Bayesian Framework for Building Kinetic Models of Catalytic Systems

• Published in: Industrial & engineering chemistry research 2009-05, Vol.48 (10), p.4768-4790
• Main Authors: Hsu, Shuo-Huan, Stamatis, Stephen D, Caruthers, James M, Delgass, W. Nicholas, Venkatasubramanian, Venkat, Blau, Gary E, Lasinski, Mike, Orcun, Seza
• Format: Article
• Language: English
• Subjects: Applied sciences; Catalysis; Catalytic reactions; Chemical engineering; Chemistry; Exact sciences and technology; General and physical chemistry; Kinetics, Catalysis, and Reaction Engineering; Reactors; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/ie801651y

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.