Kinetic modeling of CO oxidation by genetic programming

Abstract

In this thesis, the kinetic modeling of CO oxidation was performed using genetic programming. A reaction rate equation was created from the experimental data, and then this equation was used to predict the mechanism of the reaction. Firstly; the generic reactions, of which both rate equation and mechanism were known, were studied in order to test the applicability of genetic programming in generating the rate expressions and to have a basic understanding about the method. The functions used in program were obtained from the functional terms commonly appears in catalytic rate equation. It was verified that the rate expressions derived using genetic programming were quite similar in terms of their structures and the groups that describes the main features of the rate equations in the literature. Then, the catalytic CO oxidation reaction was modeled to derive the model equations using three different experimental data sets; one of them was generated in our laboratory and the remaining two were obtained from the literature over various catalytic systems. After generating possible model equations, they were statistically evaluated by comparing with the experimental results and the other models proposed in the literature. The plausible models were then used to understand the mechanism of the reaction by analyzing the form of the rate expressions and the value of the parameters. The results were generally satisfactory, and it was concluded that genetic programming can help to understand the mechanism and the kinetics of the similar catalytic reactions.