Reaction Mechanism Reduction and Optimization Using Genetic Algorithms

This study describes the development of a new binary encoded genetic algorithm to determine a subset of species and their associated reactions that best represent the full starting point reaction mechanism in modeling a low-pressure stoichiometric 12.5% CH4/25% O2/62.5% Ar, burner-stabilized premixed flame. The number of species in the subset chosen is kept fixed and is specified at the start of the procedure. The genetic algorithm chooses better and better mechanisms on the basis of an objective function which measures how well the new reduced mechanisms predict a set of speciesʹ profiles simulated by the full mechanism. To verify the validity of our approach, a full enumeration was performed on a reduced problem, and it was reassuring to find that the genetic algorithm was able to find the optimum solution to this reduced problem in very few generations. A second step was to take the reduced reaction mechanism and to use a second real encoded genetic algorithm to determine the optimal reaction rate parameters that best model an experimental set of premixed flame speciesʹ profiles. This second step not only improved the reduced mechanismʹs ability to model the experimental profiles but also provided a remarkable improvement over the mechanism developed from step 1 in modeling combustion processes outside those used during the mechanismʹs development.