Optimal, active control of oxides of nitrogen (NOx) emissions from a natural gas-fired burner using a simple genetic algorithm

This paper describes the successful application of a genetic algorithm to the problem of active performance optimization of a non-linear, turbulent combustion process. Performance is measured in terms of combustion efficiency, η_c, and the concentration of NO and NO₂ ([NO_x]) emissions from a natural gas-fired research burner. Performance is affected by varying the mixing process through actuation of the excess air (EA) level and the swirl intensity (S´) at the burner exit. The control scheme is a simple feedback loop, where the genetic algorithm functions as a specialized search operator, continuously monitoring [NO_x] and η_c and updating EA and S´ settings. The approach is shown to be effective in achieving and maintaining relatively high burner performance for a given burner geometry. The efficacy of the genetic algorithm is compared to a simpler search technique known as Powell´s direction-set method. Although practical application of this technique will benefit from further development, the results point out some of the advantages of the genetic algorithm in this application