Academic Journal
An optimal experimental design framework for fast kinetic model identification based on artificial neural networks
| العنوان: | An optimal experimental design framework for fast kinetic model identification based on artificial neural networks |
|---|---|
| المؤلفون: | Sangoi E., Quaglio M., Bezzo F., Galvanin F. |
| المساهمون: | Sangoi, E., Quaglio, M., Bezzo, F., Galvanin, F. |
| سنة النشر: | 2024 |
| المجموعة: | Padua Research Archive (IRIS - Università degli Studi di Padova) |
| مصطلحات موضوعية: | Model selection, Machine learning, Design of experiments, Evolutionary algorithm, Optimization |
| الوصف: | The development of mathematical models to describe reaction kinetics is crucial in process design, control, and optimisation. However, distinguishing between different candidate kinetic models presents a non-trivial challenge. Recent works on this topic introduced an approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure. The performance of the method is evaluated through a case study related to the identification of kinetics in a batch reaction system, where different combinations of experimental design variables and noise level on the measurements are compared to assess their impact on kinetic model identification. The proposed experimental design methodology effectively reduces the number of required experiments while enhancing the artificial neural network’s ability to accurately identify the appropriate set of equations defining the kinetic model structure. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | volume:187; numberofpages:14; journal:COMPUTERS & CHEMICAL ENGINEERING; https://hdl.handle.net/11577/3515043; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85194970082 |
| DOI: | 10.1016/j.compchemeng.2024.108752 |
| الاتاحة: | https://hdl.handle.net/11577/3515043 https://doi.org/10.1016/j.compchemeng.2024.108752 |
| رقم الانضمام: | edsbas.A4056530 |
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