Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms

The present work introduces an approach to predict the nitrogen oxides (NOx) emissions and carbon burnout characteristics of a large capacity pulverized coal-fired boiler with an artificial neural network (ANN). The NOx emissions and carbon burnout characteristics are investigated by parametric field experiments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt are studied. An ANN is used to model the NOx emissions characteristics and the carbon burnout characteristics. A genetic algorithm (GA) is employed to perform a multi-objective search to determine the optimum solution of the ANN model, finding the optimal setpoints, which can suggest operators' correct actions to decrease NOx emissions and the carbon content in the flyash simultaneously, namely, get a good boiler combustion performance with high boiler efficiency while keeping the NOx emission concentration meet the requirement. Copyright (c) 2005 John Wiley & Sons, Ltd.