Using a genetic algorithm and CFD to identify low NOx configurations in an industrial boiler

This paper focuses on a computational intelligence approach used for minimizing NOx emissions in a 600 MW tangentially-fired pulverized-coal boiler. Genetic Algorithms (GA) were used to correlate operating parameters to significant output data predicted by CFD simulations of the boiler. The operating parameters include the opening or closing of air dampers, changing the coal distribution through mill selection and feed rate and vertical tilting of the burners. A target function was introduced to estimate for each boiler settings defined by given operating parameters, the costs associated with corrosion on the water-wall tubes, heterogeneous heat flux distribution along the walls, unburned carbon in fly ash and NOx emissions. The GA was able to automatically generate innovative boiler configurations among thousands of CFD calculations performed. The target function allowed the search space to be explored to establish configurations offering a good compromise between NOx reduction and the cost associated with corrosion in particular. Moreover, the predicted NOx emissions from the GA model are consistent with the NOx levels measured during test campaigns.