Multi-objective Optimization of a Coal-fired Boiler Combustion Based on NSGA-II Algorithm

Back propagation(BP) neural network was proposed to establish a mathematical model for predicting the functional relationship between outputs(NOx emissions overall heat loss of the boiler) and inputting(operational parameters of the boiler) of a coal-fired boiler.A number of field test data from a full-scale operating 300 MWe boiler was used to train and verify the BP model.Then,BP model and the non-dominated sorting genetic algorithm II(NSGA-II) were combined to gain the optimal operating parameters which lead to lower NOx emissions and overall heat loss boiler.The NOx emissions overall heat loss predicted by the BP neural network model showed good agreement with the measured,the optimization results showed that hybrid algorithm by combining BP neural network and NSGA-II could be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion,which could reduce NOx emissions and overall heat loss effectively for the coal-fired boiler.