A global optimization method for model selection in chemical reactions networks
Model inference is a challenging problem in the analysis of chemical reactions networks. In order to empirically test which, out of a catalogue of proposed kinetic models, is governing a network of chemical reactions, the user can compare the empirical data obtained in one experiment against the the...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/107474 |
| Acceso en línea: | https://hdl.handle.net/11441/107474 https://doi.org/10.1016/j.compchemeng.2016.05.016 |
| Access Level: | acceso abierto |
| Palabra clave: | Model selection Chemical reactions networks Kinetic models Global optimization Variable neighborhood search |
| Sumario: | Model inference is a challenging problem in the analysis of chemical reactions networks. In order to empirically test which, out of a catalogue of proposed kinetic models, is governing a network of chemical reactions, the user can compare the empirical data obtained in one experiment against the theoretical values suggested by the models under consideration. It is thus fundamental to make an adequate choice of the decision variables (e.g. initial concentrations of the different species in the tank) in order to have maximal separation between sets of concentrations provided by the theoretical models, making then easier to identify which of the theoretical models yields data closest to those obtained empirically under identical conditions. In this paper we illustrate how global optimization techniques can be successfully used to address the problem of model separation, as a basis for model selection. Some examples illustrate the usefulness of our approach. |
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