| Titre : | Bayesian framework for building kinetic models of catalytic systems (2009) |
| Auteurs : | Shuo-Huan Hsu, Auteur ; Stephen D. Stamatis, Auteur ; James M. Caruthers, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 48 N° 10, Mai 2009) |
| Article en page(s) : | pp. 4768–4790 |
| Note générale : | Chemical engineering |
| Langues : | Anglais |
| Tags : | Catalytic systems Bayesian approach Monte Carlo based methods |
| Résumé : | Recent advances in statistical procedures, coupled with the availability of high performance computational resources and the large mass of data generated from high throughput screening, have enabled a new paradigm for building mathematical models of the kinetic behavior of catalytic reactions. A Bayesian approach is used to formulate the model building problem, estimate model parameters by Monte Carlo based methods, discriminate rival models, and design new experiments to improve the discrimination and fidelity of the parameter estimates. The methodology is illustrated with a typical, model building problem involving three proposed Langmuir−Hinshelwood rate expressions. The Bayesian approach gives improved discrimination of the three models and higher quality model parameters for the best model selected as compared to the traditional methods that employ linearized statistical tools. This paper describes the methodology and its capabilities in sufficient detail to allow kinetic model builders to evaluate and implement its improved model discrimination and parameter estimation features. |
| En ligne : | http://pubs.acs.org/doi/abs/10.1021/ie801651y |
