinetic model structures. In this work, a model selection approach is presented where an Artificial Neural Network classifier is trained for recognising appropriate kinetic model structures given the available experimental evidence. The method does not require the fitting of kinetic parameters and it is well suited when there is a high number of candidate kinetic mechanisms. The approach is demonstrated on a simulated case study on the selection of a kinetic model for describing the dynamics of a three-component reacting system in a batch reactor. The sensitivity of the approach to a change in the experimental design and to a change in the system noise is assessed.
| Type: | Article |
|---|---|
| Title: | An artificial neural network approach to recognise kinetic models from experimental data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.compchemeng.2020.106759 |
| Publisher version: | https://doi.org/10.1016/j.compchemeng.2020.106759 |
| 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: | Model selection; Model discrimination; Identifiability; Machine learning; Design of experiment |
| 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/10090857 |
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