A Novel Approach to the Optimization of Reaction Rate Parameters for Methane Combustion Using Multi-Objective Genetic Algorithms

This study uses a multi-objective genetic algorithm to determine new reaction rate parameters (A’s, β’s and $Ea$’s in the non-Arrhenius expressions) for the combustion of a methane/air mixture. The multi-objective structure of the genetic algorithm employed allows for the incorporation of both perfectly stirred reactor and laminar premixed flame data in the inversion process, thus enabling a greater confidence in the predictive capabilities of the reaction mechanisms obtained. Various inversion procedures based on reduced sets of data are investigated and tested on methane/air combustion in order to generate efficient inversion schemes for future investigations concerning complex hydrocarbon fuels. The inversion algorithms developed are first tested on numerically simulated data. In addition, the increased flexibility offered by this novel multi-objective GA has now, for the first time, allowed experimental data to be incorporated into our reaction mechanism development. A GA optimized methane-air reaction mechanism is presented which offers a remarkable improvement over a previously validated starting mechanism in modeling the flame structure in a stoichiometric methane-air premixed flame (http://www.personal.leeds.ac.uk/∼fuensm/project/mech.html). In addition, the mechanism outperforms the predictions of more detailed schemes and is still capable of modeling combustion phenomena that were not part of the optimization process. Therefore, the results of this study demonstrate that the genetic algorithm inversion process promises the ability to assess combustion behavior for fuels where the reaction rate coefficients are not known with any confidence and, subsequently, accurately predict emission characteristics, stable species concentrations and flame characterization. Such predictive capabilities will be of paramount importance within the gas turbine industry.