Lyu, Wenyao;
Galvanin, Federico;
(2025)
Automated Identification of Kinetic Models for Nucleophilic Aromatic Substitution Reaction via DoE-SINDy.
In:
Systems and Control Transactions.
(pp. pp. 179-185).
PSE Press
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Abstract
Nucleophilic aromatic substitutions (SNAr) are key chemical transformations in pharmaceutical and agrochemical synthesis, yet their complex mechanisms (concerted or two-step) complicate kinetic model identification. Accurate kinetic models for SNAr are essential for scale-up, optimization, and control of the reaction process, but conventional methods struggle with mechanism uncertainty driven by substrates, nucleophiles, and reaction conditions, with data collection being difficult due to its source-intensive nature. We address this using DoE-SINDy, a data-driven framework for generative modelling without complete theoretical understanding. A benchmark study on the SNAr reaction of 2,4-difluoronitrobenzene with morpholine in ethanol was conducted, incorporating parallel and consecutive side-product formation. Ground-truth kinetic models validated in prior studies were used to generate in-silico data under varying noise levels and sampling intervals. DoE-SINDy successfully identified the true kinetic model with minimal runs, quantifying the impact of key design factors such as inlet concentrations, residence time, sample size and experimental budget on model identification.
| Type: | Proceedings paper |
|---|---|
| Title: | Automated Identification of Kinetic Models for Nucleophilic Aromatic Substitution Reaction via DoE-SINDy |
| Event: | The 35th European Symposium on Computer Aided Process Engineering |
| Dates: | 6 Jul 2025 - 9 Jul 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.69997/sct.107548 |
| Publisher version: | https://doi.org/10.69997/sct.107548 |
| Language: | English |
| Additional information: | © 2025 by the authors. Licensed to PSEcommunity.org and PSE Press. This is an open access article under the creative commons CC-BY-SA licensing terms. Credit must be given to creator and adaptations must be shared under the same terms. See https://creativecommons.org/licenses/by-sa/4.0/ |
| Keywords: | Modelling and Simulations, Reaction Engineering, System Identification, Machine Learning, Model Structure Generation, 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 > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10211085 |
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