Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimization

Abstract

Abstract A computational fluid dynamics (CFD) code was connected with a systematic optimization algorithm to find the optimum settings for nitric oxide (NO) emission minimization in the bubbling fluidized bed boiler. This approach is superior to the typical approach in which CFD is used to calculate only a few intuitively selected cases, and the best case found is treated as the ‘optimum’ solution. Ammonia injection (selective non-catalytic reduction) is applied to control NO emission in the modelled fluidized bed boiler. Design variables consisted of ammonia mass flow from distinct injections. Genetic algorithm (GA) was used for the optimization. The predicted NO emission was reduced approximately 15% with respect to the current operating point by the application of GA and CFD. The use of CFD connected with systematic optimization algorithm seems to be a promising design tool for combustion process optimization. Keywords: CFD modellingFluidized bed combustionNitric oxidesOptimizationGenetic algorithm