Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion

The present work introduces a way of optimizing the low NO_x combustion using the neural network and genetic algorithms for pulverized coal burned utility boiler. The NO_x emission characteristic of a 600 MW capacity boiler operated under different conditions is experimentally investigated and on the basis of experimental results, the artificial neural network is used to describe its NO_x emission property to develop a neural network based model. A genetic algorithm is employed to perform a search to determine the optimum solution of the neural network model, identifying appropriate setpoints for the current operating conditions and the low NO_x emission of the pulverized coal burned boiler is achieved.